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1. DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Author: DeepSeek-AI, Guo, Daya, Yang, Dejian, Zhang, Haowei, Song, Junxiao, Zhang, Ruoyu, Xu, Runxin, Zhu, Qihao, Ma, Shirong, Wang, Peiyi, Bi, Xiao, Zhang, Xiaokang, Yu, Xingkai, Wu, Yu, Wu, Z. F., Gou, Zhibin, Shao, Zhihong, Li, Zhuoshu, Gao, Ziyi, Liu, Aixin, Xue, Bing, Wang, Bingxuan, Wu, Bochao, Feng, Bei, Lu, Chengda, Zhao, Chenggang, Deng, Chengqi, Zhang, Chenyu, Ruan, Chong, Dai, Damai, Chen, Deli, Ji, Dongjie, Li, Erhang, Lin, Fangyun, Dai, Fucong, Luo, Fuli, Hao, Guangbo, Chen, Guanting, Li, Guowei, Zhang, H., Bao, Han, Xu, Hanwei, Wang, Haocheng, Ding, Honghui, Xin, Huajian, Gao, Huazuo, Qu, Hui, Li, Hui, Guo, Jianzhong, Li, Jiashi, Wang, Jiawei, Chen, Jingchang, Yuan, Jingyang, Qiu, Junjie, Li, Junlong, Cai, J. L., Ni, Jiaqi, Liang, Jian, Chen, Jin, Dong, Kai, Hu, Kai, Gao, Kaige, Guan, Kang, Huang, Kexin, Yu, Kuai, Wang, Lean, Zhang, Lecong, Zhao, Liang, Wang, Litong, Zhang, Liyue, Xu, Lei, Xia, Leyi, Zhang, Mingchuan, Zhang, Minghua, Tang, Minghui, Li, Meng, Wang, Miaojun, Li, Mingming, Tian, Ning, Huang, Panpan, Zhang, Peng, Wang, Qiancheng, Chen, Qinyu, Du, Qiushi, Ge, Ruiqi, Zhang, Ruisong, Pan, Ruizhe, Wang, Runji, Chen, R. J., Jin, R. L., Chen, Ruyi, Lu, Shanghao, Zhou, Shangyan, Chen, Shanhuang, Ye, Shengfeng, Wang, Shiyu, Yu, Shuiping, Zhou, Shunfeng, Pan, Shuting, Li, S. S., Zhou, Shuang, Wu, Shaoqing, Yun, Tao, Pei, Tian, Sun, Tianyu, Wang, T., Zeng, Wangding, Zhao, Wanjia, Liu, Wen, Liang, Wenfeng, Gao, Wenjun, Yu, Wenqin, Zhang, Wentao, Xiao, W. L., An, Wei, Liu, Xiaodong, Wang, Xiaohan, Chen, Xiaokang, Nie, Xiaotao, Cheng, Xin, Liu, Xin, Xie, Xin, Liu, Xingchao, Yang, Xinyu, Li, Xinyuan, Su, Xuecheng, Lin, Xuheng, Li, X. Q., Jin, Xiangyue, Shen, Xiaojin, Chen, Xiaosha, Sun, Xiaowen, Wang, Xiaoxiang, Song, Xinnan, Zhou, Xinyi, Wang, Xianzu, Shan, Xinxia, Li, Y. K., Wang, Y. Q., Wei, Y. X., Zhang, Yang, Xu, Yanhong, Li, Yao, Zhao, Yao, Sun, Yaofeng, Wang, Yaohui, Yu, Yi, Zhang, Yichao, Shi, Yifan, Xiong, Yiliang, He, Ying, Piao, Yishi, Wang, Yisong, Tan, Yixuan, Ma, Yiyang, Liu, Yiyuan, Guo, Yongqiang, Ou, Yuan, Wang, Yuduan, Gong, Yue, Zou, Yuheng, He, Yujia, Xiong, Yunfan, Luo, Yuxiang, You, Yuxiang, Liu, Yuxuan, Zhou, Yuyang, Zhu, Y. X., Huang, Yanping, Li, Yaohui, Zheng, Yi, Zhu, Yuchen, Ma, Yunxian, Tang, Ying, Zha, Yukun, Yan, Yuting, Ren, Z. Z., Ren, Zehui, Sha, Zhangli, Fu, Zhe, Xu, Zhean, Xie, Zhenda, Zhang, Zhengyan, Hao, Zhewen, Ma, Zhicheng, Yan, Zhigang, Wu, Zhiyu, Gu, Zihui, Zhu, Zijia, Liu, Zijun, Li, Zilin, Xie, Ziwei, Song, Ziyang, Pan, Zizheng, Huang, Zhen, Xu, Zhipeng, Zhang, Zhongyu, and Zhang, Zhen
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract: We introduce our first-generation reasoning models, DeepSeek-R1-Zero and DeepSeek-R1. DeepSeek-R1-Zero, a model trained via large-scale reinforcement learning (RL) without supervised fine-tuning (SFT) as a preliminary step, demonstrates remarkable reasoning capabilities. Through RL, DeepSeek-R1-Zero naturally emerges with numerous powerful and intriguing reasoning behaviors. However, it encounters challenges such as poor readability, and language mixing. To address these issues and further enhance reasoning performance, we introduce DeepSeek-R1, which incorporates multi-stage training and cold-start data before RL. DeepSeek-R1 achieves performance comparable to OpenAI-o1-1217 on reasoning tasks. To support the research community, we open-source DeepSeek-R1-Zero, DeepSeek-R1, and six dense models (1.5B, 7B, 8B, 14B, 32B, 70B) distilled from DeepSeek-R1 based on Qwen and Llama.
Published: 2025

2. DeepSeek-V3 Technical Report

Author: DeepSeek-AI, Liu, Aixin, Feng, Bei, Xue, Bing, Wang, Bingxuan, Wu, Bochao, Lu, Chengda, Zhao, Chenggang, Deng, Chengqi, Zhang, Chenyu, Ruan, Chong, Dai, Damai, Guo, Daya, Yang, Dejian, Chen, Deli, Ji, Dongjie, Li, Erhang, Lin, Fangyun, Dai, Fucong, Luo, Fuli, Hao, Guangbo, Chen, Guanting, Li, Guowei, Zhang, H., Bao, Han, Xu, Hanwei, Wang, Haocheng, Zhang, Haowei, Ding, Honghui, Xin, Huajian, Gao, Huazuo, Li, Hui, Qu, Hui, Cai, J. L., Liang, Jian, Guo, Jianzhong, Ni, Jiaqi, Li, Jiashi, Wang, Jiawei, Chen, Jin, Chen, Jingchang, Yuan, Jingyang, Qiu, Junjie, Li, Junlong, Song, Junxiao, Dong, Kai, Hu, Kai, Gao, Kaige, Guan, Kang, Huang, Kexin, Yu, Kuai, Wang, Lean, Zhang, Lecong, Xu, Lei, Xia, Leyi, Zhao, Liang, Wang, Litong, Zhang, Liyue, Li, Meng, Wang, Miaojun, Zhang, Mingchuan, Zhang, Minghua, Tang, Minghui, Li, Mingming, Tian, Ning, Huang, Panpan, Wang, Peiyi, Zhang, Peng, Wang, Qiancheng, Zhu, Qihao, Chen, Qinyu, Du, Qiushi, Chen, R. J., Jin, R. L., Ge, Ruiqi, Zhang, Ruisong, Pan, Ruizhe, Wang, Runji, Xu, Runxin, Zhang, Ruoyu, Chen, Ruyi, Li, S. S., Lu, Shanghao, Zhou, Shangyan, Chen, Shanhuang, Wu, Shaoqing, Ye, Shengfeng, Ma, Shirong, Wang, Shiyu, Zhou, Shuang, Yu, Shuiping, Zhou, Shunfeng, Pan, Shuting, Wang, T., Yun, Tao, Pei, Tian, Sun, Tianyu, Xiao, W. L., Zeng, Wangding, Zhao, Wanjia, An, Wei, Liu, Wen, Liang, Wenfeng, Gao, Wenjun, Yu, Wenqin, Zhang, Wentao, Li, X. Q., Jin, Xiangyue, Wang, Xianzu, Bi, Xiao, Liu, Xiaodong, Wang, Xiaohan, Shen, Xiaojin, Chen, Xiaokang, Zhang, Xiaokang, Chen, Xiaosha, Nie, Xiaotao, Sun, Xiaowen, Wang, Xiaoxiang, Cheng, Xin, Liu, Xin, Xie, Xin, Liu, Xingchao, Yu, Xingkai, Song, Xinnan, Shan, Xinxia, Zhou, Xinyi, Yang, Xinyu, Li, Xinyuan, Su, Xuecheng, Lin, Xuheng, Li, Y. K., Wang, Y. Q., Wei, Y. X., Zhu, Y. X., Zhang, Yang, Xu, Yanhong, Huang, Yanping, Li, Yao, Zhao, Yao, Sun, Yaofeng, Li, Yaohui, Wang, Yaohui, Yu, Yi, Zheng, Yi, Zhang, Yichao, Shi, Yifan, Xiong, Yiliang, He, Ying, Tang, Ying, Piao, Yishi, Wang, Yisong, Tan, Yixuan, Ma, Yiyang, Liu, Yiyuan, Guo, Yongqiang, Wu, Yu, Ou, Yuan, Zhu, Yuchen, Wang, Yuduan, Gong, Yue, Zou, Yuheng, He, Yujia, Zha, Yukun, Xiong, Yunfan, Ma, Yunxian, Yan, Yuting, Luo, Yuxiang, You, Yuxiang, Liu, Yuxuan, Zhou, Yuyang, Wu, Z. F., Ren, Z. Z., Ren, Zehui, Sha, Zhangli, Fu, Zhe, Xu, Zhean, Huang, Zhen, Zhang, Zhen, Xie, Zhenda, Zhang, Zhengyan, Hao, Zhewen, Gou, Zhibin, Ma, Zhicheng, Yan, Zhigang, Shao, Zhihong, Xu, Zhipeng, Wu, Zhiyu, Zhang, Zhongyu, Li, Zhuoshu, Gu, Zihui, Zhu, Zijia, Liu, Zijun, Li, Zilin, Xie, Ziwei, Song, Ziyang, Gao, Ziyi, and Pan, Zizheng
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract: We present DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token. To achieve efficient inference and cost-effective training, DeepSeek-V3 adopts Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were thoroughly validated in DeepSeek-V2. Furthermore, DeepSeek-V3 pioneers an auxiliary-loss-free strategy for load balancing and sets a multi-token prediction training objective for stronger performance. We pre-train DeepSeek-V3 on 14.8 trillion diverse and high-quality tokens, followed by Supervised Fine-Tuning and Reinforcement Learning stages to fully harness its capabilities. Comprehensive evaluations reveal that DeepSeek-V3 outperforms other open-source models and achieves performance comparable to leading closed-source models. Despite its excellent performance, DeepSeek-V3 requires only 2.788M H800 GPU hours for its full training. In addition, its training process is remarkably stable. Throughout the entire training process, we did not experience any irrecoverable loss spikes or perform any rollbacks. The model checkpoints are available at https://github.com/deepseek-ai/DeepSeek-V3.
Published: 2024

3. Detection of two TeV gamma-ray outbursts from NGC 1275 by LHAASO

Author: Cao, Zhen, Aharonian, F., Axikegu, Bai, Y. X., Bao, Y. W., Bastieri, D., Bi, X. J., Bi, Y. J., Cai, J. T., Cao, Q., Cao, W. Y., Cao, Zhe, Chang, J., Chang, J. F., Chen, A. M., Chen, E. S., Chen, Liang, Chen, Lin, Chen, Long, Chen, M. J., Chen, M. L., Chen, Q. H., Chen, S. H., Chen, S. Z., Chen, T. L., Chen, Y., Cheng, N., Cheng, Y. D., Cui, M. Y., Cui, S. W., Cui, X. H., Cui, Y. D., Dai, B. Z., Dai, H. L., Dai, Z. G., Danzengluobu, della Volpe, D., Dong, X. Q., Duan, K. K., Fan, J. H., Fan, Y. Z., Fang, J., Fang, K., Feng, C. F., Feng, L., Feng, S. H., Feng, X. T., Feng, Y. L., Gabici, S., Gao, B., Gao, C. D., Gao, L. Q., Gao, Q., Gao, W., Gao, W. K., Ge, M. M., Geng, L. S., Giacinti, G., Gong, G. H., Gou, Q. B., Gu, M. H., Guo, F. L., Guo, X. L., Guo, Y. Q., Guo, Y. Y., Han, Y. A., He, H. H., He, H. N., He, J. Y., He, X. B., He, Y., Heller, M., Hor, Y. K., Hou, B. W., Hou, C., Hou, X., Hu, H. B., Hu, Q., Hu, S. C., Huang, D. H., Huang, T. Q., Huang, W. J., Huang, X. T., Huang, X. Y., Huang, Y., Huang, Z. C., Ji, X. L., Jia, H. Y., Jia, K., Jiang, K., Jiang, X. W., Jiang, Z. J., Jin, M., Kang, M. M., Ke, T., Kuleshov, D., Kurinov, K., Li, B. B., Li, Cheng, Li, Cong, Li, D., Li, F., Li, H. B., Li, H. C., Li, H. Y., Li, J., Li, Jian, Li, Jie, Li, K., Li, W. L., Li, X. R., Li, Xin, Li, Y. Z., Li, Zhe, Li, Zhuo, Liang, E. W., Liang, Y. F., Lin, S. J., Liu, B., Liu, C., Liu, D., Liu, H., Liu, H. D., Liu, J., Liu, J. L., Liu, J. Y., Liu, M. Y., Liu, R. Y., Liu, S. M., Liu, W., Liu, Y., Liu, Y. N., Lu, R., Luo, Q., Lv, H. K., Ma, B. Q., Ma, L. L., Ma, X. H., Mao, J. R., Min, Z., Mitthumsiri, W., Mu, H. J., Nan, Y. C., Neronov, A., Ou, Z. W., Pang, B. Y., Pattarakijwanich, P., Pei, Z. Y., Qi, M. Y., Qi, Y. Q., Qiao, B. Q., Qin, J. J., Ruffolo, D., Sáiz, A., Semikoz, D., Shao, C. Y., Shao, L., Shchegolev, O., Sheng, X. D., Shu, F. W., Song, H. C., Stenkin, Yu. V., Stepanov, V., Su, Y., Sun, Q. N., Sun, X. N., Sun, Z. B., Tam, P. H. T., Tang, Q. W., Tang, Z. B., Tian, W. W., Wang, C., Wang, C. B., Wang, G. W., Wang, H. G., Wang, H. H., Wang, J. C., Wang, K., Wang, L. P., Wang, L. Y., Wang, P. H., Wang, R., Wang, W., Wang, X. G., Wang, X. Y., Wang, Y., Wang, Y. D., Wang, Y. J., Wang, Z. H., Wang, Z. X., Wang, Zhen, Wang, Zheng, Wei, D. M., Wei, J. J., Wei, Y. J., Wen, T., Wu, C. Y., Wu, H. R., Wu, S., Wu, X. F., Wu, Y. S., Xi, S. Q., Xia, J., Xia, J. J., Xiang, G. M., Xiao, D. X., Xiao, G., Xin, G. G., Xin, Y. L., Xing, Y., Xiong, Z., Xu, D. L., Xu, R. F., Xu, R. X., Xu, W. L., Xue, L., Yan, D. H., Yan, J. Z., Yan, T., Yang, C. W., Yang, F., Yang, F. F., Yang, H. W., Yang, J. Y., Yang, L. L., Yang, M. J., Yang, R. Z., Yang, S. B., Yao, Y. H., Yao, Z. G., Ye, Y. M., Yin, L. Q., Yin, N., You, X. H., You, Z. Y., Yu, Y. H., Yuan, Q., Yue, H., Zeng, H. D., Zeng, T. X., Zeng, W., Zha, M., Zhang, B. B., Zhang, F., Zhang, H. M., Zhang, H. Y., Zhang, J. L., Zhang, L. X., Zhang, Li, Zhang, P. F., Zhang, P. P., Zhang, R., Zhang, S. B., Zhang, S. R., Zhang, S. S., Zhang, X., Zhang, X. P., Zhang, Y. F., Zhang, Yi, Zhang, Yong, Zhao, B., Zhao, J., Zhao, L., Zhao, L. Z., Zhao, S. P., Zheng, F., Zhou, B., Zhou, H., Zhou, J. N., Zhou, M., Zhou, P., Zhou, R., Zhou, X. X., Zhu, C. G., Zhu, F. R., Zhu, H., Zhu, K. J., and Zuo., X.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: The Water Cherenkov Detector Array (WCDA) is one of the components of Large High Altitude Air Shower Observatory (LHAASO) and can monitor any sources over two-thirds of the sky for up to 7 hours per day with >98\% duty cycle. In this work, we report the detection of two outbursts of the Fanaroff-Riley I radio galaxy NGC 1275 that were detected by LHAASO-WCDA between November 2022 and January 2023 with statistical significance of 5.2~$\sigma$ and 8.3~$\sigma$. The observed spectral energy distribution in the range from 500 GeV to 3 TeV is fitted by a power-law with a best-fit spectral index of $\alpha=-3.37\pm0.52$ and $-3.35\pm0.29$, respectively. The outburst flux above 0.5~TeV was ($4.55\pm 4.21)\times~10^{-11}~\rm cm^{-2}~s^{-1}$ and ($3.45\pm 1.78)\times~10^{-11}~\rm cm^{-2}~s^{-1}$, corresponding to 60\%, 45\% of Crab Nebula flux. Variation analysis reveals the variability time-scale of days at the TeV energy band. A simple test by one-zone synchrotron self-Compton model reproduces the data in the gamma-ray band well., Comment: 11 pages, 8 figures, 3 tables
Published: 2024

4. LHAASO detection of very-high-energy gamma-ray emission surrounding PSR J0248+6021

Author: Cao, Zhen, Aharonian, F., An, Q., Axikegu, Bai, Y. X., Bao, Y. W., Bastieri, D., Bi, X. J., Bi, Y. J., Cai, J. T., Cao, Q., Cao, W. Y., Cao, Zhe, Chang, J., Chang, J. F., Chen, A. M., Chen, E. S., Chen, Liang, Chen, Lin, Chen, Long, Chen, M. J., Chen, M. L., Chen, Q. H., Chen, S. H., Chen, S. Z., Chen, T. L., Chen, Y., Cheng, N., Cheng, Y. D., Cui, M. Y., Cui, S. W., Cui, X. H., Cui, Y. D., Dai, B. Z., Dai, H. L., Dai, Z. G., Danzengluobu, Dong, X. Q., Duan, K. K., Fan, J. H., Fan, Y. Z., Fang, J., Fang, K., Feng, C. F., Feng, L., Feng, S. H., Feng, X. T., Feng, Y. L., Gabici, S., Gao, B., Gao, C. D., Gao, L. Q., Gao, Q., Gao, W., Gao, W. K., Ge, M. M., Geng, L. S., Giacinti, G., Gong, G. H., Gou, Q. B., Gu, M. H., Guo, F. L., Guo, X. L., Guo, Y. Q., Guo, Y. Y., Han, Y. A., He, H. H., He, H. N., He, J. Y., He, X. B., He, Y., Hor, Y. K., Hou, B. W., Hou, C., Hou, X., Hu, H. B., Hu, Q., Hu, S. C., Huang, D. H., Huang, T. Q., Huang, W. J., Huang, X. T., Huang, X. Y., Huang, Y., Huang, Z. C., Ji, X. L., Jia, H. Y., Jia, K., Jiang, K., Jiang, X. W., Jiang, Z. J., Jin, M., Kang, M. M., Ke, T., Kuleshov, D., Kurinov, K., Li, B. B., Li, Cheng, Li, Cong, Li, D., Li, F., Li, H. B., Li, H. C., Li, H. Y., Li, J., Li, Jian, Li, Jie, Li, K., Li, W. L., Li, X. R., Li, Xin, Li, Y. Z., Li, Zhe, Li, Zhuo, Liang, E. W., Liang, Y. F., Lin, J., Liu, B., Liu, C., Liu, D., Liu, H., Liu, H. D., Liu, J., Liu, J. L., Liu, J. Y., Liu, M. Y., Liu, R. Y., Liu, S. M., Liu, W., Liu, Y., Liu, Y. N., Lu, R., Luo, Q., Lv, H. K., Ma, B. Q., Ma, L. L., Ma, X. H., Mao, J. R., Min, Z., Mitthumsiri, W., Mu, H. J., Nan, Y. C., Neronov, A., Ou, Z. W., Pang, B. Y., Pattarakijwanich, P., Pei, Z. Y., Qi, M. Y., Qi, Y. Q., Qiao, B. Q., Qin, J. J., Ruffolo, D., Sáiz, A., Semikoz, D., Shao, C. Y., Shao, L., Shchegolev, O., Sheng, X. D., Shu, F. W., Song, H. C., Stenkin, Yu. V., Stepanov, V., Su, Y., Sun, Q. N., Sun, X. N., Sun, Z. B., Tam, P. H. T., Tang, Q. W., Tang, Z. B., Tian, W. W., Wang, C., Wang, C. B., Wang, G. W., Wang, H. G., Wang, H. H., Wang, J. C., Wang, K., Wang, L. P., Wang, L. Y., Wang, P. H., Wang, R., Wang, W., Wang, X. G., Wang, X. Y., Wang, Y., Wang, Y. D., Wang, Y. J., Wang, Z. H., Wang, Z. X., Wang, Zhen, Wang, Zheng, Wei, D. M., Wei, J. J., Wei, Y. J., Wen, T., Wu, C. Y., Wu, H. R., Wu, S., Wu, X. F., Wu, Y. S., Xi, S. Q., Xia, J., Xia, J. J., Xiang, G. M., Xiao, D. X., Xiao, G., Xin, G. G., Xin, Y. L., Xing, Y., Xiong, Z., Xu, D. L., Xu, R. F., Xu, R. X., Xu, W. L., Xue, L., Yan, D. H., Yan, J. Z., Yan, T., Yang, C. W., Yang, F., Yang, F. F., Yang, H. W., Yang, J. Y., Yang, L. L., Yang, M. J., Yang, R. Z., Yang, S. B., Yao, Y. H., Yao, Z. G., Ye, Y. M., Yin, L. Q., Yin, N., You, X. H., You, Z. Y., Yu, Y. H., Yuan, Q., Yue, H., Zeng, H. D., Zeng, T. X., Zeng, W., Zha, M., Zhang, B. B., Zhang, F., Zhang, H. M., Zhang, H. Y., Zhang, J. L., Zhang, L. X., Zhang, Li, Zhang, P. F., Zhang, P. P., Zhang, R., Zhang, S. B., Zhang, S. R., Zhang, S. S., Zhang, X., Zhang, X. P., Zhang, Y. F., Zhang, Yi, Zhang, Yong, Zhao, B., Zhao, J., Zhao, L., Zhao, L. Z., Zhao, S. P., Zheng, F., Zheng, J. H., Zhou, B., Zhou, H., Zhou, J. N., Zhou, M., Zhou, P., Zhou, R., Zhou, X. X., Zhu, C. G., Zhu, F. R., Zhu, H., Zhu, K. J., Zou, Y. C., and Zuo, X.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: We report the detection of an extended very-high-energy (VHE) gamma-ray source coincident with the location of middle-aged (62.4~\rm kyr) pulsar PSR J0248+6021, by using the LHAASO-WCDA data of live 796 days and LHAASO-KM2A data of live 1216 days. A significant excess of \gray induced showers is observed both by WCDA in energy bands of 1-25~\rm TeV and KM2A in energy bands of $>$ 25~\rm TeV with 7.3 $\sigma$ and 13.5 $\sigma$, respectively. The best-fit position derived through WCDA data is R.A. = 42.06$^\circ \pm$ 0.12$^\circ$ and Dec. = 60.24$^\circ \pm $ 0.13$^\circ$ with an extension of 0.69$^\circ\pm$0.15$^\circ$ and that of the KM2A data is R.A.= 42.29$^\circ \pm $ 0.13$^\circ$ and Dec. = 60.38$^\circ \pm$ 0.07$^\circ$ with an extension of 0.37$^\circ\pm$0.07$^\circ$. No clear extended multiwavelength counterpart of this LHAASO source has been found from the radio band to the GeV band. The most plausible explanation of the VHE \gray emission is the inverse Compton process of highly relativistic electrons and positrons injected by the pulsar. These electrons/positrons are hypothesized to be either confined within the pulsar wind nebula or to have already escaped into the interstellar medium, forming a pulsar halo., Comment: 12 pages, 10 figures, Accepted by Sci. China-Phys. Mech. Astron
Published: 2024

5. Low-cost Monitoring of Energetic Particle Precipitation: Weather Balloon-borne Timepix Measurements During the May 2024 Superstorm

Author: Olifer, L., Manavalan, P., Headrick, D., Palmers, S., Harbarenko, B., Cai, J., Fourie, J., Bauer, O., and Mann, I.
Subjects: Physics - Space Physics
Abstract: Understanding energetic electron precipitation is crucial for accurate space weather modeling and forecasting, impacting the Earth's upper atmosphere and human infrastructure. This study presents a low-cost, low-mass, and low-power solution for high-fidelity analysis of electron precipitation events by measuring the resulting bremsstrahlung X-ray emissions. Specifically, we report on results from the flight of a radiation detector payload based on a silicon pixel read-out Timepix detector technology, and its successful utilization onboard a `burster' weather balloon. We launched this payload during the May 2024 superstorm, capturing high-resolution measurements of both background galactic cosmic ray radiation as well as storm-time energetic electron precipitation. We further developed particle and radiation detection algorithms to separate bremsstrahlung X-rays from other particle species in the pixel-resolved trajectories as seen in the Timepix detector. The measurements revealed a distinctive four-peak structure in X-ray flux, corresponding to periodic four-minute-long bursts of energetic electron precipitation between 21:20 and 21:40 UT. This precipitation was also observed by a riometer station close to the balloon launch path, further validating balloon measurements and the developed X-ray identification algorithm. The clear periodic structure of the measured precipitation is likely caused by modulation of the electron losses from the radiation belt by harmonic Pc5 ULF waves, observed contemporaneously on the ground. The study underscores the potential of compact, low-cost payloads for advancing our understanding of space weather. Specifically, we envision a potential use of such Timepix-based detectors in space science, for example on sounding rockets or nano-, micro-, and small satellite platforms.
Published: 2024

6. Constraints on Ultra Heavy Dark Matter Properties from Dwarf Spheroidal Galaxies with LHAASO Observations

Author: Cao, Zhen, Aharonian, F., An, Q., Axikegu, Bai, Y. X., Bao, Y. W., Bastieri, D., Bi, X. J., Bi, Y. J., Cai, J. T., Cao, Q., Cao, W. Y., Cao, Zhe, Chang, J., Chang, J. F., Chen, A. M., Chen, E. S., Chen, Liang, Chen, Lin, Chen, Long, Chen, M. J., Chen, M. L., Chen, Q. H., Chen, S. H., Chen, S. Z., Chen, T. L., Chen, Y., Cheng, N., Cheng, Y. D., Cui, M. Y., Cui, S. W., Cui, X. H., Cui, Y. D., Dai, B. Z., Dai, H. L., Dai, Z. G., Danzengluobu, della Volpe, D., Dong, X. Q., Duan, K. K., Fan, J. H., Fan, Y. Z., Fang, J., Fang, K., Feng, C. F., Feng, L., Feng, S. H., Feng, X. T., Feng, Y. L., Gabici, S., Gao, B., Gao, C. D., Gao, L. Q., Gao, Q., Gao, W., Gao, W. K., Ge, M. M., Geng, L. S., Giacinti, G., Gong, G. H., Gou, Q. B., Gu, M. H., Guo, F. L., Guo, X. L., Guo, Y. Q., Guo, Y. Y., Han, Y. A., He, H. H., He, H. N., He, J. Y., He, X. B., He, Y., Heller, M., Hor, Y. K., Hou, B. W., Hou, C., Hou, X., Hu, H. B., Hu, Q., Hu, S. C., Huang, D. H., Huang, T. Q., Huang, W. J., Huang, X. T., Huang, X. Y., Huang, Y., Huang, Z. C., Ji, X. L., Jia, H. Y., Jia, K., Jiang, K., Jiang, X. W., Jiang, Z. J., Jin, M., Kang, M. M., Ke, T., Kuleshov, D., Kurinov, K., Li, B. B., Li, Cheng, Li, Cong, Li, D., Li, F., Li, H. B., Li, H. C., Li, H. Y., Li, J., Li, Jian, Li, Jie, Li, K., Li, W. L., Li, X. R., Li, Xin, Li, Y. Z., Li, Zhe, Li, Zhuo, Liang, E. W., Liang, Y. F., Lin, S. J., Liu, B., Liu, C., Liu, D., Liu, H., Liu, H. D., Liu, J., Liu, J. L., Liu, J. Y., Liu, M. Y., Liu, R. Y., Liu, S. M., Liu, W., Liu, Y., Liu, Y. N., Lu, R., Luo, Q., Lv, H. K., Ma, B. Q., Ma, L. L., Ma, X. H., Mao, J. R., Min, Z., Mitthumsiri, W., Mu, H. J., Nan, Y. C., Neronov, A., Ou, Z. W., Pang, B. Y., Pattarakijwanich, P., Pei, Z. Y., Qi, M. Y., Qi, Y. Q., Qiao, B. Q., Qin, J. J., Ruffolo, D., Saiz, A., Semikoz, D., Shao, C. Y., Shao, L., Shchegolev, O., Sheng, X. D., Shu, F. W., Song, H. C., Stenkin, Yu. V., Stepanov, V., Su, Y., Sun, Q. N., Sun, X. N., Sun, Z. B., Tam, P. H. T., Tang, Q. W., Tang, Z. B., Tian, W. W., Wang, C., Wang, C. B., Wang, G. W., Wang, H. G., Wang, H. H., Wang, J. C., Wang, K., Wang, L. P., Wang, L. Y., Wang, P. H., Wang, R., Wang, W., Wang, X. G., Wang, X. Y., Wang, Y., Wang, Y. D., Wang, Y. J., Wang, Z. H., Wang, Z. X., Wang, Zhen, Wang, Zheng, Wei, D. M., Wei, J. J., Wei, Y. J., Wen, T., Wu, C. Y., Wu, H. R., Wu, S., Wu, X. F., Wu, Y. S., Xi, S. Q., Xia, J., Xia, J. J., Xiang, G. M., Xiao, D. X., Xiao, G., Xin, G. G., Xin, Y. L., Xing, Y., Xiong, Z., Xu, D. L., Xu, R. F., Xu, R. X., Xu, W. L., Xue, L., Yan, D. H., Yan, J. Z., Yan, T., Yang, C. W., Yang, F., Yang, F. F., Yang, H. W., Yang, J. Y., Yang, L. L., Yang, M. J., Yang, R. Z., Yang, S. B., Yao, Y. H., Yao, Z. G., Ye, Y. M., Yin, L. Q., Yin, N., You, X. H., You, Z. Y., Yu, Y. H., Yuan, Q., Yue, H., Zeng, H. D., Zeng, T. X., Zeng, W., Zha, M., Zhang, B. B., Zhang, F., Zhang, H. M., Zhang, H. Y., Zhang, J. L., Zhang, L. X., Zhang, Li, Zhang, P. F., Zhang, P. P., Zhang, R., Zhang, S. B., Zhang, S. R., Zhang, S. S., Zhang, X., Zhang, X. P., Zhang, Y. F., Zhang, Yi, Zhang, Yong, Zhao, B., Zhao, J., Zhao, L., Zhao, L. Z., Zhao, S. P., Zheng, F., Zhou, B., Zhou, H., Zhou, J. N., Zhou, M., Zhou, P., Zhou, R., Zhou, X. X., Zhu, C. G., Zhu, F. R., Zhu, H., Zhu, K. J., and Zuo, X.
Subjects: Astrophysics - High Energy Astrophysical Phenomena, High Energy Physics - Phenomenology
Abstract: In this work we try to search for signals generated by ultra-heavy dark matter at the Large High Altitude Air Shower Observatory (LHAASO) data. We look for possible gamma-ray by dark matter annihilation or decay from 16 dwarf spheroidal galaxies in the field of view of LHAASO. Dwarf spheroidal galaxies are among the most promising targets for indirect detection of dark matter which have low fluxes of astrophysical $\gamma$-ray background while large amount of dark matter. By analyzing more than 700 days observational data at LHAASO, no significant dark matter signal from 1 TeV to 1 EeV is detected. Accordingly we derive the most stringent constraints on the ultra-heavy dark matter annihilation cross-section up to EeV. The constraints on the lifetime of dark matter in decay mode are also derived., Comment: 17 pages, 12 figures, accepted by PRL
Published: 2024

7. Discovery of Very-high-energy Gamma-ray Emissions from the Low Luminosity AGN NGC 4278 by LHAASO

Author: Cao, Zhen, Aharonian, F., An, Q., Axikegu, Bai, Y. X., Bao, Y. W., Bastieri, D., Bi, X. J., Bi, Y. J., Cai, J. T., Cao, Q., Cao, W. Y., Cao, Zhe, Chang, J., Chang, J. F., Chen, A. M., Chen, E. S., Chen, Liang, Chen, Lin, Chen, Long, Chen, M. J., Chen, M. L., Chen, Q. H., Chen, S. H., Chen, S. Z., Chen, T. L., Chen, Y., Cheng, N., Cheng, Y. D., Cui, M. Y., Cui, S. W., Cui, X. H., Cui, Y. D., Dai, B. Z., Dai, H. L., Dai, Z. G., Danzengluobu, Dong, X. Q., Duan, K. K., Fan, J. H., Fan, Y. Z., Fang, J., Fang, K., Feng, C. F., Feng, L., Feng, S. H., Feng, X. T., Feng, Y. L., Gabici, S., Gao, B., Gao, C. D., Gao, L. Q., Gao, Q., Gao, W., Gao, W. K., Ge, M. M., Geng, L. S., Giacinti, G., Gong, G. H., Gou, Q. B., Gu, M. H., Guo, F. L., Guo, X. L., Guo, Y. Q., Guo, Y. Y., Han, Y. A., He, H. H., He, H. N., He, J. Y., He, X. B., He, Y., Hor, Y. K., Hou, B. W., Hou, C., Hou, X., Hu, H. B., Hu, Q., Hu, S. C., Huang, D. H., Huang, T. Q., Huang, W. J., Huang, X. T., Huang, X. Y., Huang, Y., Huang, Z. C., Ji, X. L., Jia, H. Y., Jia, K., Jiang, K., Jiang, X. W., Jiang, Z. J., Jin, M., Kang, M. M., Ke, T., Kuleshov, D., Kurinov, K., Li, B. B., Li, Cheng, Li, Cong, Li, D., Li, F., Li, H. B., Li, H. C., Li, H. Y., Li, J., Li, Jian, Li, Jie, Li, K., Li, W. L., Li, X. R., Li, Xin, Li, Y. Z., Li, Zhe, Li, Zhuo, Liang, E. W., Liang, Y. F., Lin, J., Liu, B., Liu, C., Liu, D., Liu, H., Liu, H. D., Liu, J., Liu, J. L., Liu, J. Y., Liu, M. Y., Liu, R. Y., Liu, S. M., Liu, W., Liu, Y., Liu, Y. N., Lu, R., Luo, Q., Lv, H. K., Ma, B. Q., Ma, L. L., Ma, X. H., Mao, J. R., Min, Z., Mitthumsiri, W., Mu, H. J., Nan, Y. C., Neronov, A., Ou, Z. W., Pang, B. Y., Pattarakijwanich, P., Pei, Z. Y., Qi, M. Y., Qi, Y. Q., Qiao, B. Q., Qin, J. J., Ruffolo, D., Sáiz, A., Semikoz, D., Shao, C. Y., Shao, L., Shchegolev, O., Sheng, X. D., Shu, F. W., Song, H. C., Stenkin, Yu. V., Stepanov, V., Su, Y., Sun, Q. N., Sun, X. N., Sun, Z. B., Tam, P. H. T., Tang, Q. W., Tang, Z. B., Tian, W. W., Wang, C., Wang, C. B., Wang, G. W., Wang, H. G., Wang, H. H., Wang, J. C., Wang, K., Wang, L. P., Wang, L. Y., Wang, P. H., Wang, R., Wang, W., Wang, X. G., Wang, X. Y., Wang, Y., Wang, Y. D., Wang, Y. J., Wang, Z. H., Wang, Z. X., Wang, Zhen, Wang, Zheng, Wei, D. M., Wei, J. J., Wei, Y. J., Wen, T., Wu, C. Y., Wu, H. R., Wu, S., Wu, X. F., Wu, Y. S., Xi, S. Q., Xia, J., Xia, J. J., Xiang, G. M., Xiao, D. X., Xiao, G., Xin, G. G., Xin, Y. L., Xing, Y., Xiong, Z., Xu, D. L., Xu, R. F., Xu, R. X., Xu, W. L., Xue, L., Yan, D. H., Yan, J. Z., Yan, T., Yang, C. W., Yang, F., Yang, F. F., Yang, H. W., Yang, J. Y., Yang, L. L., Yang, M. J., Yang, R. Z., Yang, S. B., Yao, Y. H., Yao, Z. G., Ye, Y. M., Yin, L. Q., Yin, N., You, X. H., You, Z. Y., Yu, Y. H., Yuan, Q., Yue, H., Zeng, H. D., Zeng, T. X., Zeng, W., Zha, M., Zhang, B. B., Zhang, F., Zhang, H. M., Zhang, H. Y., Zhang, J. L., Zhang, L. X., Zhang, Li, Zhang, P. F., Zhang, P. P., Zhang, R., Zhang, S. B., Zhang, S. R., Zhang, S. S., Zhang, X., Zhang, X. P., Zhang, Y. F., Zhang, Yi, Zhang, Yong, Zhao, B., Zhao, J., Zhao, L., Zhao, L. Z., Zhao, S. P., Zheng, F., Zheng, J. H., Zhou, B., Zhou, H., Zhou, J. N., Zhou, M., Zhou, P., Zhou, R., Zhou, X. X., Zhu, C. G., Zhu, F. R., Zhu, H., Zhu, K. J., Zou, Y. C., and Zuo, X.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: The first source catalog of Large High Altitude Air Shower Observatory reported the detection of a very-high-energy gamma ray source, 1LHAASO J1219+2915. In this paper a further detailed study of the spectral and temporal behavior of this point-like source have been carried. The best-fit position of the TeV source ($\rm{RA}=185.05^{\circ}\pm0.04^{\circ}$, $\rm{Dec}=29.25^{\circ}\pm0.03^{\circ}$) is compatible with NGC 4278 within $\sim0.03$ degree. Variation analysis shows an indication of the variability at a few months level in the TeV band, which is consistent with low frequency observations. Based on these observations, we report the detection of TeV $\gamma$-ray emissions from this low-luminosity AGN NGC 4278. The observations by LHAASO-WCDA during active period has a significance level of 8.8\,$\sigma$ with best-fit photon spectral index $\varGamma=2.56\pm0.14$ and a flux $f_{1-10\,\rm{TeV}}=(7.0\pm1.1_{\rm{sta}}\pm0.35_{\rm{syst}})\times10^{-13}\,\rm{photons\,cm^{-2}\,s^{-1}}$, or approximately $5\%$ of the Crab Nebula. The discovery of VHE from NGC 4278 indicates that the compact, weak radio jet can efficiently accelerate particles and emit TeV photons., Comment: 11 pages, 5 figures
Published: 2024
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8. DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model

Author: DeepSeek-AI, Liu, Aixin, Feng, Bei, Wang, Bin, Wang, Bingxuan, Liu, Bo, Zhao, Chenggang, Dengr, Chengqi, Ruan, Chong, Dai, Damai, Guo, Daya, Yang, Dejian, Chen, Deli, Ji, Dongjie, Li, Erhang, Lin, Fangyun, Luo, Fuli, Hao, Guangbo, Chen, Guanting, Li, Guowei, Zhang, H., Xu, Hanwei, Yang, Hao, Zhang, Haowei, Ding, Honghui, Xin, Huajian, Gao, Huazuo, Li, Hui, Qu, Hui, Cai, J. L., Liang, Jian, Guo, Jianzhong, Ni, Jiaqi, Li, Jiashi, Chen, Jin, Yuan, Jingyang, Qiu, Junjie, Song, Junxiao, Dong, Kai, Gao, Kaige, Guan, Kang, Wang, Lean, Zhang, Lecong, Xu, Lei, Xia, Leyi, Zhao, Liang, Zhang, Liyue, Li, Meng, Wang, Miaojun, Zhang, Mingchuan, Zhang, Minghua, Tang, Minghui, Li, Mingming, Tian, Ning, Huang, Panpan, Wang, Peiyi, Zhang, Peng, Zhu, Qihao, Chen, Qinyu, Du, Qiushi, Chen, R. J., Jin, R. L., Ge, Ruiqi, Pan, Ruizhe, Xu, Runxin, Chen, Ruyi, Li, S. S., Lu, Shanghao, Zhou, Shangyan, Chen, Shanhuang, Wu, Shaoqing, Ye, Shengfeng, Ma, Shirong, Wang, Shiyu, Zhou, Shuang, Yu, Shuiping, Zhou, Shunfeng, Zheng, Size, Wang, T., Pei, Tian, Yuan, Tian, Sun, Tianyu, Xiao, W. L., Zeng, Wangding, An, Wei, Liu, Wen, Liang, Wenfeng, Gao, Wenjun, Zhang, Wentao, Li, X. Q., Jin, Xiangyue, Wang, Xianzu, Bi, Xiao, Liu, Xiaodong, Wang, Xiaohan, Shen, Xiaojin, Chen, Xiaokang, Chen, Xiaosha, Nie, Xiaotao, Sun, Xiaowen, Wang, Xiaoxiang, Liu, Xin, Xie, Xin, Yu, Xingkai, Song, Xinnan, Zhou, Xinyi, Yang, Xinyu, Lu, Xuan, Su, Xuecheng, Wu, Y., Li, Y. K., Wei, Y. X., Zhu, Y. X., Xu, Yanhong, Huang, Yanping, Li, Yao, Zhao, Yao, Sun, Yaofeng, Li, Yaohui, Wang, Yaohui, Zheng, Yi, Zhang, Yichao, Xiong, Yiliang, Zhao, Yilong, He, Ying, Tang, Ying, Piao, Yishi, Dong, Yixin, Tan, Yixuan, Liu, Yiyuan, Wang, Yongji, Guo, Yongqiang, Zhu, Yuchen, Wang, Yuduan, Zou, Yuheng, Zha, Yukun, Ma, Yunxian, Yan, Yuting, You, Yuxiang, Liu, Yuxuan, Ren, Z. Z., Ren, Zehui, Sha, Zhangli, Fu, Zhe, Huang, Zhen, Zhang, Zhen, Xie, Zhenda, Hao, Zhewen, Shao, Zhihong, Wen, Zhiniu, Xu, Zhipeng, Zhang, Zhongyu, Li, Zhuoshu, Wang, Zihan, Gu, Zihui, Li, Zilin, and Xie, Ziwei
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract: We present DeepSeek-V2, a strong Mixture-of-Experts (MoE) language model characterized by economical training and efficient inference. It comprises 236B total parameters, of which 21B are activated for each token, and supports a context length of 128K tokens. DeepSeek-V2 adopts innovative architectures including Multi-head Latent Attention (MLA) and DeepSeekMoE. MLA guarantees efficient inference through significantly compressing the Key-Value (KV) cache into a latent vector, while DeepSeekMoE enables training strong models at an economical cost through sparse computation. Compared with DeepSeek 67B, DeepSeek-V2 achieves significantly stronger performance, and meanwhile saves 42.5% of training costs, reduces the KV cache by 93.3%, and boosts the maximum generation throughput to 5.76 times. We pretrain DeepSeek-V2 on a high-quality and multi-source corpus consisting of 8.1T tokens, and further perform Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) to fully unlock its potential. Evaluation results show that, even with only 21B activated parameters, DeepSeek-V2 and its chat versions still achieve top-tier performance among open-source models.
Published: 2024

9. Research on the Evaluation Index System of Enterprise Production Efficiency

Author: Li, W., Cai, J., Wang, C., Chen, Y., Xu, J., and Zhao, J.
Subjects: Mathematics - Optimization and Control
Abstract: This paper focuses on studying the evaluation index system for the production efficiency of tobacco enterprises. Considering the limitations of existing evaluation methods in accurately assessing the production quality of cigarette enterprises, a mathematical model based on the Analytic Hierarchy Process (AHP) is established. This model constructs an evaluation framework for the production efficiency of cigarette enterprises and subsequently analyzes the significance of each index within this framework. To comprehensively analyze the multi-index and feasibility aspects of the selected projects, the AHP method is employed to establish a comprehensive feasibility research and evaluation structure model. The result of this feasibility study provides the conclusion that the construction of an evaluation index system for the production efficiency of cigarette enterprises can indeed promote the enhancement of their production efficiency.
Published: 2024

10. LHAASO-KM2A detector simulation using Geant4

Author: Cao, Zhen, Aharonian, F., An, Q., Axikegu, Bai, Y. X., Bao, Y. W., Bastieri, D., Bi, X. J., Bi, Y. J., Cai, J. T., Cao, Q., Cao, W. Y., Cao, Zhe, Chang, J., Chang, J. F., Chen, A. M., Chen, E. S., Chen, Liang, Chen, Lin, Chen, Long, Chen, M. J., Chen, M. L., Chen, Q. H., Chen, S. H., Chen, S. Z., Chen, T. L., Chen, Y., Cheng, N., Cheng, Y. D., Cui, M. Y., Cui, S. W., Cui, X. H., Cui, Y. D., Dai, B. Z., Dai, H. L., Dai, Z. G., Danzengluobu, Dong, X. Q., Duan, K. K., Fan, J. H., Fan, Y. Z., Fang, J., Fang, K., Feng, C. F., Feng, L., Feng, S. H., Feng, X. T., Feng, Y. L., Gabici, S., Gao, B., Gao, C. D., Gao, L. Q., Gao, Q., Gao, W., Gao, W. K., Ge, M. M., Geng, L. S., Giacinti, G., Gong, G. H., Gou, Q. B., Gu, M. H., Guo, F. L., Guo, X. L., Guo, Y. Q., Guo, Y. Y., Han, Y. A., He, H. H., He, H. N., He, J. Y., He, X. B., He, Y., Hor, Y. K., Hou, B. W., Hou, C., Hou, X., Hu, H. B., Hu, Q., Hu, S. C., Huang, D. H., Huang, T. Q., Huang, W. J., Huang, X. T., Huang, X. Y., Huang, Y., Huang, Z. C., Ji, X. L., Jia, H. Y., Jia, K., Jiang, K., Jiang, X. W., Jiang, Z. J., Jin, M., Kang, M. M., Ke, T., Kuleshov, D., Kurinov, K., Li, B. B., Li, Cheng, Li, Cong, Li, D., Li, F., Li, H. B., Li, H. C., Li, H. Y., Li, J., Li, Jian, Li, Jie, Li, K., Li, W. L., Li, X. R., Li, Xin, Li, Y. Z., Li, Zhe, Li, Zhuo, Liang, E. W., Liang, Y. F., Lin, J., Liu, B., Liu, C., Liu, D., Liu, H., Liu, H. D., Liu, J., Liu, J. L., Liu, J. Y., Liu, M. Y., Liu, R. Y., Liu, S. M., Liu, W., Liu, Y., Liu, Y. N., Lu, R., Luo, Q., Lv, H. K., Ma, B. Q., Ma, L. L., Ma, X. H., Mao, J. R., Min, Z., Mitthumsiri, W., Mu, H. J., Nan, Y. C., Neronov, A., Ou, Z. W., Pang, B. Y., Pattarakijwanich, P., Pei, Z. Y., Qi, M. Y., Qi, Y. Q., Qiao, B. Q., Qin, J. J., Ruffolo, D., Sáiz, A., Semikoz, D., Shao, C. Y., Shao, L., Shchegolev, O., Sheng, X. D., Shu, F. W., Song, H. C., Stenkin, Yu. V., Stepanov, V., Su, Y., Sun, Q. N., Sun, X. N., Sun, Z. B., Tam, P. H. T., Tang, Q. W., Tang, Z. B., Tian, W. W., Wang, C., Wang, C. B., Wang, G. W., Wang, H. G., Wang, H. H., Wang, J. C., Wang, K., Wang, L. P., Wang, L. Y., Wang, P. H., Wang, R., Wang, W., Wang, X. G., Wang, X. Y., Wang, Y., Wang, Y. D., Wang, Y. J., Wang, Z. H., Wang, Z. X., Wang, Zhen, Wang, Zheng, Wei, D. M., Wei, J. J., Wei, Y. J., Wen, T., Wu, C. Y., Wu, H. R., Wu, S., Wu, X. F., Wu, Y. S., Xi, S. Q., Xia, J., Xia, J. J., Xiang, G. M., Xiao, D. X., Xiao, G., Xin, G. G., Xin, Y. L., Xing, Y., Xiong, Z., Xu, D. L., Xu, R. F., Xu, R. X., Xu, W. L., Xue, L., Yan, D. H., Yan, J. Z., Yan, T., Yang, C. W., Yang, F., Yang, F. F., Yang, H. W., Yang, J. Y., Yang, L. L., Yang, M. J., Yang, R. Z., Yang, S. B., Yao, Y. H., Yao, Z. G., Ye, Y. M., Yin, L. Q., Yin, N., You, X. H., You, Z. Y., Yu, Y. H., Yuan, Q., Yue, H., Zeng, H. D., Zeng, T. X., Zeng, W., Zha, M., Zhang, B. B., Zhang, F., Zhang, H. M., Zhang, H. Y., Zhang, J. L., Zhang, L. X., Zhang, Li, Zhang, P. F., Zhang, P. P., Zhang, R., Zhang, S. B., Zhang, S. R., Zhang, S. S., Zhang, X., Zhang, X. P., Zhang, Y. F., Zhang, Yi, Zhang, Yong, Zhao, B., Zhao, J., Zhao, L., Zhao, L. Z., Zhao, S. P., Zheng, F., Zheng, J. H., Zhou, B., Zhou, H., Zhou, J. N., Zhou, M., Zhou, P., Zhou, R., Zhou, X. X., Zhu, C. G., Zhu, F. R., Zhu, H., Zhu, K. J., and Zuo, X.
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - High Energy Astrophysical Phenomena
Abstract: KM2A is one of the main sub-arrays of LHAASO, working on gamma ray astronomy and cosmic ray physics at energies above 10 TeV. Detector simulation is the important foundation for estimating detector performance and data analysis. It is a big challenge to simulate the KM2A detector in the framework of Geant4 due to the need to track numerous photons from a large number of detector units (>6000) with large altitude difference (30 m) and huge coverage (1.3 km^2). In this paper, the design of the KM2A simulation code G4KM2A based on Geant4 is introduced. The process of G4KM2A is optimized mainly in memory consumption to avoid memory overffow. Some simpliffcations are used to signiffcantly speed up the execution of G4KM2A. The running time is reduced by at least 30 times compared to full detector simulation. The particle distributions and the core/angle resolution comparison between simulation and experimental data of the full KM2A array are also presented, which show good agreement.
Published: 2024
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11. Measurements of All-Particle Energy Spectrum and Mean Logarithmic Mass of Cosmic Rays from 0.3 to 30 PeV with LHAASO-KM2A

Author: The LHAASO Collaboration, Cao, Zhen, Aharonian, F., An, Q., Axikegu, A., Bai, Y. X., Bao, Y. W., Bastieri, D., Bi, X. J., Bi, Y. J., Cai, J. T., Cao, Q., Cao, W. Y., Cao, Zhe, Chang, J., Chang, J. F., Chen, A. M., Chen, E. S., Chen, Liang, Chen, Lin, Chen, Long, Chen, M. J., Chen, M. L., Chen, Q. H., Chen, S. H., Chen, S. Z., Chen, T. L., Chen, Y., Cheng, N., Cheng, Y. D., Cui, M. Y., Cui, S. W., Cui, X. H., Cui, Y. D., Dai, B. Z., Dai, H. L., Dai, Z. G., Danzengluobu, della Volpe, D., Dong, X. Q., Duan, K. K., Fan, J. H., Fan, Y. Z., Fang, J., Fang, K., Feng, C. F., Feng, L., Feng, S. H., Feng, X. T., Feng, Y. L., Gabici, S., Gao, B., Gao, C. D., Gao, L. Q., Gao, Q., Gao, W., Gao, W. K., Ge, M. M., Geng, L. S., Giacinti, G., Gong, G. H., Gou, Q. B., Gu, M. H., Guo, F. L., Guo, X. L., Guo, Y. Q., Guo, Y. Y., Han, Y. A., He, H. H., He, H. N., He, J. Y., He, X. B., He, Y., Heller, M., Hor, Y. K., Hou, B. W., Hou, C., Hou, X., Hu, H. B., Hu, Q., Hu, S. C., Huang, D. H., Huang, T. Q., Huang, W. J., Huang, X. T., Huang, X. Y., Huang, Y., Huang, Z. C., Ji, X. L., Jia, H. Y., Jia, K., Jiang, K., Jiang, X. W., Jiang, Z. J., Jin, M., Kang, M. M., Ke, T., Kuleshov, D., Kurinov, K., Li, B. B., Li, Cheng, Li, Cong, Li, D., Li, F., Li, H. B., Li, H. C., Li, H. Y., Li, J., Li, Jian, Li, Jie, Li, K., Li, W. L., Li, X. R., Li, Xin, Li, Y. Z., Li, Zhe, Li, Zhuo, Liang, E. W., Liang, Y. F., Lin, S. J., Liu, B., Liu, C., Liu, D., Liu, H., Liu, H. D., Liu, J., Liu, J. L., Liu, J. Y., Liu, M. Y., Liu, R. Y., Liu, S. M., Liu, W., Liu, Y., Liu, Y. N., Lu, R., Luo, Q., Lv, H. K., Ma, B. Q., Ma, L. L., Ma, X. H., Mao, J. R., Min, Z., Mitthumsiri, W., Mu, H. J., Nan, Y. C., Neronov, A., Ou, Z. W., Pang, B. Y., Pattarakijwanich, P., Pei, Z. Y., Qi, M. Y., Qi, Y. Q., Qiao, B. Q., Qin, J. J., Ruffolo, D., Sáiz, A., Semikoz, D., Shao, C. Y., Shao, L., Shchegolev, O., Sheng, X. D., Shu, F. W., Song, H. C., Stenkin, Yu. V., Stepanov, V., Su, Y., Sun, Q. N., Sun, X. N., Sun, Z. B., Tam, P. H. T., Tang, Q. W., Tang, Z. B., Tian, W. W., Wang, C., Wang, C. B., Wang, G. W., Wang, H. G., Wang, H. H., Wang, J. C., Wang, K., Wang, L. P., Wang, L. Y., Wang, P. H., Wang, R., Wang, W., Wang, X. G., Wang, X. Y., Wang, Y., Wang, Y. D., Wang, Y. J., Wang, Z. H., Wang, Z. X., Wang, Zhen, Wang, Zheng, Wei, D. M., Wei, J. J., Wei, Y. J., Wen, T., Wu, C. Y., Wu, H. R., Wu, S., Wu, X. F., Wu, Y. S., Xi, S. Q., Xia, J., Xia, J. J., Xiang, G. M., Xiao, D. X., Xiao, G., Xin, G. G., Xin, Y. L., Xing, Y., Xiong, Z., Xu, D. L., Xu, R. F., Xu, R. X., Xu, W. L., Xue, L., Yan, D. H., Yan, J. Z., Yan, T., Yang, C. W., Yang, F., Yang, F. F., Yang, H. W., Yang, J. Y., Yang, L. L., Yang, M. J., Yang, R. Z., Yang, S. B., Yao, Y. H., Yao, Z. G., Ye, Y. M., Yin, L. Q., Yin, N., You, X. H., You, Z. Y., Yu, Y. H., Yuan, Q., Yue, H., Zeng, H. D., Zeng, T. X., Zeng, W., Zha, M., Zhang, B. B., Zhang, F., Zhang, H. M., Zhang, H. Y., Zhang, J. L., Zhang, L. X., Zhang, Li, Zhang, P. F., Zhang, P. P., Zhang, R., Zhang, S. B., Zhang, S. R., Zhang, S. S., Zhang, X., Zhang, X. P., Zhang, Y. F., Zhang, Yi, Zhang, Yong, Zhao, B., Zhao, J., Zhao, L., Zhao, L. Z., Zhao, S. P., Zheng, F., Zhou, B., Zhou, H., Zhou, J. N., Zhou, M., Zhou, P., Zhou, R., Zhou, X. X., Zhu, C. G., Zhu, F. R., Zhu, H., Zhu, K. J., and Zuo, X.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: We present the measurements of all-particle energy spectrum and mean logarithmic mass of cosmic rays in the energy range of 0.3-30 PeV using data collected from LHAASO-KM2A between September 2021 and December 2022, which is based on a nearly composition-independent energy reconstruction method, achieving unprecedented accuracy. Our analysis reveals the position of the knee at $3.67 \pm 0.05 \pm 0.15$ PeV. Below the knee, the spectral index is found to be -$2.7413 \pm 0.0004 \pm 0.0050$, while above the knee, it is -$3.128 \pm 0.005 \pm 0.027$, with the sharpness of the transition measured with a statistical error of 2%. The mean logarithmic mass of cosmic rays is almost heavier than helium in the whole measured energy range. It decreases from 1.7 at 0.3 PeV to 1.3 at 3 PeV, representing a 24% decline following a power law with an index of -$0.1200 \pm 0.0003 \pm 0.0341$. This is equivalent to an increase in abundance of light components. Above the knee, the mean logarithmic mass exhibits a power law trend towards heavier components, which is reversal to the behavior observed in the all-particle energy spectrum. Additionally, the knee position and the change in power-law index are approximately the same. These findings suggest that the knee observed in the all-particle spectrum corresponds to the knee of the light component, rather than the medium-heavy components., Comment: 8 pages, 3 figures
Published: 2024
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12. Single-Cell RNA Sequencing Reveals Peripheral Immune Cell Senescence and Inflammatory Phenotypes in Patients with Premature Ovarian Failure

Author: Liu J, Wang L, Zhong W, Cai J, Sun Y, Li S, Li J, Liu Y, and Xiong F
Subjects: premature ovarian failure, immune microenvironment, single-cell rna sequencing, monocytes, cd8+ effector t cells, ccl5, Pathology, RB1-214, Therapeutics. Pharmacology, RM1-950
Abstract: Jianan Liu,1 Li Wang,2 Weijun Zhong,1 Jing Cai,3 Yan Sun,3 SongJun Li,2 Jiayi Li,4 Yanhui Liu,2 Fu Xiong1,5,6 1Department of Medical Genetics/Experimental Education/Administration Center, School of Basic Medical Sciences, Southern Medical University, Guangzhou, People’s Republic of China; 2Reproductive Medicine Department, The Third Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong, People’s Republic of China; 3Reproductive Medicine Department, Dongguan Maternal and Children Health Hospital, Dongguan, Guangdong, People’s Republic of China; 4The First Clinical Medical School, Nan Fang Hospital, Southern Medical University, Guangzhou, People’s Republic of China; 5Guangdong Provincial Key Laboratory of Single Cell Technology and Application, Guangzhou, Guangdong, People’s Republic of China; 6Department of Fetal Medicine and Prenatal Diagnosis, Zhujiang Hospital, Southern Medical University, Guangzhou, People’s Republic of ChinaCorrespondence: Fu Xiong, Department of Medical Genetics/Experimental Education/Administration Center, School of Basic Medical Sciences, Southern Medical University, Guangzhou, 510515, People’s Republic of China, Email xiongfu@smu.edu.cn Yanhui Liu, Reproductive Medicine Department, The Third Affiliated Hospital of Shenzhen University, Shenzhen, 518061, People’s Republic of China, Email liuliang71215@163.comBackground: Premature Ovarian Failure (POF) is a heterogeneous syndrome characterized by ovarian dysfunction, frequently associated with autoimmune factors. The interaction between peripheral and ovarian immune signals remains unclear. Recent advancements in single-cell technology provide a unique opportunity to examine the complex peripheral immune response in POF patients at the microstructural level. This study investigates the immune microenvironment’s complexity through the interaction between peripheral and ovarian local immune responses.Methods: Peripheral blood mononuclear cells (PBMCs) were isolated from three healthy individuals and four POF patients. Single-cell RNA sequencing (scRNA-seq) was used to delineate cell clusters and identify differentially expressed genes (DEGs). Enrichment, SCENIC, and pseudo-time analyses were utilized to explore cellular phenotype diversity, regulatory patterns, and evolutionary trajectories. A POF mouse model was used for validation.Results: Seven clusters were identified and classified into two groups. POF patients exhibited increased proportions in T cells, NK cells, and B cells as well as upregulated IGLC2, GNLY, GZMB, FCGR3A, and CCL5 expressions compared to healthy controls. Monocytes, particularly non-classical monocytes, exhibited inflammatory phenotypes. CD8+ Effector T cells demonstrated increased cytotoxicity and TCR clonal expansion. The trajectory of CD8+ Effector T cells in POF patients involved the synchronous upregulation of cytotoxic-related genes and immune checkpoint molecules. Notably, CCL5, primarily produced by non-classical monocytes, emerged as a critical factor. Elevated levels of CCL5 in plasma and local ovaries, along with increased CD8+ T cell infiltration, suggested its potential role in chemotaxis and ovarian damage in POF. Validation in the POF mouse model further supported these findings.Conclusion: In summary, this study provides in-depth insights into the immune landscape of POF, revealing distinct cell populations, pathways, and signaling networks linked to the disease. These findings enhance our understanding of POF’s immunological mechanisms, contributing to the development of potential diagnostic and therapeutic strategies.Keywords: premature ovarian failure, immune microenvironment, single-cell RNA sequencing, monocytes, CD8+ effector T cells, CCL5
Published: 2025

13. Comparison of Effect Sevoflurane-Based Anesthesia and Propofol-Based Anesthesia on the Early Postoperative Renal Function of Living Kidney Transplant Donors: A Randomized Controlled Trial

Author: Cai J, Kang F, Han M, Huang X, Yan W, Wan F, and Li J
Subjects: general anesthetics, sevoflurane, propofol, renal transplantation., Therapeutics. Pharmacology, RM1-950
Abstract: Jianyue Cai,1,&ast; Fang Kang,1,&ast; Mingming Han,1 Xiang Huang,1 Wenlong Yan,1 Fuzhen Wan,2,3 Juan Li1 1Department of Anesthesiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, People’s Republic of China; 2Department of Gastrointestinal Surgery, The second Affiliated Hospital of Anhui Medical University, Hefei, People’s Republic of China; 3Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia&ast;These authors contributed equally to this workCorrespondence: Juan Li, Administrative Director, Department of Anesthesia, First Affiliated Hospital of the University of Science and Technology of China, Tel +8613956005465, Email huamuzi1999@163.comPurpose: Living kidney transplantation is a common treatment for end-stage renal disease. The impact of anaesthetics on postoperative biomarkers of renal injury in living kidney transplant donors is not well understood.Patients and Methods: 70 transplant donors who underwent kidney extraction were randomly assigned to following two groups: sevoflurane (S group) and propofol (P group). Urine and blood were collected before induction and 1, 2, 6 days after operation. Kidney injury marker-1 (KIM-1), interleukin-18 (IL-18) and tissue inhibitor of metalloproteinase-2 (TIMP-2) were measured by enzyme-linked immunosorbent assay. Record the cystatin C, glomerular filtration rate, urine output during perioperative period.Results: There were both increases in biomarkers of kidney injury before and 1, 2 and 6 days after the anaesthetic surgery in donors, However, no statistical differences in KIM-1 (P (0.42 pg/mL (95% CI 0.21 to 0.63 pg/mL)) vs S (0.26 pg/mL (95% CI 0.02 to 0.49 pg/mL)), − 0.16 pg/mL (95% CI − 0.48 to 0.16 pg/mL)), IL-18 (P (178.54 pg/mL (95% CI 110.15 to 24693 pg/mL)) vs S (175.86 pg/mL (95% CI 100.35 to 251.38 pg/mL)), − 2.68 pg/mL (95% CI − 105.61 to 100.25 pg/mL)), and TIMP-2 (P (12.88 ng/mL (95% CI 8.69 to 17.07 ng/mL)) vs S (14.85 ng/mL (95% CI 10.23 to 19.46 ng/mL)), 1.97 ng/mL (95% CI − 4.30 to 8.23 ng/mL)) concentration changes between the two types of anaesthesia.Conclusion: There was no difference between sevoflurane and propofol anaesthesia on postoperative changes in biomarkers of renal injury in living kidney transplant donors.Keywords: general anesthetics, sevoflurane, propofol, renal transplantation
Published: 2025

14. Does or did the supernova remnant Cassiopeia A operate as a PeVatron?

Author: Cao, Zhen, Aharonian, F., An, Q., Axikegu, Bai, Y. X., Bao, Y. W., Bastieri, D., Bi, X. J., Bi, Y. J., Cai, J. T., Cao, Q., Cao, W. Y., Cao, Zhe, Chang, J., Chang, J. F., Chen, A. M., Chen, E. S., Chen, Liang, Chen, Lin, Chen, Long, Chen, M. J., Chen, M. L., Chen, Q. H., Chen, S. H., Chen, S. Z., Chen, T. L., Chen, Y., Cheng, N., Cheng, Y. D., Cui, M. Y., Cui, S. W., Cui, X. H., Cui, Y. D., Dai, B. Z., Dai, H. L., Dai, Z. G., Danzengluobu, della Volpe, D., Dong, X. Q., Duan, K. K., Fan, J. H., Fan, Y. Z., Fang, J., Fang, K., Feng, C. F., Feng, L., Feng, S. H., Feng, X. T., Feng, Y. L., Gabici, S., Gao, B., Gao, C. D., Gao, L. Q., Gao, Q., Gao, W., Gao, W. K., Ge, M. M., Geng, L. S., Giacinti, G., Gong, G. H., Gou, Q. B., Gu, M. H., Guo, F. L., Guo, X. L., Guo, Y. Q., Guo, Y. Y., Han, Y. A., He, H. H., He, H. N., He, J. Y., He, X. B., He, Y., Heller, M., Hor, Y. K., Hou, B. W., Hou, C., Hou, X., Hu, H. B., Hu, Q., Hu, S. C., Huang, D. H., Huang, T. Q., Huang, W. J., Huang, X. T., Huang, X. Y., Huang, Y., Huang, Z. C., Ji, X. L., Jia, H. Y., Jia, K., Jiang, K., Jiang, X. W., Jiang, Z. J., Jin, M., Kang, M. M., Ke, T., Kuleshov, D., Kurinov, K., Li, B. B., Li, Cheng, Li, Cong, Li, D., Li, F., Li, H. B., Li, H. C., Li, H. Y., Li, J., Li, Jian, Li, Jie, Li, K., Li, W. L., Li, X. R., Li, Xin, Li, Y. Z., Li, Zhe, Li, Zhuo, Liang, E. W., Liang, Y. F., Lin, S. J., Liu, B., Liu, C., Liu, D., Liu, H., Liu, H. D., Liu, J., Liu, J. L., Liu, J. Y., Liu, M. Y., Liu, R. Y., Liu, S. M., Liu, W., Liu, Y., Liu, Y. N., Lu, R., Luo, Q., Lv, H. K., Ma, B. Q., Ma, L. L., Ma, X. H., Mao, J. R., Min, Z., Mitthumsiri, W., Mu, H. J., Nan, Y. C., Neronov, A., Ou, Z. W., Pang, B. Y., Pattarakijwanich, P., Pei, Z. Y., Qi, M. Y., Qi, Y. Q., Qiao, B. Q., Qin, J. J., Ruffolo, D., Sáiz, A., Semikoz, D., Shao, C. Y., Shao, L., Shchegolev, O., Sheng, X. D., Shu, F. W., Song, H. C., Stenkin, Yu. V., Stepanov, V., Su, Y., Sun, Q. N., Sun, X. N., Sun, Z. B., Tam, P. H. T., Tang, Q. W., Tang, Z. B., Tian, W. W., Wang, C., Wang, C. B., Wang, G. W., Wang, H. G., Wang, H. H., Wang, J. C., Wang, K., Wang, L. P., Wang, L. Y., Wang, P. H., Wang, R., Wang, W., Wang, X. G., Wang, X. Y., Wang, Y., Wang, Y. D., Wang, Y. J., Wang, Z. H., Wang, Z. X., Wang, Zhen, Wang, Zheng, Wei, D. M., Wei, J. J., Wei, Y. J., Wen, T., Wu, C. Y., Wu, H. R., Wu, S., Wu, X. F., Wu, Y. S., Xi, S. Q., Xia, J., Xia, J. J., Xiang, G. M., Xiao, D. X., Xiao, G., Xin, G. G., Xin, Y. L., Xing, Y., Xiong, Z., Xu, D. L., Xu, R. F., Xu, R. X., Xu, W. L., Xue, L., Yan, D. H., Yan, J. Z., Yan, T., Yang, C. W., Yang, F., Yang, F. F., Yang, H. W., Yang, J. Y., Yang, L. L., Yang, M. J., Yang, R. Z., Yang, S. B., Yao, Y. H., Yao, Z. G., Ye, Y. M., Yin, L. Q., Yin, N., You, X. H., You, Z. Y., Yu, Y. H., Yuan, Q., Yue, H., Zeng, H. D., Zeng, T. X., Zeng, W., Zha, M., Zhang, B. B., Zhang, F., Zhang, H. M., Zhang, H. Y., Zhang, J. L., Zhang, L. X., Zhang, Li, Zhang, P. F., Zhang, P. P., Zhang, R., Zhang, S. B., Zhang, S. R., Zhang, S. S., Zhang, X., Zhang, X. P., Zhang, Y. F., Zhang, Yi, Zhang, Yong, Zhao, B., Zhao, J., Zhao, L., Zhao, L. Z., Zhao, S. P., Zheng, F., Zhou, B., Zhou, H., Zhou, J. N., Zhou, M., Zhou, P., Zhou, R., Zhou, X. X., Zhu, C. G., Zhu, F. R., Zhu, H., Zhu, K. J., and Zuo., X.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: For decades, supernova remnants (SNRs) have been considered the prime sources of Galactic Cosmic rays (CRs). But whether SNRs can accelerate CR protons to PeV energies and thus dominate CR flux up to the knee is currently under intensive theoretical and phenomenological debate. The direct test of the ability of SNRs to operate as CR PeVatrons can be provided by ultrahigh-energy (UHE; $E_\gamma \geq 100$~TeV) $\gamma$-rays. In this context, the historical SNR Cassiopeia A (Cas A) is considered one of the most promising target for UHE observations. This paper presents the observation of Cas A and its vicinity by the LHAASO KM2A detector. The exceptional sensitivity of LHAASO KM2A in the UHE band, combined with the young age of Cas A, enabled us to derive stringent model-independent limits on the energy budget of UHE protons and nuclei accelerated by Cas A at any epoch after the explosion. The results challenge the prevailing paradigm that Cas A-type SNRs are major suppliers of PeV CRs in the Milky Way., Comment: 11 pages, 3 figures, Accepted by the APJL
Published: 2023

15. Very high energy gamma-ray emission beyond 10 TeV from GRB 221009A

Author: Cao, Zhen, Aharonian, F., An, Q., Axikegu, A., Bai, Y. X., Bao, Y. W., Bastieri, D., Bi, X. J., Bi, Y. J., Cai, J. T., Cao, Q., Cao, W. Y., Cao, Zhe, Chang, J., Chang, J. F., Chen, A. M., Chen, E. S., Chen, Liang, Chen, Lin, Chen, Long, Chen, M. J., Chen, M. L., Chen, Q. H., Chen, S. H., Chen, S. Z., Chen, T. L., Chen, Y., Cheng, N., Cheng, Y. D., Cui, M. Y., Cui, S. W., Cui, X. H., Cui, Y. D., Dai, B. Z., Dai, H. L., Dai, Z. G., Danzengluobu, della Volpe, D., Dong, X. Q., Duan, K. K., Fan, J. H., Fan, Y. Z., Fang, J., Fang, K., Feng, C. F., Feng, L., Feng, S. H., Feng, X. T., Feng, Y. L., Gabici, S., Gao, B., Gao, C. D., Gao, L. Q., Gao, Q., Gao, W., Gao, W. K., Ge, M. M., Geng, L. S., Giacinti, G., Gong, G. H., Gou, Q. B., Gu, M. H., Guo, F. L., Guo, X. L., Guo, Y. Q., Guo, Y. Y., Han, Y. A., He, H. H., He, H. N., He, J. Y., He, X. B., He, Y., Heller, M., Hor, Y. K., Hou, B. W., Hou, C., Hou, X., Hu, H. B., Hu, Q., Hu, S. C., Huang, D. H., Huang, T. Q., Huang, W. J., Huang, X. T., Huang, X. Y., Huang, Y., Huang, Z. C., Ji, X. L., Jia, H. Y., Jia, K., Jiang, K., Jiang, X. W., Jiang, Z. J., Jin, M., Kang, M. M., Ke, T., Kuleshov, D., Kurinov, K., Li, B. B., Li, Cheng, Li, Cong, Li, D., Li, F., Li, H. B., Li, H. C., Li, H. Y., Li, J., Li, Jian, Li, Jie, Li, K., Li, W. L., Li, X. R., Li, Xin, Li, Y. Z., Li, Zhe, Li, Zhuo, Liang, E. W., Liang, Y. F., Lin, S. J., Liu, B., Liu, C., Liu, D., Liu, H., Liu, H. D., Liu, J., Liu, J. L., Liu, J. Y., Liu, M. Y., Liu, R. Y., Liu, S. M., Liu, W., Liu, Y., Liu, Y. N., Lu, R., Luo, Q., Lv, H. K., Ma, B. Q., Ma, L. L., Ma, X. H., Mao, J. R., Min, Z., Mitthumsiri, W., Mu, H. J., Nan, Y. C., Neronov, A., Ou, Z. W., Pang, B. Y., Pattarakijwanich, P., Pei, Z. Y., Qi, M. Y., Qi, Y. Q., Qiao, B. Q., Qin, J. J., Ruffolo, D., Sáiz, A., Semikoz, D., Shao, C. Y., Shao, L., Shchegolev, O., Sheng, X. D., Shu, F. W., Song, H. C., Stenkin, Yu. V., Stepanov, V., Su, Y., Sun, Q. N., Sun, X. N., Sun, Z. B., Tam, P. H. T., Tang, Q. W., Tang, Z. B., Tian, W. W., Wang, C., Wang, C. B., Wang, G. W., Wang, H. G., Wang, H. H., Wang, J. C., Wang, K., Wang, L. P., Wang, L. Y., Wang, P. H., Wang, R., Wang, W., Wang, X. G., Wang, X. Y., Wang, Y., Wang, Y. D., Wang, Y. J., Wang, Z. H., Wang, Z. X., Wang, Zhen, Wang, Zheng, Wei, D. M., Wei, J. J., Wei, Y. J., Wen, T., Wu, C. Y., Wu, H. R., Wu, S., Wu, X. F., Wu, Y. S., Xi, S. Q., Xia, J., Xia, J. J., Xiang, G. M., Xiao, D. X., Xiao, G., Xin, G. G., Xin, Y. L., Xing, Y., Xiong, Z., Xu, D. L., Xu, R. F., Xu, R. X., Xu, W. L., Xue, L., Yan, D. H., Yan, J. Z., Yan, T., Yang, C. W., Yang, F., Yang, F. F., Yang, H. W., Yang, J. Y., Yang, L. L., Yang, M. J., Yang, R. Z., Yang, S. B., Yao, Y. H., Yao, Z. G., Ye, Y. M., Yin, L. Q., Yin, N., You, X. H., You, Z. Y., Yu, Y. H., Yuan, Q., Yue, H., Zeng, H. D., Zeng, T. X., Zeng, W., Zha, M., Zhang, B. B., Zhang, F., Zhang, H. M., Zhang, H. Y., Zhang, J. L., Zhang, L. X., Zhang, Li, Zhang, P. F., Zhang, P. P., Zhang, R., Zhang, S. B., Zhang, S. R., Zhang, S. S., Zhang, X., Zhang, X. P., Zhang, Y. F., Zhang, Yi, Zhang, Yong, Zhao, B., Zhao, J., Zhao, L., Zhao, L. Z., Zhao, S. P., Zheng, F., Zhou, B., Zhou, H., Zhou, J. N., Zhou, M., Zhou, P., Zhou, R., Zhou, X. X., Zhu, C. G., Zhu, F. R., Zhu, H., Zhu, K. J., and Zuo, X.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: The highest energy gamma-rays from gamma-ray bursts (GRBs) have important implications for their radiation mechanism. Here we report for the first time the detection of gamma-rays up to 13 TeV from the brightest GRB 221009A by the Large High Altitude Air-shower Observatory (LHAASO). The LHAASO-KM2A detector registered more than 140 gamma-rays with energies above 3 TeV during 230$-$900s after the trigger. The intrinsic energy spectrum of gamma-rays can be described by a power-law after correcting for extragalactic background light (EBL) absorption. Such a hard spectrum challenges the synchrotron self-Compton (SSC) scenario of relativistic electrons for the afterglow emission above several TeV. Observations of gamma-rays up to 13 TeV from a source with a measured redshift of z=0.151 hints more transparency in intergalactic space than previously expected. Alternatively, one may invoke new physics such as Lorentz Invariance Violation (LIV) or an axion origin of very high energy (VHE) signals., Comment: 49pages, 11figures
Published: 2023
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16. Gate-controlled suppression of light-driven proton transport through graphene electrodes

Author: Huang, S., Griffin, E., Cai, J., Xin, B., Tong, J., Fu, Y., Kravets, V., Peeters, F. M., and Lozada-Hidalgo, M.
Subjects: Condensed Matter - Mesoscale and Nanoscale Physics, Physics - Chemical Physics
Abstract: Recent experiments demonstrated that proton transport through graphene electrodes can be accelerated by over an order of magnitude with low intensity illumination. Here we show that this photo-effect can be suppressed for a tuneable fraction of the infrared spectrum by applying a voltage bias. Using photocurrent measurements and Raman spectroscopy, we show that such fraction can be selected by tuning the Fermi energy of electrons in graphene with a bias, a phenomenon controlled by Pauli blocking of photo-excited electrons. These findings demonstrate a dependence between graphene's electronic and proton transport properties and provide fundamental insights into molecularly thin electrode-electrolyte interfaces and their interaction with light.
Published: 2023
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17. Effect of Al2O3 on the Mechanical Properties of (B4C + Al2O3)/Al Neutron Absorbing Materials

Author: Cai, J. X., Shi, B. M., Li, N., Liu, Y., Zhang, Z. G., Zan, Y. N., Wang, Q. Z., Xiao, B. L., and Ma, Z. Y.
Published: 2024
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18. Causes and consequences of tipping points in river delta social–ecological systems

Author: Cremin, Emilie, Ladd, Cai J. T., Balke, Thorsten, Banerjee, Sumana, Bui, Ly H., Ghosh, Tuhin, Large, Andy, Thi Van Le, Hue, Nguyen, Kien V., Nguyen, Lan X., Nguyen, Tanh T. N., Nguyen, Vinh, Pal, Indrajit, Szabo, Sylvia, Tran, Ha, Sebesvari, Zita, Khan, Shah Alam, and Renaud, Fabrice G.
Published: 2024
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19. Towards the demonstration of photon-photon collision with compact lasers

Author: Han, L. Q., Cai, J., Shou, Y. R., Liu, X. D., Yu, J. Q., and Yan, X. Q.
Subjects: Physics - Plasma Physics
Abstract: We report a proposal to observe the two-photon Breit-Wheeler process in plasma driven by compact lasers. A high charge electron bunch can be generated from laser plasma wakefield acceleration when a tightly focused laser pulse transports in a sub-critical density plasma. The electron bunch scatters with the laser pulse coming from the opposite direction and results the emitting of high brilliance X-ray pulses. In a three-dimensional particle-in-cell simulation with a laser pulse of $\sim$10 J, one could produce a X-ray pulse with photon number higher than $3\times10^{11}$ and brilliance above $1.6\times 10^{23}$ photons/s/mm$^2$/mrad$^2$/0.1$\%$BW at 1 MeV. The X-ray pulses collide in the plasma and create more than $1.1\times 10^5$ electron-positron pairs per shot. It is also found that the positrons can be accelerated transversely by a transverse electric field generated in the plasma, which enables the safe detection in the direction away from the laser pulses. This proposal which has solved key challenges in laser driven photon-photon collision could demonstrate the two-photon Breit-Wheeler process on a much more compact device in a single shot.
Published: 2023

20. Deep Learning-Based Quantification of Adenoid Hypertrophy and Its Correlation with Apnea-Hypopnea Index in Pediatric Obstructive Sleep Apnea

Author: Cai J, Xiu T, Song Y, Fan X, Wu J, Tuohuti A, Hu Y, and Chen X
Subjects: adenoid hypertrophy, obstructive sleep apnea (osa), deep learning, transfer learning, ensemble learning., Psychiatry, RC435-571, Neurophysiology and neuropsychology, QP351-495
Abstract: Jie Cai,1,&ast; Tianyu Xiu,2,&ast; Yuliang Song,1 Xuwei Fan,3 Jianghao Wu,1 Aikebaier Tuohuti,1 Yifan Hu,1 Xiong Chen1,4 1Department of Otorhinolaryngology, Head and Neck Surgery, Zhongnan Hospital of Wuhan University, Wuhan, 430000, People’s Republic of China; 2State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 430079, People’s Republic of China; 3School of Informatics, Xiamen University, Xiamen, 361000, People’s Republic of China; 4Sleep Medicine Center, Zhongnan Hospital of Wuhan University, Wuhan, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Xiong Chen; Tianyu Xiu, Email zn_chenxiong@whu.edu.cn; xiutianyu@whu.edu.cnPurpose: This study aims to develop a deep learning methodology for quantitative assessing adenoid hypertrophy in nasopharyngoscopy images and to investigate its correlation with the apnea-hypopnea index (AHI) in pediatric patients with obstructive sleep apnea (OSA).Patients and Methods: A total of 1642 nasopharyngoscopy images were collected from pediatric patients aged 3 to 12 years. After excluding images with obscured secretions, incomplete adenoid exposure, 1500 images were retained for analysis. The adenoid-to-nasopharyngeal (A/N) ratio was manually annotated by two experienced otolaryngologists using MATLAB’s imfreehand tool. Inter-annotator agreement was assessed using the Mann–Whitney U-test. Deep learning segmentation models were developed with the MMSegmentation framework, incorporating transfer learning and ensemble learning techniques. Model performance was evaluated using precision, recall, mean intersection over union (MIoU), overall accuracy, Cohen’s Kappa, confusion matrices, and receiver operating characteristic (ROC) curves. The correlation between the A/N ratio and AHI, derived from polysomnography, was analyzed to evaluate clinical relevance.Results: Manual evaluation of adenoid hypertrophy by otolaryngologists (p=0.8507) and MATLAB calibration (p=0.679) demonstrated high consistency, with no significant differences. Among the deep learning models, the ensemble learning-based SUMNet outperformed others, achieving the highest precision (0.9616), MIoU (0.8046), overall accuracy (0.9182), and Kappa (0.87). SUMNet also exhibited superior consistency in classifying adenoid sizes. ROC analysis revealed that SUMNet (AUC=0.85) outperformed expert evaluations (AUC=0.74). A strong positive correlation was observed between the A/N ratio and AHI, with the correlation coefficients for SUMNet-derived ratios ranging from r=0.9052 (tonsils size+1) to r=0.4452 (tonsils size+3) and for expert-derived ratios ranging from r=0.4590 (tonsils size+1) to r=0.2681 (tonsils size+3).Conclusion: This study introduces a precise and reliable deep learning-based method for quantifying adenoid hypertrophy and addresses the challenge posed limited sample sizes in deep learning applications. The significant correlation between adenoid hypertrophy and AHI underscores the clinical utility of this method in pediatric OSA diagnosis.Keywords: adenoid hypertrophy, obstructive sleep apnea (OSA), deep learning, transfer learning, ensemble learning
Published: 2024

21. Recent Developments in Drug Design of Oral Synthetic Free Fatty Acid Receptor 1 Agonists

Author: Liu L, Zhang Q, Ma Y, Lin L, Liu W, Ding A, Wang C, Zhou S, Cai J, and Tang H
Subjects: antidiabetics, oral ffar1 development, natural inspired structure modification, fatty acid-based lipotoxicity, artificial intelligence aided, Therapeutics. Pharmacology, RM1-950
Abstract: Lei Liu,1,2 Qinghua Zhang,1,2 Yichuan Ma,3 Ling Lin,2 Wenli Liu,2 Aizhong Ding,2 Chunjian Wang,2 Shuiping Zhou,1 Jinyong Cai,1 Hai Tang1,2 1Tasly Academy, Tasly Pharma Co., Ltd., Tianjin, People’s Republic of China; 2Tasly Academy Jiangsu Branch, Jiangsu Tasly Diyi Pharmaceutical Co., Ltd., Huaian, Jiangsu, People’s Republic of China; 3China Medical University (CMU)-The Queen’s University of Belfast (QUB) Joint College, Shenyang, Liaoning, People’s Republic of ChinaCorrespondence: Hai Tang, Tasly Academy, Tasly Pharma Co., Ltd., No. 1 Tingjiang Road, Beichen District, Tianjin, 300410, People’s Republic of China, Email tsl-tanghai2020@tasly.comAbstract: Over the past two decades, synthetic FFAR1 agonists such as TAK-875 and TSL1806 have undergone meticulous design and extensive clinical trials. However, due to issues primarily related to hepatotoxicity, no FFAR1 agonist has yet received regulatory approval. Research into the sources of hepatotoxicity suggests that one potential cause lies in the molecular structure itself. These structures typically feature lipid-like carboxylic acid head groups, which tend to generate toxic metabolites. Strategies to mitigate these risks focus on optimizing chemical groups to reduce lipophilicity and prevent the formation of reactive metabolites. Recent studies have concentrated on developing low-molecular-weight compounds that more closely resemble natural products, with CPL207280 showing promising potential and liver safety, currently in Phase II clinical trials. Moreover, ongoing research continues to explore the potential applications of FFAR1 agonists in diabetes management, as well as in conditions such as non-alcoholic fatty liver disease (NAFLD) and cerebrovascular diseases. Utilizing advanced technologies such as artificial intelligence and computer-aided design, the development of compact molecules that mimic natural structures represents a hopeful direction for future research and development.Keywords: antidiabetics, oral FFAR1 development, natural-inspired structure modification, hepatotoxicity, fatty acid-based lipotoxicity, artificial intelligence aided
Published: 2024

22. Levistolide a Attenuates Acute Kidney Injury in Mice by Inhibiting the TLR-4/NF-κB Pathway

Author: Shi J, Li S, Yi L, Gao M, Cai J, Yang C, Ma Y, Mo Y, and Wang Q
Subjects: acute kidney injury, levistolide a, tlr-4/nf-κb, inflammatory response, antioxidant effect, Therapeutics. Pharmacology, RM1-950
Abstract: Jiahui Shi,1 Shuangwei Li,1 Langping Yi,1 Minghuang Gao,1 Jiaying Cai,1 Cong Yang,1 Yujie Ma,1 Yousheng Mo,2 Qi Wang1 1Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, People’s Republic of China; 2Department of Hepatology, Guangdong Provincial Hospital of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, People’s Republic of ChinaCorrespondence: Yousheng Mo; Qi Wang, Email 378480308@qq.com; wangqi@gzucm.edu.cnIntroduction: Acute kidney injury (AKI) is characterized by a significant reduction in kidney function and the accumulation of metabolites such as Creatinine (CRE) and Blood Urea Nitrogen (BUN). Levistolide A (LA), an active component of Ligusticum chuanxiong, offers multiple therapeutic benefits, including cardiovascular and neuroprotection, antitumor and analgesic effects, as well as anti-inflammatory, antioxidant, antifibrotic, and proapoptotic actions. However, the underlying mechanism of LA in treating AKI has not been fully elucidated.Methods: In this study, we established a glycerol-induced AKI model in mice to evaluate the protective effects of LA. Renal function was assessed by measuring levels of CRE and BUN. Histological analyses were performed to evaluate kidney tissue damage. Additionally, oxidative stress markers, apoptosis indicators, inflammatory cell infiltration, and inflammatory mediator levels were assessed. The involvement of the TLR-4/NF-κB signaling pathway was investigated through molecular assays.Results: LA treatment significantly ameliorated glycerol-induced AKI in mice, evidenced by reduced levels of CRE and BUN. Histological examination revealed decreased renal tissue damage in LA-treated groups. LA exerted antioxidant effects by increasing the levels of Glutathione (GSH) and Superoxide Dismutase (SOD), while reducing Reactive Oxygen Species (ROS) accumulation. Apoptosis in renal tissues was attenuated, as indicated by decreased caspase-3 activation. Furthermore, LA reduced the infiltration of inflammatory cells and the release of inflammatory mediators such as TNF-α and IL-6. Mechanistically, LA suppressed the inflammatory response by inhibiting the TLR-4/NF-κB signaling pathway, as demonstrated by reduced NF-κB activation and decreased expression of TLR-4.Conclusion: Levistolide A mitigates acute kidney injury through its antioxidative properties and modulation of the TLR-4/NF-κB signaling pathway. These findings provide valuable insights into the therapeutic potential of LA for AKI treatment and lay the groundwork for further mechanistic studies.Keywords: acute kidney injury, levistolide A, TLR-4/NF-&Kgr;b, inflammatory response, antioxidant effect
Published: 2024

23. Soil carbon in the world’s tidal marshes

Author: Tania L. Maxwell, Mark D. Spalding, Daniel A. Friess, Nicholas J. Murray, Kerrylee Rogers, Andre S. Rovai, Lindsey S. Smart, Lukas Weilguny, Maria Fernanda Adame, Janine B. Adams, William E. N. Austin, Margareth S. Copertino, Grace M. Cott, Micheli Duarte de Paula Costa, James R. Holmquist, Cai J. T. Ladd, Catherine E. Lovelock, Marvin Ludwig, Monica M. Moritsch, Alejandro Navarro, Jacqueline L. Raw, Ana-Carolina Ruiz-Fernández, Oscar Serrano, Craig Smeaton, Marijn Van de Broek, Lisamarie Windham-Myers, Emily Landis, and Thomas A. Worthington
Subjects: Science
Abstract: Abstract Tidal marshes are threatened coastal ecosystems known for their capacity to store large amounts of carbon in their water-logged soils. Accurate quantification and mapping of global tidal marshes soil organic carbon (SOC) stocks is of considerable value to conservation efforts. Here, we used training data from 3710 unique locations, landscape-level environmental drivers and a global tidal marsh extent map to produce a global, spatially explicit map of SOC storage in tidal marshes at 30 m resolution. Here we show the total global SOC stock to 1 m to be 1.44 Pg C, with a third of this value stored in the United States of America. On average, SOC in tidal marshes’ 0–30 and 30–100 cm soil layers are estimated at 83.1 Mg C ha−1 (average predicted error 44.8 Mg C ha−1) and 185.3 Mg C ha−1 (average predicted error 105.7 Mg C ha−1), respectively.
Published: 2024
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24. Using Network Analysis to Subgroup Risk Factors for Depressive Symptoms in College Students

Author: Ding J, Wu Y, Li H, Wang S, Cai J, Cheng H, and Liang S
Subjects: depressive symptoms, anhedonia, smartphone addiction, network analysis, college students, Psychology, BF1-990, Industrial psychology, HF5548.7-5548.85
Abstract: Jinqi Ding,1,&ast; Yue Wu,1,&ast; Hanxiaoran Li,1 Shengsheng Wang,1 Jia Cai,2 Hong Cheng,3 Sugai Liang1 1Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, People’s Republic of China; 2Mental Health Center & Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, People’s Republic of China; 3Hangzhou City University, Hangzhou, Zhejiang, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Sugai Liang, Email liangsugai@zju.edu.cnPurpose: Network modeling has been suggested as an effective method to explore intricate relationships among antecedents, mediators, and symptoms. In this study, we aimed to investigate whether the severity of depressive symptoms in college students affects the multivariate relationships among anhedonia, smartphone addiction, and mediating factors.Methods: A survey was conducted among 1347 Chinese college students (587 female) to assess depressive symptoms, anhedonia, addictive behaviors, anxiety, and insomnia. The participants were categorized the non-depressive symptom (NDS) and depressive symptom (DS) groups based on a cut-off score of 5 on the 9-item Patient Health Questionnaire-9. Network analysis was performed to investigate the symptom-to-symptom influences of symptoms in these two groups.Results: The network of the DS group was more densely connected than that of the NDS group. Social anticipatory anhedonia was a central factor for DS, while withdraw/escape (one factor of smartphone addiction) was a central factor for NDS. The DS group exhibited greater strength between the PHQ9 score and social anticipatory anhedonia, as well as between the PHQ9 score and alcohol misuse score, compared to the NDS group. On the other hand, the NDS group had higher strength between anxiety and feeling lost, as well as between anxiety and withdraw/escape, compared to the DS group.Conclusion: The findings suggest that there is a close relationship between social anhedonia, smartphone addiction, and alcohol consumption in the DS group. Addressing on ameliorating social anhedonia and smartphone addiction may be effective in preventing and managing depression in college students.Keywords: depressive symptoms, anhedonia, smartphone addiction, network analysis, college students
Published: 2024

25. Three Artificial Liver Models of Treatment of Acute-on-Chronic Liver Failure

Author: Xiang Y, Li R, Cai J, and Jiang Q
Subjects: artificial liver treatment mode, acute-on-chronic liver failure, plasma exchange, plasma perfusion, double plasma molecular adsorption system, Therapeutics. Pharmacology, RM1-950
Abstract: Yanping Xiang, Renhua Li, Jia Cai, Qian Jiang Department of Infectious Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of ChinaCorrespondence: Renhua Li, Department of Infectious Diseases, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuanjiagang, Yuzhong District, Chongqing, 400016, People’s Republic of China, Email 854561075@qq.comBackground: This study aimed to investigate clinical efficacy, safety and short-term prognosis of plasma exchange (PE), plasma perfusion combining PE (PP+PE), dual-plasma molecular adsorption system combining PE (DPMAS+PE) in treating acute-on-chronic liver failure (ACLF).Methods: Two hundred and fourteen ACLF patients admitted to our hospital were included and divided into PE (n = 72), PP+PE (n = 75), DPMAS+PE group (n = 67). Laboratory indexes and MELD scores were collected, and clinical efficacy was compared. Patients’ adverse reactions during and 24-h after treatment were collected, and safety was compared. Survival status of patients was followed-up within 90 days, and prognosis was analyzed.Results: PE, PP+PE and DPMAS+PE significantly reduce TBiL, DBiL, ALT, AST, SA, PT, INR, PCT and CRP levels, and increase PA and PTA levels, compared with pre-treatments (P < 0.05). WBC and SCR levels in DPMAS+PE group decreased significantly post-treatment (P < 0.05). Na+ and Cl− levels in PE and PP+PE group decreased significantly post-treatment (P < 0.05). Total adverse reaction incidence in PE, PP+PE, DPMAS+PE group were 38.89%, 22.70%, 17.90%, respectively, with significant differences among three groups (P < 0.05). Ninety-day mortality rates of patients in PE, PP+PE, DPMAS+PE group were 41.67%, 34.67%, 20.90%, respectively, with significant differences among three groups (P < 0.05).Conclusion: PE, PP+PE and DPMAS+PE three artificial liver treatment modes can effectively improve liver, kidney and coagulation function of ACLF patients. DPMAS+PE demonstrated better ability to remove endotoxin and inflammatory mediators, showed advantages in reducing ACLF patient mortality within 90 days, and had the least impact on electrolyte post-treatment. Therefore, DPMAS+PE can be used as a better choice for clinical treatment.Keywords: artificial liver treatment mode, acute-on-chronic liver failure, plasma exchange, plasma perfusion, double plasma molecular adsorption system
Published: 2024

26. The First LHAASO Catalog of Gamma-Ray Sources

Author: Cao, Zhen, Aharonian, F., An, Q., Axikegu, Bai, Y. X., Bao, Y. W., Bastieri, D., Bi, X. J., Bi, Y. J., Cai, J. T., Cao, Q., Cao, W. Y., Cao, Zhe, Chang, J., Chang, J. F., Chen, A. M., Chen, E. S., Chen, Liang, Chen, Lin, Chen, Long, Chen, M. J., Chen, M. L., Chen, Q. H., Chen, S. H., Chen, S. Z., Chen, T. L., Chen, Y., Cheng, N., Cheng, Y. D., Cui, M. Y., Cui, S. W., Cui, X. H., Cui, Y. D., Dai, B. Z., Dai, H. L., Dai, Z. G., Danzengluobu, della Volpe, D., Dong, X. Q., Duan, K. K., Fan, J. H., Fan, Y. Z., Fang, J., Fang, K., Feng, C. F., Feng, L., Feng, S. H., Feng, X. T., Feng, Y. L., Gabici, S., Gao, B., Gao, C. D., Gao, L. Q., Gao, Q., Gao, W., Gao, W. K., Ge, M. M., Geng, L. S., Giacinti, G., Gong, G. H., Gou, Q. B., Gu, M. H., Guo, F. L., Guo, X. L., Guo, Y. Q., Guo, Y. Y., Han, Y. A., He, H. H., He, H. N., He, J. Y., He, X. B., He, Y., Heller, M., Hor, Y. K., Hou, B. W., Hou, C., Hou, X., Hu, H. B., Hu, Q., Hu, S. C., Huang, D. H., Huang, T. Q., Huang, W. J., Huang, X. T., Huang, X. Y., Huang, Y., Huang, Z. C., Ji, X. L., Jia, H. Y., Jia, K., Jiang, K., Jiang, X. W., Jiang, Z. J., Jin, M., Kang, M. M., Ke, T., Kuleshov, D., Kurinov, K., Li, B. B., Li, Cheng, Li, Cong, Li, D., Li, F., Li, H. B., Li, H. C., Li, H. Y., Li, J., Li, Jian, Li, Jie, Li, K., Li, W. L., Li, X. R., Li, Xin, Li, Y. Z., Li, Zhe, Li, Zhuo, Liang, E. W., Liang, Y. F., Lin, S. J., Liu, B., Liu, C., Liu, D., Liu, H., Liu, H. D., Liu, J., Liu, J. L., Liu, J. Y., Liu, M. Y., Liu, R. Y., Liu, S. M., Liu, W., Liu, Y., Liu, Y. N., Lu, R., Luo, Q., Lv, H. K., Ma, B. Q., Ma, L. L., Ma, X. H., Mao, J. R., Min, Z., Mitthumsiri, W., Mu, H. J., Nan, Y. C., Neronov, A., Ou, Z. W., Pang, B. Y., Pattarakijwanich, P., Pei, Z. Y., Qi, M. Y., Qi, Y. Q., Qiao, B. Q., Qin, J. J., Ruffolo, D., Sáiz, A., Semikoz, D., Shao, C. Y., Shao, L., Shchegolev, O., Sheng, X. D., Shu, F. W., Song, H. C., Stenkin, Yu. V., Stepanov, V., Su, Y., Sun, Q. N., Sun, X. N., Sun, Z. B., Tam, P. H. T., Tang, Q. W., Tang, Z. B., Tian, W. W., Wang, C., Wang, C. B., Wang, G. W., Wang, H. G., Wang, H. H., Wang, J. C., Wang, K., Wang, L. P., Wang, L. Y., Wang, P. H., Wang, R., Wang, W., Wang, X. G., Wang, X. Y., Wang, Y., Wang, Y. D., Wang, Y. J., Wang, Z. H., Wang, Z. X., Wang, Zhen, Wang, Zheng, Wei, D. M., Wei, J. J., Wei, Y. J., Wen, T., Wu, C. Y., Wu, H. R., Wu, S., Wu, X. F., Wu, Y. S., Xi, S. Q., Xia, J., Xia, J. J., Xiang, G. M., Xiao, D. X., Xiao, G., Xin, G. G., Xin, Y. L., Xing, Y., Xiong, Z., Xu, D. L., Xu, R. F., Xu, R. X., Xu, W. L., Xue, L., Yan, D. H., Yan, J. Z., Yan, T., Yang, C. W., Yang, F., Yang, F. F., Yang, H. W., Yang, J. Y., Yang, L. L., Yang, M. J., Yang, R. Z., Yang, S. B., Yao, Y. H., Yao, Z. G., Ye, Y. M., Yin, L. Q., Yin, N., You, X. H., You, Z. Y., Yu, Y. H., Yuan, Q., Yue, H., Zeng, H. D., Zeng, T. X., Zeng, W., Zha, M., Zhang, B. B., Zhang, F., Zhang, H. M., Zhang, H. Y., Zhang, J. L., Zhang, L. X., Zhang, Li, Zhang, P. F., Zhang, P. P., Zhang, R., Zhang, S. B., Zhang, S. R., Zhang, S. S., Zhang, X., Zhang, X. P., Zhang, Y. F., Zhang, Yi, Zhang, Yong, Zhao, B., Zhao, J., Zhao, L., Zhao, L. Z., Zhao, S. P., Zheng, F., Zhou, B., Zhou, H., Zhou, J. N., Zhou, M., Zhou, P., Zhou, R., Zhou, X. X., Zhu, C. G., Zhu, F. R., Zhu, H., Zhu, K. J., and Zuo., X.
Subjects: Astrophysics - High Energy Astrophysical Phenomena, High Energy Physics - Phenomenology
Abstract: We present the first catalog of very-high energy and ultra-high energy gamma-ray sources detected by the Large High Altitude Air Shower Observatory (LHAASO). The catalog was compiled using 508 days of data collected by the Water Cherenkov Detector Array (WCDA) from March 2021 to September 2022 and 933 days of data recorded by the Kilometer Squared Array (KM2A) from January 2020 to September 2022. This catalog represents the main result from the most sensitive large coverage gamma-ray survey of the sky above 1 TeV, covering declination from $-$20$^{\circ}$ to 80$^{\circ}$. In total, the catalog contains 90 sources with an extended size smaller than $2^\circ$ and a significance of detection at $> 5\sigma$. Based on our source association criteria, 32 new TeV sources are proposed in this study. Among the 90 sources, 43 sources are detected with ultra-high energy ($E > 100$ TeV) emission at $> 4\sigma$ significance level. We provide the position, extension, and spectral characteristics of all the sources in this catalog., Comment: 40 pages, 13 figures, 4 tables
Published: 2023
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27. Measurement of ultra-high-energy diffuse gamma-ray emission of the Galactic plane from 10 TeV to 1 PeV with LHAASO-KM2A

Author: Cao, Zhen, Aharonian, F., An, Q., Axikegu, Bai, Y. X., Bao, Y. W., Bastieri, D., Bi, X. J., Bi, Y. J., Cai, J. T., Cao, Q., Cao, W. Y., Cao, Zhe, Chang, J., Chang, J. F., Chen, A. M., Chen, E. S., Chen, Liang, Chen, Lin, Chen, Long, Chen, M. J., Chen, M. L., Chen, Q. H., Chen, S. H., Chen, S. Z., Chen, T. L., Chen, Y., Cheng, N., Cheng, Y. D., Cui, M. Y., Cui, S. W., Cui, X. H., Cui, Y. D., Dai, B. Z., Dai, H. L., Dai, Z. G., Danzengluobu, della Volpe, D., Dong, X. Q., Duan, K. K., Fan, J. H., Fan, Y. Z., Fang, J., Fang, K., Feng, C. F., Feng, L., Feng, S. H., Feng, X. T., Feng, Y. L., Gabici, S., Gao, B., Gao, C. D., Gao, L. Q., Gao, Q., Gao, W., Gao, W. K., Ge, M. M., Geng, L. S., Giacinti, G., Gong, G. H., Gou, Q. B., Gu, M. H., Guo, F. L., Guo, X. L., Guo, Y. Q., Guo, Y. Y., Han, Y. A., He, H. H., He, H. N., He, J. Y., He, X. B., He, Y., Heller, M., Hor, Y. K., Hou, B. W., Hou, C., Hou, X., Hu, H. B., Hu, Q., Hu, S. C., Huang, D. H., Huang, T. Q., Huang, W. J., Huang, X. T., Huang, X. Y., Huang, Y., Huang, Z. C., Ji, X. L., Jia, H. Y., Jia, K., Jiang, K., Jiang, X. W., Jiang, Z. J., Jin, M., Kang, M. M., Ke, T., Kuleshov, D., Kurinov, K., Li, B. B., Li, Cheng, Li, Cong, Li, D., Li, F., Li, H. B., Li, H. C., Li, H. Y., Li, J., Li, Jian, Li, Jie, Li, K., Li, W. L., Li, X. R., Li, Xin, Li, Y. Z., Li, Zhe, Li, Zhuo, Liang, E. W., Liang, Y. F., Lin, S. J., Liu, B., Liu, C., Liu, D., Liu, H., Liu, H. D., Liu, J., Liu, J. L., Liu, J. Y., Liu, M. Y., Liu, R. Y., Liu, S. M., Liu, W., Liu, Y., Liu, Y. N., Lu, R., Luo, Q., Lv, H. K., Ma, B. Q., Ma, L. L., Ma, X. H., Mao, J. R., Min, Z., Mitthumsiri, W., Mu, H. J., Nan, Y. C., Neronov, A., Ou, Z. W., Pang, B. Y., Pattarakijwanich, P., Pei, Z. Y., Qi, M. Y., Qi, Y. Q., Qiao, B. Q., Qin, J. J., Ruffolo, D., Saiz, A., Semikoz, D., Shao, C. Y., Shao, L., Shchegolev, O., Sheng, X. D., Shu, F. W., Song, H. C., Stenkin, Yu. V., Stepanov, V., Su, Y., Sun, Q. N., Sun, X. N., Sun, Z. B., Tam, P. H. T., Tang, Q. W., Tang, Z. B., Tian, W. W., Wang, C., Wang, C. B., Wang, G. W., Wang, H. G., Wang, H. H., Wang, J. C., Wang, K., Wang, L. P., Wang, L. Y., Wang, P. H., Wang, R., Wang, W., Wang, X. G., Wang, X. Y., Wang, Y., Wang, Y. D., Wang, Y. J., Wang, Z. H., Wang, Z. X., Wang, Zhen, Wang, Zheng, Wei, D. M., Wei, J. J., Wei, Y. J., Wen, T., Wu, C. Y., Wu, H. R., Wu, S., Wu, X. F., Wu, Y. S., Xi, S. Q., Xia, J., Xia, J. J., Xiang, G. M., Xiao, D. X., Xiao, G., Xin, G. G., Xin, Y. L., Xing, Y., Xiong, Z., Xu, D. L., Xu, R. F., Xu, R. X., Xu, W. L., Xue, L., Yan, D. H., Yan, J. Z., Yan, T., Yang, C. W., Yang, F., Yang, F. F., Yang, H. W., Yang, J. Y., Yang, L. L., Yang, M. J., Yang, R. Z., Yang, S. B., Yao, Y. H., Yao, Z. G., Ye, Y. M., Yin, L. Q., Yin, N., You, X. H., You, Z. Y., Yu, Y. H., Yuan, Q., Yue, H., Zeng, H. D., Zeng, T. X., Zeng, W., Zha, M., Zhang, B. B., Zhang, F., Zhang, H. M., Zhang, H. Y., Zhang, J. L., Zhang, L. X., Zhang, Li, Zhang, P. F., Zhang, P. P., Zhang, R., Zhang, S. B., Zhang, S. R., Zhang, S. S., Zhang, X., Zhang, X. P., Zhang, Y. F., Zhang, Yi, Zhang, Yong, Zhao, B., Zhao, J., Zhao, L., Zhao, L. Z., Zhao, S. P., Zheng, F., Zhou, B., Zhou, H., Zhou, J. N., Zhou, M., Zhou, P., Zhou, R., Zhou, X. X., Zhu, C. G., Zhu, F. R., Zhu, H., Zhu, K. J., and Zuo, X.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: The diffuse Galactic $\gamma$-ray emission, mainly produced via interactions between cosmic rays and the interstellar medium and/or radiation field, is a very important probe of the distribution, propagation, and interaction of cosmic rays in the Milky Way. In this work we report the measurements of diffuse $\gamma$-rays from the Galactic plane between 10 TeV and 1 PeV energies, with the square kilometer array of the Large High Altitude Air Shower Observatory (LHAASO). Diffuse emissions from the inner ($15^{\circ}10$~TeV). The energy spectrum in the inner Galaxy regions can be described by a power-law function with an index of $-2.99\pm0.04$, which is different from the curved spectrum as expected from hadronic interactions between locally measured cosmic rays and the line-of-sight integrated gas content. Furthermore, the measured flux is higher by a factor of $\sim3$ than the prediction. A similar spectrum with an index of $-2.99\pm0.07$ is found in the outer Galaxy region, and the absolute flux for $10\lesssim E\lesssim60$ TeV is again higher than the prediction for hadronic cosmic ray interactions. The latitude distributions of the diffuse emission are consistent with the gas distribution, while the longitude distributions show clear deviation from the gas distribution. The LHAASO measurements imply that either additional emission sources exist or cosmic ray intensities have spatial variations., Comment: 12 pages, 8 figures, 5 tables; accepted for publication in Physical Review Letters; source mask file provided as ancillary file
Published: 2023
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28. The CompactLight Design Study

Author: D’Auria, G., Adli, E., Aicheler, M., Aksoy, A., Alesini, D., Apsimon, R., Arnsberg, J., Auchettl, R., Bainbridge, A., Balazs, K., Bantekas, D., Bedolla, J., Behtouei, M., Bellaveglia, M., vd Berg, M., Bernhard, A., Bignami, A., Breitenbach, M., Breukers, M., Burt, G., Cai, J., Calvi, M., Cardelli, F., Carpanese, M., Cortes, H. M. Castaneda, Castilla, A., Cianchi, A., Clarke, J., Cowie, L., Croia, M., Cross, A., Danailov, M., Dattoli, G., Deleval, S., Mitri, S. Di, Diomede, M., Dowd, R., Dunning, D., Easton, J., Fang, W., Fatehi, S., Faus-Golfe, A., Ferianis, M., Ferrario, M., Ficcadenti, L., Gallo, A., Gazis, E., Gazis, N., Geometrante, R., Gethmann, J., Gioppo, R., Giribono, A., González-Iglesias, D., Goryashko, V., Grohmann, S., Gu, Q., Han, Y., Hinton, A., Hobi, A., Hoekstra, R., Huang, X., Jacewicz, M., Jones, J., Kaertner, F., Karagiannaki, A., Kokole, M., Kotitsa, R., Kotsopoulos, D., Krasch, B., Latina, A., Lepercq, P., Liu, X., Lucas, T. G., Luiten, O. J., Maheshwari, M., Mahnic, J., Mak, A., Marcos, J., Marin, E., Marinov, K., Martínez, B. G., Mercier, B., Migliorati, M., Milharcic, T., Mostacci, A., Mu noz, R., Musat, V., Mutsaers, P. H. A., Nergiz, Z., Nguyen, F., Nix, L., Palumbo, L., Parodi, M., Pavlica, R., Pellegrino, L., Pereira, D. E., Perez, F., Petralia, A., Piersanti, L., Pockar, J., Pramatari, K., Priem, H., Primozic, U., Rassool, R., Reiche, S., Revilak, P., Richter, S. C., Rochow, R., Rossi, C., Salén, P., Schmidt, T., Schoerling, D., Schulte, D., Scifo, J., Sheehy, S., Shepherd, B., Spataro, B., Stapnes, S., Stragier, X. F. D., Syratchev, I., Tabacco, C., Tan, J., Tanke, E., Taylor, G., Telahi, I., Thompson, N., Trachanas, E., Tzanetou, K. S., Vaccarezza, C., Vainola, J., Vannozzi, A., Volpi, M., Wang, C., Williams, P., Wu, X., Wuensch, W., Yap, J., Zangrando, M., Zhang, K., Zhang, L., Zhao, Y., Zhao, Z., and Zhu, D.
Published: 2024
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29. Integrating Network Pharmacology and Experimental Validation to Investigate the Mechanism of Qushi Huatan Decoction Against Coronary Heart Disease

Author: Yin C, Lan T, Wu Y, Cai J, Li H, Kuang X, Jiao L, Ou X, Yang H, Liu B, and Lu W
Subjects: network pharmacology, molecular docking, qushi huatan decoction, coronary heart disease, Therapeutics. Pharmacology, RM1-950
Abstract: Chunxia Yin,1,&ast; Taohua Lan,2,3,&ast; Yunshan Wu,4,5 Jing Cai,1 Haoxiang Li,1 Xiaolan Kuang,1 Lin Jiao,1 Xiaomin Ou,1 Hua Yang,2 Bo Liu,3– 5 Weihui Lu2,6,7 1The Second Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, People’s Republic of China; 2Department of Cardiology, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, People’s Republic of China; 3State Key Laboratory of Dampness Syndrome of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, People’s Republic of China; 4Guangzhou Key Laboratory of Chirality Research on Active Components of Traditional Chinese Medicine, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, People’s Republic of China; 5Guangdong Provincial Key Laboratory of Clinical Research on Traditional Chinese Medicine Syndrome, Guangzhou University of Chinese Medicine, Guangzhou, People’s Republic of China; 6State Key Laboratory of Traditional Chinese Medicine Syndrome, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou University of Chinese Medicine, Guangzhou, People’s Republic of China; 7Chinese Medicine Guangdong Laboratory, Guangdong Hengqin, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Weihui Lu, Bo Liu, The Second Clinical Medical College, Guangzhou University of Chinese Medicine, Email weihui.lu@gzucm.edu.cn; doctliu@gzucm.edu.cnPurpose: This study was designed to evaluate the effect and mechanism of the Qushi Huatan (QSHT) decoction against coronary heart disease (CHD) through network pharmacology and experimental verification.Methods: In the present study, the active ingredients of the QSHT decoction were identified by ultra performance liquid chromatography/tandem mass spectrometry (UPLC/MS), then the potential ingredients and coronary heart disease targets were predicted using the SwissTarget Prediction database and the database of Genecards and OMIM database, respectively. A herb-compound-target network was constructed using Cytoscape. GO and KEGG enrichment analysis were performed using the ClusterProfiler data package of R software. Molecular docking was used to predict the core targets of QSHT against CHD. In addition, we used a myocardial infarction (MI) and high-fat diet ApoE−/− mice model to investigate the cardioprotective effects of QSHT. Western blotting and immunochemistry were used to verify the core targets and the signaling pathway.Results: A total of 68 active ingredients were found in the QSHT decoction. Network pharmacology indicated 28 targets and 147 signal pathways, including AKT1, HIF-1α, GSK-3β, TLR4 and NF-κB, those key targets were also verified by molecular docking. The results of GO and KEGG enrichment analysis showed that the targets of QSHT against CHD were largely associated with inflammatory and oxidative stress, and AKT/HIF-1α and TLR4/NF-κB pathways might be key functional pathways. In vivo, QSHT significantly improved cardiac function and attenuated fibrosis and inflammation. Furthermore, QSHT could significantly inhibit the expression of HIF-1α, TLR4, phosphorylation of AKT1, GSK-3β and NF-κB after MI in ApoE−/− mice.Conclusion: Based on network pharmacology, molecular docking and experimental verification, this study demonstrated that QSHT could improve cardiac function and attenuate cardiac fibrosis by regulating TLR4/NF-κB and AKT/HIF-1α signaling pathway in post- MI and high-fat diet ApoE−/− mice.Keywords: network pharmacology, molecular docking, Qushi Huatan decoction, coronary heart disease
Published: 2024

30. A Prediction Nomogram of Severe Obstructive Sleep Apnea in Patients with Obesity Based on the Liver Stiffness and Abdominal Visceral Adipose Tissue Quantification

Author: Zhao A, Hao B, Liu S, Qiu X, Ming X, Yang X, Cai J, Li Z, and Chen X
Subjects: obstructive sleep apnea, obesity, liver stiffness measurement, visceral adipose tissue, nomogram, Psychiatry, RC435-571, Neurophysiology and neuropsychology, QP351-495
Abstract: Anbang Zhao,1,2,&ast; Bin Hao,1,2,&ast; Simin Liu,3,&ast; Xiaoyu Qiu,1,2,&ast; Xiaoping Ming,1,2 Xiuping Yang,1,2 Jie Cai,1,2 Zhen Li,4,5 Xiong Chen1,2 1Department of Otorhinolaryngology, Head and Neck Surgery, Zhongnan Hospital of Wuhan University, Wuhan, People’s Republic of China; 2Sleep Medicine Center, Zhongnan Hospital of Wuhan University, Wuhan, People’s Republic of China; 3Department of Neurosurgery, Union Hospital Tongji Medical College Huazhong University of Science and Technology, Wuhan, People’s Republic of China; 4Department of Hepatobiliary and Pancreatic Surgery, Zhongnan Hospital of Wuhan University, Wuhan, People’s Republic of China; 5Bariatric and Metabolic Disease Surgery Center, Zhongnan Hospital of Wuhan University, Wuhan, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Xiong Chen, Department of Otorhinolaryngology, Head and Neck Surgery, Zhongnan Hospital of Wuhan University, Wuhan, People’s Republic of China, Email zn_chenxiong@whu.edu.com Zhen Li, Department of Hepatobiliary and Pancreatic Surgery, Zhongnan Hospital of Wuhan University, Wuhan, People’s Republic of China, Email kamel_li@163.comPurpose: The diagnosis of severe OSA still relies on polysomnography, which causes a strong sense of restraint in patients with obesity. However, better prediction tools for severe OSA applicable to patients with obesity have not been developed.Patients and Methods: Relevant clinical data of 1008 patients with OSA who underwent bariatric surgery in our hospital were collected retrospectively. Patients were divided into training and test cohorts by machine learning. Univariate and multivariate logistic regression analysis was used to screen associations, including liver stiff measurement (LSM) and abdominal visceral tissue (aVAT), and to construct a severe OSA risk prediction nomogram. Then, we evaluated the effectiveness of our model and compared our model with the traditional Epworth Sleepiness Scale (ESS) model. Finally, our associations were used to explore the correlation with other indicators of OSA severity.Results: Our study revealed that age, biological sex, BMI, LSM, aVAT, and LDL were independent risk factors for severe OSA in patients with obesity. A severe OSA risk prediction nomogram constructed by six indicators possessed high AUC (0.845), accuracy (77.6%), and relatively balanced specificity and sensitivity (72.4%, 82.8%). The Hosmer-Lemeshow test (P=0.296, 0.785), calibration curves, and DCA of the training and test cohorts suggested better calibration and more net clinical benefit. Compared with the traditional ESS model, our model had higher AUC (0.829 vs 0.545), sensitivity (78.9% vs 12.2%), PPV (77.9% vs 53.3%), and accuracy (75.4% vs 55.2%). In addition, the associations in our model were independently correlated with other indicators reflecting OSA severity.Conclusion: We provided a simple, cheap, and non-invasive nomogram of severe OSA risk prediction for patients with obesity, which would be helpful for preventing further complications associated with severe OSA.Plain Language Summary: Question: Can we predict severe OSA in patients with obesity by their metabolic complications through some non-invasive examinations?Findings: Compared with traditional questionnaires, we developed and validated a new prediction model, including liver stiffness measurement and abdominal visceral adipose tissue, to screen severe OSA in bariatric surgery candidates through non-invasive examinations, which may contribute to perioperative safety and ultimate weight loss outcomes.Meaning: For patients with obesity who are in hospital because of metabolic disorders, it is necessary for them to be screened for possible severe OSA according to our new prediction nomogram, which is helpful for preventing further complications and perioperative risk associated with severe OSA.Keywords: obstructive sleep apnea, obesity, liver stiffness measurement, visceral adipose tissue, nomogram
Published: 2024

31. Psychometric Validation of the Dampness Syndrome Scale of Chinese Medicine for People in China

Author: Lu T, Fan M, Cai J, Liu A, Xie Q, Zhou X, Chang B, Yin L, Jiang G, Sun B, Tang W, and Wu D
Subjects: dampness syndrome, dampness syndrome scale of chinese medicine, reliability, validity, Medicine (General), R5-920
Abstract: Taoying Lu,1– 3 Mingyue Fan,1 Jianxiong Cai,4 Aolin Liu,1 Qianwen Xie,2,3 Xiaowen Zhou,1 Bei Chang,1 Lingjia Yin,2,3,5 Guli Jiang,6 Bin Sun,7 Wenyi Tang,7 Darong Wu4 1The Second Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, People’s Republic of China; 2Outcome Assessment Research Team in Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, People’s Republic of China; 3Outcome Assessment Research Team in Chinese Medicine, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, People’s Republic of China; 4State Key Laboratory of Dampness Syndrome of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, People’s Republic of China; 5Department of Global Public Health, Karolinska Institutet, Stockholm, Sweden; 6Department of Scientific Research, Guangzhou Cadre Health Management Center, Guangzhou, People’s Republic of China; 7Department of Traditional Chinese Medicine, Guangzhou Cadre Health Management Center, Guangzhou, People’s Republic of ChinaCorrespondence: Darong Wu, State Key Laboratory of Dampness Syndrome of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, 111 Dade Road, Guangzhou, 510120, People’s Republic of China, Tel +86 13808869436, Email darongwu@gzucm.edu.cnBackground: Dampness syndrome is a basic syndrome of many major and chronic diseases. Early screening and identification of dampness syndrome are important. The Dampness Syndrome Scale of Chinese Medicine (DSSCM) was developed to measure the occurrence and severity of dampness syndrome. The measurement characteristics of the DSSCM should be further validated. The objective of this study is to evaluate the reliability and validity of the DSSCM based on a cross-sectional survey.Methods: The development of DSSCM was based on the traditional Chinese medicine (TCM) theory, expert consensus and clinical investigations. Participants were recruited from Guangdong Provincial Hospital of Chinese Medicine and Guangzhou Cadre Health Management Center in Guangdong Province, China. All participants completed the baseline questionnaire and DSSCM. The reliability of the DSSCM was evaluated by internal consistency reliability and test–retest reliability. The factor structure was tested with confirmatory factor analysis (CFA), while discriminant validity was evaluated by calculating the difference between the total score of the DSSCM and its four dimensions for participants with or without dampness syndrome. We used a clinician’s diagnosis of dampness syndrome as a criterion to evaluate the criterion validity. Multivariate regression analysis was used to analyze the associations among gender, age, height, and weight and dampness syndrome.Results: A total of 880 participants were enrolled for psychometric validation and 869 participants were included in the final analyses after exclusion. The mean age of the participants was 42.62± 12.86 years. The Cronbach’s alpha coefficients for the four dimensions of the DSSCM were 0.809, 0.891, 0.816 and 0.780. The ICCs for the four dimensions of the DSSCM were 0.806, 0.674, 0.780 and 0.760. For validity, the results showed that the DSSCM had good content validity, discriminant validity and criterion validity. For the CFA, the root mean square error of approximation (RMSEA) was 0.091; the standardized root mean square residual (SRMR) was 0.074; the comparative fit index (CFI) was 0.766; and the Tucker–Lewis index (TLI) was 0.745. Weight was found to be associated with the total scores of the DSSCM and its three dimensions.Conclusion: The DSSCM has good reliability and validity to support its use as a tool for assessing the severity of dampness syndrome. This study provides empirical evidence for future updates to the DSSCM.Keywords: dampness syndrome, dampness syndrome scale of Chinese medicine, reliability, validity
Published: 2024

32. Comprehensive Analysis of scRNA-Seq and Bulk RNA-Seq Reveals Transcriptional Signatures of Macrophages in Intrahepatic Cholestasis of Pregnancy

Author: Tang M, Xiong L, Cai J, Gong X, Fan L, Zhou X, Xing S, and Yang X
Subjects: intrahepatic cholestasis of pregnancy, scrna-seq, bulk rna-seq, immune gene, macrophage, Pathology, RB1-214, Therapeutics. Pharmacology, RM1-950
Abstract: Mi Tang,1,2,&ast; Liling Xiong,3,&ast; Jianghui Cai,2,4,&ast; Xuejia Gong,2 Li Fan,1 Xiaoyu Zhou,1 Shasha Xing,1 Xiao Yang3 1Chengdu Women’s and Children’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, People’s Republic of China; 2School of medicine, University of Electronic Science and Technology of China, Chengdu, People’s Republic of China; 3Obstetrics Department, Chengdu Women’s and Children’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, People’s Republic of China; 4Department of Pharmacy, Chengdu Women’s and Children’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Shasha Xing; Xiao Yang, Chengdu Women’s and Children’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, People’s Republic of China, Email xingshasha1230@126.com; wcchgcptg@163.comPurpose: Intrahepatic cholestasis of pregnancy (ICP) is a disorder that characterized by maternal pruritus, abnormal liver function, and an elevation in total bile acid concentrations during pregnancy. Immune factors have been recognized as playing a vital role in the mechanism of ICP. However, the underlying mechanisms regulating dysfunctional immune cells and immune genes remain to be fully elucidated.Patients and Methods: Single-cell RNA sequencing and bulk RNA sequencing data of the placenta were downloaded from the SRA database. The AUCell package, Monocle package and SCENIC package were utilized to explored immune cell activity, cell trajectory and transcription factor, respectively. GO, KEGG, and GSEA were employed to explore potential biological mechanisms. Cell-cell communications were further investigated using the CellChat package. RT-PCR, and Western blot were used to verify the gene expression in placenta.Results: In placenta cells, macrophages were found to be significantly increased in ICP. Additionally, macrophages exhibited the highest immune gene score and were divided into four subclusters (MF1-4). Our analysis revealed significant elevations in MF2, associated with LPS response and antigen presentation, and MF4, associated with TNF and cytokine production. MF3 displayed an anti-inflammatory phenotype. MF1, closely related to ribosomes and proteins, exhibited a sharp decrease. Although ICP maintained an anti-inflammatory state, macrophage trajectories showed a gradual progression toward inflammation. Subsequently, we confirmed that cytokine- and chemokine-related signaling pathways were emphasized in macrophages. Within the CXCL signaling pathway, the increased expression of CXCL1 in macrophages can interact with CXCR2 in neutrophils, potentially inducing macrophage infiltration, stimulating neutrophil chemotaxis, and leading to an inflammatory response and cellular damage.Conclusion: In conclusion, we firstly revealed the transcriptional signatures of macrophages in ICP and discovered a tendency toward an inflammatory state. This study also provides new evidence that the CXCL1-CXCR2 axis may play an important role in the pathogenesis of ICP.Keywords: intrahepatic cholestasis of pregnancy, scRNA-Seq, bulk RNA-Seq, immune gene, macrophage
Published: 2024

33. New HCC Subtypes Based on CD8 Tex-Related lncRNA Signature Could Predict Prognosis, Immunological and Drug Sensitivity Characteristics of Hepatocellular Carcinoma

Author: Ge J, Tao M, Zhang G, Cai J, Li D, and Tao L
Subjects: hepatocellular carcinoma, lncrna, t cell exhaustion, single-cell rna-seq, machine learning, prognostic signature, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract: Jiachen Ge,1,&ast; Ming Tao,2,&ast; Gaolei Zhang,1 Jianping Cai,1 Deyu Li,1 Lianyuan Tao1 1Department of Hepatobiliary Surgery, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, Zhengzhou, People’s Republic of China; 2Department of General Surgery, Peking University Third Hospital, Beijing, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Deyu Li; Lianyuan Tao, Henan Provincial People’s Hospital, Zhengzhou University People’s Hospital, No. 7 Weiwu Road, Zhengzhou City, Henan Province, People’s Republic of China, Email lideyu19@hotmail.com; tly2007tly@hotmail.comPurpose: Hepatocellular carcinoma has become one of the severe diseases threatening human health. T cell exhaustion is deemed as a reason for immunotherapy resistance. However, little is known about the roles of CD8 Tex-related lncRNAs in HCC.Materials and Methods: We processed single-cell RNA sequencing to identify CD8 Tex-related genes. CD8 Tex-related lncRNAs were identified based on their correlations with mRNAs. Unsupervised clustering approach was used to identify molecular clusters of CD8 Tex-related lncRNAs. Differences in prognosis and immune infiltration between the clusters were explored. Machine learning algorithms were used to construct a prognostic signature. Samples were classified as low- and high-risk groups based on their risk scores. We identified prognosis-related lncRNAs and constructed a ceRNA network. In vitro experiments were conducted to investigate the impacts of CD8 Tex-related lncRNAs on proliferation and apoptosis of HCC cells.Results: We clarified cell types within two HCC single-cell datasets. We identified specific markers of CD8 Tex cells and analyzed their potential functions. Twenty-eight lncRNAs were identified as CD8 Tex-related. Based on CD8 Tex-related lncRNAs, samples were categorized into two distinct clusters, which exhibited significant differences in survival rates and immune infiltration. Ninety-six algorithm combinations were employed to establish a prognostic signature. RSF emerged as the one with the highest C-index. Patients in high- and low-risk groups exhibited marked differences in prognosis, enriched pathways, mutations and drug sensitivities. MCM3AP-AS1, MAPKAPK5-AS1 and PART1 were regarded as prognosis-related lncRNAs. A ceRNA network was constructed based on CD8 Tex-related lncRNAs and mRNAs. Experiments on cell lines and organoids indicated that downregulation of MCM3AP-AS1, MAPKAPK5-AS1 and PART1 suppressed cell proliferation and induced apoptosis.Conclusion: CD8 Tex-related lncRNAs played crucial roles in HCC progression. Our findings provided new insights into the regulatory mechanisms of CD8 Tex-related lncRNAs in HCC.Keywords: hepatocellular carcinoma, lncRNA, T cell exhaustion, single-cell RNA-seq, machine learning, prognostic signature
Published: 2024

34. Knowledge, Attitudes and Practices Among Anesthesia and Thoracic Surgery Medical Staff Toward Ai-PCA

Author: Song Z, Cai J, Zhou Y, Jiang Y, Huang S, Gu L, and Tan J
Subjects: knowledge, attitudes, practices, ai-pca, cross-sectional study, Medicine (General), R5-920
Abstract: Zhenghuan Song,1,&ast; Jiaqin Cai,2,&ast; Yihu Zhou,3 Yueyi Jiang,3 Shiyi Huang,2 Lianbing Gu,1,2 Jing Tan1,2 1Department of Anesthesiology, Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research & The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, 21009, People’s Republic of China; 2Xuzhou Medical University, Xuzhou, 21009, People’s Republic of China; 3Nanjing Medical University, Nanjing, 21009, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Jing Tan; Lianbing Gu, Department of Anesthesiology, Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research & The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, 21009, People’s Republic of China, Tel +86-13813967939; +86-13951947684, Email tanjing@njmu.edu.cn; 13951947684@163.comPurpose: Artificial intelligence (AI) is increasingly influencing various medical fields, including anesthesiology. The Introduction of artificial intelligent patient-controlled analgesia (Ai-PCA) has been seen as a significant advancement in pain management. However, the adoption and practical application of Ai-PCA by medical staff, particularly in anesthesia and thoracic surgery, have not been extensively studied. This study aimed to investigate the knowledge, attitudes and practices (KAP) among anesthesia and thoracic surgery medical staff toward artificial intelligent patient-controlled analgesia (Ai-PCA).Participants and Methods: This web-based cross-sectional study was conducted between November 1, 2023 and November 15, 2023 at Jiangsu Cancer Hospital. A self-designed questionnaire was developed to collect demographic information of anesthesia and thoracic surgery medical staff, and to assess their knowledge, attitudes and practices toward Ai-PCA.Results: A total of 519 valid questionnaires were collected. Among the participants, 278 (53.56%) were female, 497 (95.76%) were employed in the field of anesthesiology, and 188 (36.22%) had participated in Ai-PCA training. The mean knowledge, attitude, and practice scores were 7.8± 1.75 (possible range: 0– 10), 37.43± 4.16 (possible range: 9– 45), and 28.38± 9.27 (possible range: 9– 45), respectively.Conclusion: The findings revealed that anesthesia and thoracic surgery medical staff have sufficient knowledge, active attitudes, but poor practices toward the Ai-PCA. Comprehensive training programs are needed to improve anesthesia and thoracic surgery medical staff’s practices in this area.Keywords: knowledge, attitudes, practices, Ai-PCA, cross-sectional study
Published: 2024

35. Nomogram Model for Cardiac Surgery-Associated Acute Kidney Injury Based on Clinical Characteristics Combined with Plasma suPAR

Author: Zhu L, Cai J, Fang J, Ran L, Chang H, Zhang H, Zeng J, Yang Q, Fu C, Li Q, Pan Q, and Zhao H
Subjects: nomogram, acute kidney injury, prediction model, risk factors, cardiac surgery, Medicine (General), R5-920
Abstract: Longyin Zhu,&ast; Juan Cai,&ast; Jia Fang, Lingyu Ran, Huan Chang, Huhai Zhang, Jiamin Zeng, Qin Yang, Chunxiao Fu, Qingping Li, Qianguang Pan, Hongwen Zhao Department of Nephrology, the First Hospital Affiliated to Army Military Medical University (Southwest Hospital), Chongqing, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Hongwen Zhao; Qianguang Pan, Email zhaohw212@126.com; panqianguang@163.comObjective: Analyze risk factors for cardiac surgery-associated acute kidney injury (CSA-AKI) in adults and establish a nomogram model for CSA-AKI based on plasma soluble urokinase-type plasminogen activator receptor (suPAR) and clinical characteristics.Methods: In a study of 170 patients undergoing cardiac surgery with cardiopulmonary bypass, enzyme-linked immunosorbent assay (ELISA) measured plasma suPAR levels. Multivariable logistic regression analysis identified risk factors associated with CSA-AKI. Subsequently, the CSA-AKI nomogram model was developed using R software. Predictive performance was evaluated using a receiver operating characteristic (ROC) curve and the area under the curve (AUC). Internal validation was performed through the Bootstrap method with 1000 repeated samples. Additionally, decision curve analysis (DCA) assessed the clinical applicability of the model.Results: Multivariable logistic regression analysis revealed that being male, age ≥ 50 years, operation time ≥ 290 minutes, postoperative plasma suPAR at 2 hours, and preoperative left ventricular ejection fraction (LVEF) were independent risk factors for CSA-AKI. Employing these variables as predictive factors, a nomogram model was constructed, an ROC curve was generated, and the AUC was computed as 0.817 (95% CI 0.726– 0.907). The calibration curve indicated the accuracy of the model, and the results of DCA demonstrated that the model could benefit the majority of patients.Conclusion: Being male, age ≥ 50 years, operation time ≥ 290 minutes, low preoperative LVEF, and elevated plasma suPAR at 2 hours are independent risk factors for CSA-AKI. The nomogram model established based on these risk factors has high accuracy and clinical value, serving as a predictive tool for assessing the risk of CSA-AKI.Keywords: nomogram, acute kidney injury, prediction model, risk factors, cardiac surgery
Published: 2024

36. Flavonoids and Gastric Cancer Therapy: From Signaling Pathway to Therapeutic Significance

Author: Cai J, Tan X, Hu Q, Pan H, Zhao M, Guo C, Zeng J, Ma X, and Zhao Y
Subjects: flavonoid, gastric cancer, apoptosis, angiogenesis, Therapeutics. Pharmacology, RM1-950
Abstract: Jiaying Cai,1,&ast; Xiyue Tan,2,&ast; Qichao Hu,2,&ast; Huafeng Pan,3 Maoyuan Zhao,4 Cui Guo,4 Jinhao Zeng,1,4,5 Xiao Ma,2 Yanling Zhao6 1School of Clinical Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, People’s Republic of China; 2State Key Laboratory of Southwestern Chinese Medicine Resources, School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, People’s Republic of China; 3Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, People’s Republic of China; 4TCM Regulating Metabolic Diseases Key Laboratory of Sichuan Province, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, People’s Republic of China; 5Department of Gastroenterology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, People’s Republic of China; 6Department of Pharmacy, Chinese PLA General Hospital, Beijing, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Jinhao Zeng; Yanling Zhao, Email zengjinhao@cdutcm.edu.cn; zhaoyl2855@126.comAbstract: Gastric cancer (GC) is a prevalent gastrointestinal tumor characterized by high mortality and recurrence rates. Current treatments often have limitations, prompting researchers to explore novel anti-tumor substances and develop new drugs. Flavonoids, natural compounds with diverse biological activities, are gaining increasing attention in this regard. We searched from PubMed, Web of Science, SpringerLink and other databases to find the relevant literature in the last two decades. Using “gastric cancer”, “stomach cancers”, “flavonoid”, “bioflavonoid”, “ 2-Phenyl-Chromene” as keywords, were searched, then analyzed and summarized the mechanism of flavonoids in the treatment of GC. It was revealed that the anti-tumor mechanism of flavonoids involves inhibiting tumor growth, proliferation, invasion, and metastasis, as well as inducing cell death through various processes such as apoptosis, autophagy, ferroptosis, and pyroptosis. Additionally, combining flavonoids with other chemotherapeutic agents like 5-FU and platinum compounds can potentially reduce chemoresistance. Flavonoids have also demonstrated enhanced biological activity when used in combination with other natural products. Consequently, this review proposes innovative perspectives for the development of flavonoids as new anti-GC agents.Keywords: flavonoid, gastric cancer, apoptosis, angiogenesis
Published: 2024

37. Photo-accelerated water dissociation across one-atom-thick electrodes

Author: Cai, J., Griffin, E., Guarochico-Moreira, V., Barry, D., Xin, B., Huang, S., Geim, A. K., Peeters, F. M., and Lozada-Hidalgo, M.
Subjects: Physics - Chemical Physics, Condensed Matter - Mesoscale and Nanoscale Physics
Abstract: Recent experiments demonstrated that interfacial water dissociation (H2O = H+ + OH-) could be accelerated exponentially by an electric field applied to graphene electrodes, a phenomenon related to the Wien effect. Here we report an order-of-magnitude acceleration of the interfacial water dissociation reaction under visible-light illumination. This process is accompanied by spatial separation of protons and hydroxide ions across one-atom-thick graphene and enhanced by strong interfacial electric fields. The found photo-effect is attributed to the combination of graphene's perfect selectivity with respect to protons, which prevents proton-hydroxide recombination, and to proton transport acceleration by the Wien effect, which occurs in synchrony with the water dissociation reaction. Our findings provide fundamental insights into ion dynamics near atomically-thin proton-selective interfaces and suggest that strong interfacial fields can enhance and tune very fast ionic processes, which is of relevance for applications in photo-catalysis and designing reconfigurable materials.
Published: 2022
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38. Deep underground laboratory measurement of $^{13}$C($\alpha$,$n$)$^{16}$O in the Gamow windows of the $s$- and $i$-processes

Author: Gao, B., Jiao, T. Y., Li, Y. T., Chen, H., Lin, W. P., An, Z., Ru, L. H., Zhang, Z. C., Tang, X. D., Wang, X. Y., Zhang, N. T., Fang, X., Xie, D. H., Fan, Y. H., Ma, L., Zhang, X., Bai, F., Wang, P., Fan, Y. X., Liu, G., Huang, H. X., Wu, Q., Zhu, Y. B., Chai, J. L., Li, J. Q., Sun, L. T., Wang, S., Cai, J. W., Li, Y. Z., Su, J., Zhang, H., Li, Z. H., Li, Y. J., Li, E. T., Chen, C., Shen, Y. P., Lian, G., Guo, B., Li, X. Y., Zhang, L. Y., He, J. J., Sheng, Y. D., Chen, Y. J., Wang, L. H., Zhang, L., Cao, F. Q., Nan, W., Nan, W. K., Li, G. X., Song, N., Cui, B. Q., Chen, L. H., Ma, R. G., Yan, S. Q., Liao, J. H., Wang, Y. B., Zeng, S., Nan, D., Fan, Q. W., Qi, N. C., Sun, W. L., Guo, X. Y., Zhang, P., Chen, Y. H., Zhou, Y., Zhou, J. F., He, J. R., Shang, C. S., Li, M. C., Kubono, S., Liu, W. P., deBoer, R. J., Wiescher, M., and Pignatari, M.
Subjects: Nuclear Experiment
Abstract: The $^{13}$C($\alpha$,$n$)$^{16}$O reaction is the main neutron source for the slow-neutron-capture (s-) process in Asymptotic Giant Branch stars and for the intermediate (i-) process. Direct measurements at astrophysical energies in above-ground laboratories are hindered by the extremely small cross sections and vast cosmic-ray induced background. We performed the first consistent direct measurement in the range of $E_{\rm c.m.}=$0.24 MeV to 1.9 MeV using the accelerators at the China Jinping Underground Laboratory (CJPL) and Sichuan University. Our measurement covers almost the entire i-process Gamow window in which the large uncertainty of the previous experiments has been reduced from 60\% down to 15\%, eliminates the large systematic uncertainty in the extrapolation arising from the inconsistency of existing data sets, and provides a more reliable reaction rate for the studies of the s- and i-processes along with the first direct determination of the alpha strength for the near-threshold state.
Published: 2022
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39. $^{197}$Au($\gamma,\,xn;\,x\,=\,1\thicksim9$) Reaction Cross Section Measurements using Laser-Driven Ultra-Intense $\gamma$-Ray Source

Author: Wu, D., Lan, H. Y., Zhang, J. Y., Liu, J. X., Lu, H. G., Lv, J. F., Wu, X. Z., Zhang, H., Cai, J., Ma, Q. Y., Xia, Y. H., Wang, Z. N., Wang, M. Z., Yang, Z. Y., Xu, X. L., Geng, Y. X., Zhao, Y. Y., Lin, C., Ma, W. J., Yu, J. Q., Wang, H. R., Liu, F. L., He, C. Y., Guo, B., Zhu, P., Zhang, G. Q., Wang, N. Y., Ma, Y. G., and Yan, X. Q.
Subjects: Nuclear Experiment, Physics - Plasma Physics
Abstract: We present a new method for the measurements of photonuclear reaction flux-weighted average cross sections and isomeric ratios using a laser-driven bremsstrahlung $\gamma$-ray source. An ultra-bright ultra-fast 60$\,\thicksim\,$250 MeV bremsstrahlung $\gamma$-ray source was established using the 200 TW laser facility in the Compact Laser Plasma Accelerator Laboratory, Peking University, which could cover the energy range from knocking out neutrons to producing pions. Stable quasi-monoenergetic electron beams were generated via laser wakefield acceleration with a charge of 300$\,\thicksim\,$600 pC per shot. The averaged $\gamma$-ray intensities ($\geqslant$8 MeV) were higher than 10$^{8}$ per shot and the instantaneous intensities can reach above 10$^{19}$ s$^{-1}$ with a duration time about 6.7 ps. $^{65}$Cu($\gamma,\,n$)$^{64}$Cu and $^{27}$Al($\gamma,\,x$)$^{24}$Na reactions were used as $\gamma$-ray flux monitors in the experiments. The flux-weighted average cross sections and isomeric ratios of $^{197}$Au($\gamma,\,xn;\,x\,=\,1\thicksim9$) reactions were analyzed through activation measurements. The results showed good agreement with previous works and proved this method to be accurate. The $^{197}$Au($\gamma,\,xn;\,x\,=\,7\thicksim\,9$) reaction cross sections were first achieved with the highest threshold energy of 71.410 MeV. Theoretical cross sections of TALYS 1.9 were calculated to compare with experiment results. This method offered a unique way of gaining insight into photonuclear reaction research, especially for short-lived isomers which extremely lack experimental data.
Published: 2022

40. Wien effect in interfacial water dissociation through proton-permeable graphene electrodes

Author: Cai, J., Griffin, E., Guarochico-Moreira, V., Barry, D., Xin, B., Yagmurcukardes, M., Zhang, S., Geim, A. K., Peeters, F. M., and Lozada-Hidalgo, M.
Subjects: Physics - Chemical Physics, Condensed Matter - Mesoscale and Nanoscale Physics
Abstract: Strong electric fields can accelerate molecular dissociation reactions. The phenomenon known as the Wien effect was previously observed using high-voltage electrolysis cells that produced fields of about 10^7 V m-1, sufficient to accelerate the dissociation of weakly bound molecules (e.g., organics and weak electrolytes). The observation of the Wien effect for the common case of water dissociation (H2O = H+ + OH-) has remained elusive. Here we study the dissociation of interfacial water adjacent to proton-permeable graphene electrodes and observe strong acceleration of the reaction in fields reaching above 10^8 V m-1. The use of graphene electrodes allow measuring the proton currents arising exclusively from the dissociation of interfacial water, while the electric field driving the reaction is monitored through the carrier density induced in graphene by the same field. The observed exponential increase in proton currents is in quantitative agreement with Onsager's theory. Our results also demonstrate that graphene electrodes can be valuable for the investigation of various interfacial phenomena involving proton transport.
Published: 2022
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41. Flux Variations of Cosmic Ray Air Showers Detected by LHAASO-KM2A During a Thunderstorm on 10 June 2021

Author: LHAASO Collaboration, Aharonian, F., An, Q., Axikegu, Bai, L. X., Bai, Y. X., Bao, Y. W., Bastieri, D., Bi, X. J., Bi, Y. J., Cai, J. T., Cao, Zhe, Cao, Zhen, Chang, J., Chang, J. F., Chen, E. S., Chen, Liang, Chen, Long, Chen, M. J., Chen, M. L., Chen, S. H., Chen, S. Z., Chen, T. L., Chen, X. J., Chen, Y., Cheng, H. L., Cheng, N., Cheng, Y. D., Cui, S. W., Cui, X. H., Cui, Y. D., Dai, B. Z., Dai, H. L., Dai, Z. G., Danzengluobu, della Volpe, D., Duan, K. K., Fan, J. H., Fan, Y. Z., Fan, Z. X., Fang, J., Fang, K., Feng, C. F., Feng, L., Feng, S. H., Feng, X. T., Feng, Y. L., Gao, B., Gao, C. D., Gao, L. Q., Gao, Q., Gao, W., Gao, W. K., Ge, M. M., Geng, L. S., Gong, G. H., Gou, Q. B., Gu, M. H., Gu, F. L., Guo, J. G., Guo, X. L., Guo, Y. Q., Guo, Y. Y., Han, Y. A., He, H. H., He, H. N., He, S. L., He, X. B., He, Y., Heller, M., Hor, Y. K., Hou, C., Hou, X., Hu, H. B., Hu, Q., Hu, S., Hu, S. C., Hu, X. J., Huang, D. H., Huang, W. H., Huang, X. T., Huang, X. Y., Huang, Y., Huang, Z. C., Ji, X. L., Jia, H. Y., Jia, K., Jiang, K., Jiang, Z. J., Jin, M., Kang, M. M., Ke, T., Kuleshov, D., Li, B. B., Li, Cheng, Li, Cong, Li, F., Li, H. B., Li, H. C., Li, H. Y., Li, J., Li, Jian, Li, Jie, Li, K., Li, W. L., Li, X. R., Li, Xin, Li, Y. Z., Li, Zhe, Li, Zhuo, Liang, E. W., Liang, Y. F., Lin, S. J., Liu, B., Liu, C., Liu, D., Liu, H., Liu, H. D., Liu, J., Liu, J. L., Liu, J. S., Liu, J. Y., Liu, M. Y., Liu, R. Y., Liu, S. M., Liu, W., Liu, Y., Liu, Y. N., Long, W. J., Lu, R., Luo, Q., Lv, H. K., Ma, B. Q., Ma, L. L., Ma, X. H., Mao, J. R., Masood, A., Min, Z., Mitthumsiri, W., Nan, Y. C., Ou, Z. W., Pang, B. Y., Pattarakijwanich, P., Pei, Z. Y., Qi, M. Y., Qi, Y. Q., Qiao, B. Q., Qin, J. J., Ruffolo, D., Sáiz, A., Shao, C. Y., Shao, L., Shchegolev, O., Sheng, X. D., Shi, J. Y., Song, H. C., Stenkin, Yu. V., Stepanov, V., Su, Y., Sun, Q. N., Sun, X. N., Sun, Z. B., Tam, P. H. T., Tang, Z. B., Tian, W. W., Wang, B. D., Wang, C., Wang, H., Wang, H. G., Wang, J. C., Wang, J. S., Wang, L. P., Wang, L. Y., Wang, R., Wang, R. N., Wang, W., Wang, X. G., Wang, X. Y., Wang, Y., Wang, Y. D., Wang, Y. J., Wang, Y. P., Wang, Z. H. Wang. Z. X., Wang, Zhen, Wang, Zheng, Wei, D. M., Wei, J. J., Wei, Y. J., Wen, T., Wu, C. Y., Wu, H. R., Wu, S., Wu, X. F., W, Y. S., Xi, S. Q., Xia, J., Xia, J. J., Xiang, G. M., Xiao, D. X., Xiao, G., Xin, G. G., Xin, Y. L., Xing, Y., Xiong, Z., Xu, D. L., Xu, R. X., Xue, L., Yan, D. H., Yan, J. Z., Yang, C. W., Yang, F. F., Yang, H. W., Yang, J. Y., Yang, L. L., Yang, M. J., Yang, R. Z., Yang, S. B., Yao, Y. H., Yao, Z. G., Ye, Y. M., Yin, L. Q., Yin, N., You, X. H., You, Z. Y., Yu, Y. H., Yuan, Q., Yue, H., Zeng, H. D., Zeng, T. X., Zeng, W., Zeng, Z. K., Zha, M., Zhai, X. X., Zhang, B. B., Zhang, F., Zhang, H. M., Zhang, H. Y., Zhang, J. L., Zhang, L. X., Zhang, Li, Zhang, Lu, Zhang, P. F., Zhang, P. P., Zhang, R., Zhang, S. B., Zhang, S. R., Zhang, S. S., Zhang, X., Zhang, X. P., Zhang, Y. F., Zhang, Y. L., Zhang, Yi, Zhang, Yong, Zhao, B., Zhao, J., Zhao, L., Zhao, L. Z., Zhao, S. P., Zheng, F., Zheng, Y., Zhou, B., Zhou, H., Zhou, J. N., Zhou, P., Zhou, R., Zhou, X. X., Zhu, C. G., Zhu, F. R., Zhu, H., Zhu, K. J., and Zuo, X.
Subjects: Astrophysics - High Energy Astrophysical Phenomena, High Energy Physics - Experiment
Abstract: The Large High Altitude Air Shower Observatory (LHAASO) has three sub-arrays, KM2A, WCDA and WFCTA. The flux variations of cosmic ray air showers were studied by analyzing the KM2A data during the thunderstorm on 10 June 2021. The number of shower events that meet the trigger conditions increases significantly in atmospheric electric fields, with maximum fractional increase of 20%. The variations of trigger rates (increases or decreases) are found to be strongly dependent on the primary zenith angle. The flux of secondary particles increases significantly, following a similar trend with that of the shower events. To better understand the observed behavior, Monte Carlo simulations are performed with CORSIKA and G4KM2A (a code based on GEANT4). We find that the experimental data (in saturated negative fields) are in good agreement with simulations, assuming the presence of a uniform upward electric field of 700 V/cm with a thickness of 1500 m in the atmosphere above the observation level. Due to the acceleration/deceleration and deflection by the atmospheric electric field, the number of secondary particles with energy above the detector threshold is modified, resulting in the changes in shower detection rate., Comment: 18 pages, 11 figures
Published: 2022
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42. Lessons to Learn About the Misdiagnosis of a Rare Case in China: Bart Syndrome or Carmi Syndrome?

Author: Wei XQ, Zhang JY, Mei YW, Li EQ, Dai QL, Yang XL, Luo D, Li B, Hua P, Cai J, Lai H, Qi DF, Lai S, Qin M, and Lin YH
Subjects: carmi syndrome, bart syndrome，epidermolysis bullosa，eb，junctional epidermolysis bullosa, jeb, congenital pyloric atresia, cpa, aplasia cutis congenita, acc, congenital localized absence of skin, clas, pyloric atresia, pa, epidermolysis bullosa with pyloric atresia, eb-pa, multidisciplinary teams, mdt., Medicine (General), R5-920
Abstract: Xiaoqing Wei,1,&ast; Junying Zhang,2,&ast; Youwen Mei,3 Eqiong Li,4 Qianling Dai,1 Xiaoli Yang,1 Dan Luo,5 Biao Li,6 Ping Hua,7 Jian Cai,7 Hua Lai,8 Dongfeng Qi,9 Sha Lai,10 Mi Qin,11 Yonghong Lin4 1Department of Cervical Disease and Cervical Cancer Prevention and Treatment, Chengdu Women’s and Children’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, People’s Republic of China; 2Clinical Laboratory Department, Chengdu Women’s and Children’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, People’s Republic of China; 3Department of Reproduction and Infertility, Chengdu Women’s and Children’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, People’s Republic of China; 4Department of Gynecology, Chengdu Women’s and Children’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, People’s Republic of China; 5Department of Obstetrics, Chengdu Women’s and Children’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, People’s Republic of China; 6Department of Neonatal Intensive Care Unit, Chengdu Women’ and Children’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, People’s Republic of China; 7Department of Pathology, Chengdu Women’s and Children’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, People’s Republic of China; 8Department of Radiation and Interventional Therapy, Chengdu Women’s and Children’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, People’s Republic of China; 9Department of Ultrasonics, Chengdu Women’s and Children’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, People’s Republic of China; 10Dermatology Department, Chengdu Women’s and Children’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, People’s Republic of China; 11Department of Pediatric Surgery, Chengdu Women’s and Children’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Yonghong Lin; Xiaoqing Wei, Email linyhcd2011@163.com; xqcherry@163.comObjective: We report a case of Carmi Syndrome in a neonate.Aim: To share our lessons in diagnosis of the case of Carmi Syndrome.Case Report: Carmi Syndrome is an extremely rare autosomal recessive genetic disorder characterized the coexistence of pyloric atresia and junctional epidermolysis bullosa, and with aplasia cutis congenita in approximately 28% patients. In this case, a full-term male neonate was born to a G4P2+1L1 multipara through cesarean section delivery in hospital in a non-consanguineous marriage with 4000mL of II°meconium-stained amniotic fluid. He was found extensive skin loss over lower legs and other parts, with scattered blisters and bilateral microtia. Plain abdominal X-ray revealed a large gastric air bubble with no gas distally. The mother had an intrauterine fetal loss previously for reasons unknown. The dermatologist diagnosed the newborn with Bart Syndrome, while the pediatric surgeon diagnosed congenital pyloric atresia(CPA). The parents refused further treatment and the neonate passed away about 30 hours after birth.Outcome: The neonate passed away about 30 hours after birth.Conclusion: Lessons from this case:①.Rule out Carmi Syndrome in patients with PA, and differentiate Bart syndrome and Carmi Syndrome in patients with abnormal skin manifestations. ②. For rare and/or severe diseases, multidisciplinary teams(MDTs) should be establish. ③. Genetic counseling and prenatal diagnosis are necessary prior to subsequent childbearings. ④.Termination of pregnancy might be contemplated if certain indicators are revealed.Keywords: Carmi syndrome, bart syndrome, epidermolysis bullosa, EB, junctional epidermolysis bullosa, JEB, congenital pyloric atresia, CPA, aplasia cutis congenita, ACC, congenital localized absence of skin, CLAS, pyloric atresia, PA, epidermolysis bullosa with pyloric atresia, EB-PA, multidisciplinary teams, MDT
Published: 2024

43. Cuproptosis-Related Biomarkers and Characterization of Immune Infiltration in Sepsis

Author: Wang Y, Qiu X, Liu J, Liu X, Pan J, Cai J, and Qu S
Subjects: machine learning, cuproptosis, immune infiltration, sepsis, diagnostic model, Pathology, RB1-214, Therapeutics. Pharmacology, RM1-950
Abstract: Yuanfeng Wang,1,&ast; Xu Qiu,1,&ast; Jiao Liu,1,&ast; Xuanyi Liu,1 Jialu Pan,2 Jiayi Cai,2 Xiaodong Liu,1,3 Shugen Qu1,3 1College of Public Health and Management, Zhejiang Provincial Key Laboratory of Watershed Science and Health, Wenzhou Medical University, Wenzhou, People’s Republic of China; 2The First Clinical Medical College, Wenzhou Medical University, Wenzhou, People’s Republic of China; 3South Zhejiang Institute of Radiation Medicine and Nuclear Technology, Wenzhou, People’s Republic of China&ast;These authors contributed equally to this work.These authors contributed equally to this workCorrespondence: Xiaodong Liu; Shugen Qu, Email forget45@wmu.edu.cn; shugenju@wmu.edu.cnIntroduction: Sepsis is a worldwide epidemic, with high morbidity and mortality. Cuproptosis is a form of cell death that is associated with a wide range of diseases. This study aimed to explore genes associated with cuproptosis in sepsis, construct predictive models and screen for potential targets.Methods: The LASSO algorithm and SVM-RFE model has been analysed the expression of cuproptosis-related genes in sepsis and immune infiltration characteristics and identified the marker genes under a diagnostic model. Gene-drug networks, mRNA-miRNA networks and PPI networks were constructed to screen for potential biological targets. The expression of marker genes was validated based on the GSE57065 dataset. Consensus clustering method was used to classify sepsis samples.Results: We found 381 genes associated with the development of sepsis and discovered significantly differentially expressed cuproptosis-related genes of 16 cell types in sepsis and immune infiltration with CD8/CD4 T cells being lower. NFE2L2, NLRP3, SLC31A1, DLD, DLAT, PDHB, MTF1, CDKN2A and DLST were identified as marker genes by the LASSO algorithm and the SVM-RFE model. AUC > 0.9 was constructed for PDHB and MTF1 alone respectively. The validation group data for PDHB (P=0.00099) and MTF1 (P=7.2e-14) were statistically significant. Consistent clustering analysis confirmed two subtypes. The C1 subtype may be more relevant to cellular metabolism and the C2 subtype has some relevance to immune molecules.The results of animal experiments showed that the gene expression was consistent with the bioinformatics analysis.Discussion: Our study systematically explored the relationship between sepsis and cuproptosis and constructed a diagnostic model. And, several cuproptosis-related genes may interfere with the progression of sepsis through immune cell infiltration.Keywords: machine learning, cuproptosis, immune infiltration, sepsis, diagnostic model
Published: 2024

44. Cyanopolyyne line survey towards high-mass star-forming regions with TMRT

Author: Wang, Y. X., Zhang, J. S., Yan, Y. T., Qiu, J. J., Chen, J. L., Zhao, J. Y., Zou, Y. P., Wu, X. C., He, X. L., Gong, Y. B., and Cai, J. H.
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: We carried out a cyanopolyyne line survey towards a large sample of HMSFRs using the Shanghai Tian Ma 65m Radio Telescope (TMRT). Our sample consisted of 123 targets taken from the TMRT C band line survey. It included three kinds of sources, namely those with detection of the 6.7 GHz CH3OH maser alone, with detection of the radio recombination line (RRL) alone, and with detection of both (hereafter referred to as Maser-only, RRL-only, and Maser-RRL sources, respectively). We detected HC3N in 38 sources, HC5N in 11 sources, and HC7N in G24.790+0.084, with the highest detection rate being found for Maser-RRL sources and a very low detection rate found for RRL-only sources. Their column densities were derived using the rotational temperature measured from the NH3 lines. And we constructed and fitted the far-infrared (FIR) spectral energy distributions. Based on these, we derive their dust temperatures, H2 column densities, and abundances of cyanopolyynes relative to H2. The detection rate, the column density, and the relative abundance of HC3N increase from Maser-only to Maser-RRL sources and decrease from Maser-RRL to RRL-only sources. This trend is consistent with the proposed evolutionary trend of HC3N under the assumption that our Maser-only, Maser-RRL, and RRL-only sources correspond to massive young stellar objects, ultra-compact HII regions, and normal classical HII regions, respectively. Furthermore, a statistical analysis of the integrated line intensity and column density of HC3N and shock-tracing molecules (SiO, H2CO) enabled us to find positive correlations between them. This suggests that HC3N may be another tracer of shocks, and should therefore be the subject of further observations and corresponding chemical simulations. Our results indirectly support the idea that the neutral--neutral reaction between C2H2 and CN is the dominant formation pathway of HC3N., Comment: 23 pages, 5 figures, 4 tables, Accepted to A&A
Published: 2022
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45. A Possible 3-Year Quasi-Periodic Oscillation in {\gamma}-Ray Emission from the FSRQ S5 1044+71

Author: Wang, G. G., Cai, J. T., and Fan, J. H.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: Variability is a typical observation feature of Fermi blazars, sometimes it shows quasi-periodic oscillation (QPO). In this work, we obtained 5-day binned light curves (with a time coverage of $\sim$ 12.9 yr) for S5 1044+71 based on Fermi LAT data, adopted five different methods: Date-compensated Discrete Fourier Transform (DCDFT), Jurkevich (JV), Lomb-Scargle Periodogram (LSP), a Fortran 90 program (REDFIT) and the Weighted Wavelet Z-transform (WWZ) to the $\gamma$-ray light curve, and found a possible QPO of 3.06 $\pm$ 0.43 yr at the significance level of $\sim3.6\sigma$. A binary black hole model including accretion model and dual-jets model is used to explain this quasi-periodic variability. We also estimated the Doppler factors and the apparent velocity for the two jet components. We speculate that this $\gamma$-ray quasi-periodic modulation suggest the presence of a binary supermassive black hole in S5 1044+71., Comment: 11 pages, 10 figures, accepted by APJ
Published: 2022
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46. Construction and Validation of a Novel Nomogram Predicting Recurrence in Alpha-Fetoprotein-Negative Hepatocellular Carcinoma Post-Surgery Using an Innovative Liver Function-Nutrition-Inflammation-Immune (LFNII) Score: A Bicentric Investigation

Author: Zhang BL, Liu J, Diao G, Chang J, Xue J, Huang Z, Zhao H, Yu L, and Cai J
Subjects: hepatocellular carcinoma, alpha-fetoprotein-negative, immunity, inflammation, nutrition, recurrence-free survival, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract: Bo-Lun Zhang,1 Jia Liu,2 Guanghao Diao,2 Jianping Chang,1 Junshuai Xue,1 Zhen Huang,1 Hong Zhao,1 Lingxiang Yu,2 Jianqiang Cai1 1Department of Hepatobiliary Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China; 2Department of Hepatobiliary Surgery, the Fifth Medical Center of the PLA General Hospital, Beijing, People’s Republic of ChinaCorrespondence: Jianqiang Cai, Department of Hepatobiliary Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, People’s Republic of China, Tel +86-10-87787100, Fax +86-10-67709698, Email caijianqiang@cicams.ac.cnPurpose: We developed a nomogram based on the liver function, nutrition, inflammation, and immunity (LFNII) score to predict recurrence-free survival (RFS) post-resection in patients with hepatocellular carcinoma (HCC) exhibiting alpha-fetoprotein (AFP) negativity (AFP ≤ 20 ng/mL).Patients and Methods: Clinical data of 661 patients diagnosed with alpha-fetoprotein-negative hepatocellular carcinoma (AFP-NHCC) who underwent surgical resection at two medical centers between 2012 and 2021 were collected. A total of 462 and 199 patients served as the training and validation sets, respectively. Pre-operative blood markers were collected and analyzed for LFNII. The LFNII score was formulated using the least absolute shrinkage and selection operator Cox regression model. A nomogram model was developed using the training set to incorporate other relevant clinicopathological indicators and predict postoperative recurrence. Model discrimination was assessed using the receiver operating characteristic curve, calibration was evaluated using a calibration curve, and clinical applicability was assessed using clinical decision curve analysis. A comparison with liver cancer staging was performed using the nomogram model. Finally, a cohort study was conducted to validate our findings.Results: We derived the LFNII scores from nine indicators. Elevated LFNII scores correlated with unfavorable clinicopathological features. The LFNII score area under the curve revealed superior predictive efficacy at 1-, 2-, and 5-year RFS intervals, with values of 0.675, 0.658, and 0.633, respectively. Multivariate Cox analysis revealed that a high LFNII score independently increased RFS risk in patients with AFP-NHCC. The C-index of the LFNII-nomogram model was 0.686 (95% confidence interval [CI], 0.651– 0.721). The nomogram model’s clinical application value surpassed that of standard HCC staging systems.Conclusion: The LFNII score-derived nomogram effectively predicted the RFS of patients with AFP-NHCC after curative resection.Keywords: hepatocellular carcinoma, alpha-fetoprotein-negative, immunity, inflammation, nutrition, recurrence-free survival
Published: 2024

47. Recombinant Human Heavy Chain Ferritin Nanoparticles Serve as ROS Scavengers for the Treatment of Ischemic Stroke

Author: Qi M, Cheng Y, Liu K, Cai J, Liu T, Wu X, Tang H, Huang H, Chen Q, and Zhou X
Subjects: human heavy chain ferritin, ros scavenger, ischemic stroke, sod-mimetic, neuroprotection, Medicine (General), R5-920
Abstract: Mi Qi, Yajuan Cheng, Ke Liu, Jingxing Cai, Tianyu Liu, Xiaoying Wu, Huili Tang, He Huang, Qinbiao Chen, Xiaoguang Zhou The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong, People’s Republic of ChinaCorrespondence: Mi Qi; Xiaoguang Zhou, Tel +86-13510794978 ; +86-18818780760, Email qimi@mail.sysu.edu.cn; gzzhouxg@163.comPurpose: Ischemic stroke is a high-incidence disease that threatens human well-being. The potent neuroprotective effects render reactive oxygen species (ROS) scavengers potential agents for acute ischemic stroke therapy. Challenges such as inadequate permeability across the blood-brain barrier (BBB), limited half-life, and adverse effects hinder the widespread utilization of small molecule and inorganic ROS scavengers. Thus, there is an urgent demand for efficacious neuroprotective agents targeting ischemic stroke. Our study discovered the superoxide dismutase (SOD)-mimetic activity of recombinant human heavy chain ferritin (rHF) nanoparticles expressed from Escherichia coli (E. coli). Subsequent investigations delved into the ROS-scavenging proficiency of rHF within neural cells, its therapeutic efficacy against ischemic stroke, and the elucidation of its neuroprotective mechanisms.Methods: rHF protein nanoparticles were expressed in E. coli and purified via size-exclusion chromatography. The superoxide anion (•O2−) scavenging SOD-mimetic activity of rHF nanoparticles was measured using a SOD detection kit. The ROS scavenging ability and protection effects against oxidative damage of rHF nanoparticles were studied in H2O2-induced PC12 cells. Therapeutic effects and neuroprotective mechanisms of rHF against ischemic stroke were investigated with transient middle cerebral artery occlusion (MCAO) reperfusion mice model.Results: rHF nanoparticles can eliminate excessive ROS in nerve cells and alleviate oxidative damage. The results of animal experiments demonstrated that rHF nanoparticles passed across BBB, reduced infarct areas in brain tissue, and lowered the neurological deficit score of ischemia-reperfusion model mice. Additionally, rHF nanoparticles mitigated neuronal apoptosis and ferroptosis, suppressed microglial activation, maintained oxygen homeostasis, and exhibited negligible organ toxicity.Conclusion: rHF nanoparticle could be developed as a new ROS scavenger for nerve cells and has therapeutic potential as a drug for cerebral ischemia-reperfusion injury. Keywords: human heavy chain ferritin, ROS scavenger, ischemic stroke, SOD-mimetic, neuroprotection
Published: 2024

48. The impacts of exposure to risk factors during youth on the increasing global trend of early-onset pancreatic cancer

Author: Cai, J., Lu, B., Chen, H., Lu, M., Zhang, Y., Luo, C., You, L., Dai, M., and Zhao, Y.
Published: 2024
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49. Microplastic pollution and characteristics in the surface waters of the middle and lower reaches of the Han River along Hubei Province, China

Author: Chen, C., Deng, J., Zhang, Q., and Cai, J.
Published: 2023
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50. Comparative Clinical Implications of Calcified versus Non-Calcified Tissue Protrusion After Coronary Stent Implantation

Author: Wu X, Wu M, Huang H, Wang L, Liu Z, and Cai J
Subjects: percutaneous coronary intervention, intravascular ultrasound, tissue protrusion, prognosis., Medicine (General), R5-920
Abstract: Xi Wu, Mingxing Wu, Haobo Huang, Lei Wang, Zhe Liu, Jie Cai, He Huang Department of Cardiology, Xiangtan Central Hospital, Xiangtan, Hunan, 411100, People’s Republic of ChinaCorrespondence: He Huang, Department of Cardiology, Xiangtan Central Hospital, Xiangtan, Hunan, 411100, People’s Republic of China, Email 1764200045@e.gzhu.edu.cnPurpose: The prognostic implications of calcified versus non-calcified tissue protrusions (TPs) following stent implantation remain undetermined. This study aimed to evaluate the differential clinical outcomes associated with calcified and non-calcified TP morphologies.Patients and Methods: Employing intravascular ultrasound (IVUS), we identified calcified TPs as calcium fragment extrusions permeating the stent struts, while non-calcified TPs were characterized as plaque and/or thrombus extensions through the stent into the arterial lumen. The primary endpoint encompassed target lesion failure (TLF), comprising cardiac death, target vessel myocardial infarction, clinically driven target lesion revascularization (TLR), or stent thrombosis, assessed in patients with a follow-up period exceeding one year.Results: Of 1033 patients subjected to pre- and post-intervention IVUS, 62 exhibited calcified TPs (6.0%), and 279 presented non-calcified TPs (27.0%), forming the basis of this analysis. Multivariable linear regression indicated calcified nodules as a significant predictor of calcified TP [Odds Ratio (OR) 2.47; 95% Confidence Interval (CI) 2.33 to 2.62; P < 0.001], with ST-segment elevation myocardial infarction emerging as an inverse correlate [OR 0.82; 95% CI 0.73 to 0.93; P = 0.004]. Two-year data revealed a higher incidence of TLF in patients with calcified TPs versus their non-calcified counterparts (11.3% vs 2.2%, P < 0.001), and a marked increase in clinically driven TLR (9.7% vs 1.4%, P < 0.001). Calcified TPs were independently correlated with increased TLF risk in the adjusted model [Hazard Ratio (HR) 2.47; 95% CI 1.17 to 5.16; P = 0.027].Conclusion: After drug-eluting stent implantation, IVUS-identified calcified TPs correlate with adverse clinical outcomes compared to non-calcified TP formations.Keywords: percutaneous coronary intervention, intravascular ultrasound, tissue protrusion, prognosis
Published: 2023

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