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4. Repeated pulmonary nodules as the primary symptom of familial hemophagocytic lymphohistiocytosis in adults: a case report and review

Author

Zhang, Lulu, primary, Dong, Chuanchuan, additional, Wu, Qiannan, additional, Li, Yupeng, additional, Feng, Liting, additional, Xing, Yanqing, additional, Dong, Yangdou, additional, Liu, Le, additional, Li, Xiaohui, additional, Huo, Rujie, additional, Dong, Yanting, additional, Cheng, Erjing, additional, Ge, Xiaoyan, additional, and Xinrui, Tian, additional

Published
2023
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6. Predictors of COVID-19 Severity in Elderly Patients Infected by Omicron in China, 18 December 2022–5 February 2023

Author

Xing,Yanqing, Li,Yupeng, Feng,Liting, Huo,Rujie, Ma,Xinkai, Dong,Yanting, Liu,Dai, Niu,Yuheng, Tian,Xinrui, and Chen,Erjing

Subjects
Infection and Drug Resistance
Abstract

Yanqing Xing,1 Yupeng Li,1 Liting Feng,1 Rujie Huo,1 Xinkai Ma,1 Yanting Dong,1 Dai Liu,1 Yuheng Niu,2 Xinrui Tian,1,&ast; Erjing Chen1,&ast; 1The Second Hospital of Shanxi Medical University, Taiyuan, People’s Republic of China; 2The First Hospital of Shanxi Medical University, Taiyuan, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Xinrui Tian; Erjing Chen, The Second Hospital of Shanxi Medical University, No. 382, Wuyi Road, Xinghualing District, Taiyuan, Shanxi, People’s Republic of China, Email tianxr@126.com; mayyoubehappy44@163.comPurpose: To analyze the clinical characteristics and prognosis of patients hospitalized with non-severe, severe pneumonia and death in Omicron COVID-19.Patients and Methods: We collected clinical data from 118 patients with COVID-19 in China from 18 December, 2022 and 5 February, 2023. According to the outcome, the patients were divided into non-severe group, severe group and death group. Subsequently, we statistically analyzed the general condition, clinical manifestations, laboratory parameters, NLR, MLR, PLR and HALP of these groups. We also retrospectively analyzed the possible factors affecting the prognostic regression of patients with COVID-19.Results: A total of 118 COVID-19 patients were enrolled in this study, including 64 non-severe patients, 38 severe patients and 16 death patients. Compared with the non-severe group, T lymphocytes, B lymphocytes, Th1, Th2, Th17, Treg cells, IgA, IgG, IgM in the severe and death groups decreased more significantly (P< 0.05). The levels of myocardial markers, ALT, AST, BUN, Cr, D-dimer, fibrinogen, NLR, MLR and PLR in the severe and death groups were significantly higher than those in the non-severe group (P< 0.05). The level of HALP was significantly lower than that of non-severe group (P< 0.05). MLR is not only an independent risk factor for the transition from non-severe to severe disease, but also an independent risk factor for predicting the possibility of death in COVID-19 patients.Conclusion: The analysis of COVID-19 patients in China showed that severe patients were older, more likely to have related complications, lower lymphocyte count, liver and kidney function disorder, glucose and lipid metabolism disorders, myocardial injury, and abnormal coagulation function, suggesting the need for early anticoagulant therapy. In addition, NLR, MLR, PLR and HALP can be used as biomarkers to evaluate the severity and prognosis of COVID-19 patients.Keywords: COVID-19, omicron, clinical characteristics, lymphocyte subpopulation, Shanxi

Published
2023

7. Exploration and Validation of Potential Biomarkers and Therapeutic Targets in Ferroptosis of Asthma

Author

Xing,Yanqing, Feng,Liting, Dong,Yangdou, Li,Yupeng, Zhang,Lulu, Wu,Qiannan, Huo,Rujie, Dong,Yanting, Tian,Xinrui, Tian,Xinli, Xing,Yanqing, Feng,Liting, Dong,Yangdou, Li,Yupeng, Zhang,Lulu, Wu,Qiannan, Huo,Rujie, Dong,Yanting, Tian,Xinrui, and Tian,Xinli

Abstract

Yanqing Xing,1 Liting Feng,1 Yangdou Dong,2 Yupeng Li,1 Lulu Zhang,1 Qiannan Wu,1 Rujie Huo,1 Yanting Dong,1 Xinrui Tian,1 Xinli Tian3 1Department of Respiratory and Critical Care Medicine, The Second Hospital of Shanxi Medical University, Taiyuan, People’s Republic of China; 2College of Basic Medicine, Shanxi Medical University, Taiyuan, People’s Republic of China; 3Department of Cardiology, Chinese PLA General Hospital, Beijing, People’s Republic of ChinaCorrespondence: Xinrui Tian, Department of Respiratory and Critical Care Medicine, The Second Hospital of Shanxi Medical University, No. 382, Wuyi Road, Xinghualing District, Taiyuan, People’s Republic of China, Tel +8613834575570, Email tianxr@126.com Xinli Tian, Department of Cardiology, Chinese PLA General Hospital, No. 7 Medical Center No. 5 Nanmencang, Dongsijiao, Dongcheng District, Beijing, People’s Republic of China, Email 18600501329@126.comPurpose: Asthma is a chronic inflammatory airway disease involving multiple mechanisms, of which ferroptosis is a form of programmed cell death. Recent studies have shown that ferroptosis may play a crucial role in the pathogenesis of asthma, but no specific ferroptosis gene has been found in asthma, and the exact mechanism is still unclear. The present study aimed to screen ferroptosis genes associated with asthma and find therapeutic targets, in order to contribute a new clue for the diagnosis and therapy of asthma.Methods: Ferroptosis-related differentially expressed genes (FR-DEGs) in asthma were selected by the GSE41861, GSE43696 and ferroptosis datasets. Next, the FR-DEGs were subjected by GO and KEGG enrichment, and the mRNA-miRNA network was constructed. Then, GSEA and GSVA enrichment analysis and Immune infiltration analysis were performed, followed by targeted drug prediction. Finally, the expression of FR-DEGs was confirmed using GSE63142 dataset and RT-PCR assay.Results: We found 13 FR-DEGs by the GSE41861, GSE43696 and ferroptosis database. Funct

Published
2023

8. Developing a visual model for predicting depression in patients with lung cancer.

Author

Xing, Yanqing, Zhao, Wenxiao, Duan, Chenchen, Zheng, Jun, Zhao, Xuelian, Yang, Jingyu, Sun, Na, and Chen, Jie

Subjects
*MENTAL depression risk factors, *BIOLOGICAL models, *CROSS-sectional method, *DISCRIMINATION (Sociology), *CALIBRATION, *AGE distribution, *LUNG tumors, *SOCIAL stigma, *RISK assessment, *MENTAL depression, *DESCRIPTIVE statistics, *CHI-squared test, *DISEASE duration, *EXERCISE, *DISEASE prevalence, *RESEARCH funding, *PREDICTION models, *STATISTICAL sampling, *LOGISTIC regression analysis, *STATISTICAL models, *PSYCHOLOGICAL resilience, *DISEASE complications
Abstract

Aims and objectives: To investigate and analyse the prevalence of depression among patients with lung cancer, identify risk factors of depression, and develop a visual, non‐invasive, and straightforward clinical prediction model that can be used to predict the risk probability of depression in patients with lung cancer quantitatively. Background: Depression is one of the common concomitant symptoms of patients with lung cancer, which can increase the risk of suicide. However, the current assessment tools cannot combine multiple risk factors to predict the risk probability of depression in patients. Design: A cross‐sectional study. Methods: The clinical data from 297 patients with lung cancer in China were collected and analysed in this cross‐sectional study. The clinical prediction model was constructed according to the results of the Chi‐square test and the logistic regression analysis, evaluated by discrimination, calibration, and decision curve analysis, and visualised by a nomogram. This study was reported using the TRIPOD checklist. Results: 130 patients with lung cancer had depressive symptoms with a prevalence of 43.77%. A visual prediction model was constructed based on age, disease duration, exercise, stigma, and resilience. This model showed good discrimination at an AUC of 0.842. Calibration curve analysis indicated a good agreement between experimental and predicted values, and the decision curve analysis showed a high clinical utility. Conclusions: The visual prediction model developed in this study has excellent performance, which can accurately predict the occurrence of depression in patients with lung cancer at an early stage and assist the medical staff in taking targeted preventative measures. Relevance to clinical practice: The visual, non‐invasive, and simple nomogram can help clinical medical staff to calculate the risk probability of depression among patients with lung cancer, formulate personalised preventive care measures for high‐risk groups as soon as possible, and improve the quality of life of patients. [ABSTRACT FROM AUTHOR]

Published
2023
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11. Experimental study on replacement of methane hydrates by CO2.

Author

Xing Yanqing, Qi Yingxia, Yu Zhiguang, and Wang Le

Subjects
*METHANE hydrates, *MISCIBLE-phase displacement, *CARBON dioxide flooding, *PRESSURE, *EFFECT of temperature on quartz, *CARBON dioxide
Abstract

Replacement of methane hydrate by CO2 is a new approach for the development of CH4, and the method can develop CH4 and store CO2 permanently. By optimizing design of experiment device, the influences on displacement efficiency of temperature and pressure in the system of quartz sand media are studied. The experiment results show that the replacement rates are 1.75%, 6.99%, 13.43%, 5.53%, 22.64%, 44.90% respectively, the corresponding replacement temperature are 273.15 K, 274.15 K, 275.15 K, 276.15 K, 277.15 K, 278.15 K, and the CO2 charge pressure is constant at 2.5 MPa. On the other hand, the replacement efficiency are 37.11%, 13.43%, 3.44%, 4.58% respectively, corresponding to the CO2 charge pressure at 2.0 MPa, 2.5 MPa, 3.0 MPa, 3.5 MPa and the replacement temperature being constant at 275.15 K. Therefore, temperature and pressure are driving force factors of CO2 replacement of CH4 hydrate reaction. [ABSTRACT FROM AUTHOR]

Published
2014
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12. Experimental study on replacement of methane hydrates by CO2.

Author

Xing Yanqing, Qi Yingxia, Yu Zhiguang, and Wang Le

Subjects
METHANE hydrates, MISCIBLE-phase displacement, CARBON dioxide flooding, PRESSURE, EFFECT of temperature on quartz, CARBON dioxide
Abstract

Replacement of methane hydrate by CO2 is a new approach for the development of CH4, and the method can develop CH4 and store CO2 permanently. By optimizing design of experiment device, the influences on displacement efficiency of temperature and pressure in the system of quartz sand media are studied. The experiment results show that the replacement rates are 1.75%, 6.99%, 13.43%, 5.53%, 22.64%, 44.90% respectively, the corresponding replacement temperature are 273.15 K, 274.15 K, 275.15 K, 276.15 K, 277.15 K, 278.15 K, and the CO2 charge pressure is constant at 2.5 MPa. On the other hand, the replacement efficiency are 37.11%, 13.43%, 3.44%, 4.58% respectively, corresponding to the CO2 charge pressure at 2.0 MPa, 2.5 MPa, 3.0 MPa, 3.5 MPa and the replacement temperature being constant at 275.15 K. Therefore, temperature and pressure are driving force factors of CO2 replacement of CH4 hydrate reaction. [ABSTRACT FROM AUTHOR]

Published
2014
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13. “Fatty” or “steatotic”: Position statement from a linguistic perspective by the Chinese-speaking community

Author

Miao, Lei, Ye, Shu-Mian, Fan, Jian-Gao, Seto, Wai-Kay, Yu, Hon Ho, Yu, Ming-Lung, Kao, Jia-Horng, Boon-Bee Goh, George, Young, Dan Yock, Wong, Yu Jun, Chan, Wah-Kheong, Yang, Wah, Jia, Jidong, Lau, George, Wei, Lai, Shi, Junping, Zhang, Huijie, Bi, Yan, Pik-Shan Kong, Alice, Pan, Calvin Q., Zheng, Ming-Hua, Liang, Huiqing, Yang, Ling, Li, Xinhua, Zeng, Qing-Lei, Gao, Rong, Hu, Songhao, Yan, Bi, Jin, Xiaozhi, Li, Gang, Chen, En-Qiang, Hu, Dandan, Fan, Xiaotang, Hu, Peng, Chang, Xiangrong, Jin, Yihui, Cai, Yijing, Chen, Liangmiao, Wen, Qianjun, Sun, Jian, Xu, Hexiang, Li, Junfeng, Yang, Yongping, Huang, Ang, Zhang, Dongmei, Tan, Lin, Li, Dongdong, Zhu, Yueyong, Cai, Chenxi, Gu, Xuemei, Shen, Jilong, Zhong, Jianhong, Li, Lu, Li, Zhenzhen, Ma, Chiye, Liu, Yaming, Zhang, Yimin, Zhao, Lei, Han, Juqiang, Chen, Tao, Zhang, Qiang, Yang, Song, Zhang, Le, Chen, Lanlan, Feng, Gong, Wang, Qixia, Hao, Kunyan, Lu, Qinghua, Mao, Yimin, Zhong, Yandan, Wang, Ningjian, Xin, Yongning, Yu, Yongtao, Qi, Xingshun, Wang, Ke, He, Yingli, Du, Mulong, Zou, Zhengsheng, Xia, Mingfeng, Zhao, Suxian, Zhao, Jingjie, Xie, Wen, Zhang, Yao, Ji, Mao, Richeng, Du, Qingwei, Chen, Haitao, Song, Yongfeng, Wang, Cunchuan, Lu, Yan, Song, Yu, Zhang, Chi, Shi, Li, Mak, Lungyi, Chen, Li, Xu, Liang, Yuan, Hai-Yang, Hong, Liang, Hai, Li, Wu, Xiaoning, Yang, Naibin, Li, Jing-Wei, Jiejin, Zou, Zhuolin, Zheng, Wen, Zhao, Jian, Zhang, Xiang, Huang, Chen-Xiao, Yao, Ying, Yuan, Bao-Hong, Huang, Shanshan, Min, Lian, Chai, Jin, Hong, Wandong, Miao, Kai-Wen, Xiao, Tie, Chen, Shun-Ping, Ye, Feng, Song, Yuhu, Zhang, Jinshun, Zhou, Xiao-Dong, Wang, Mingwei, Dai, Kai, Lou, Jianjun, Duan, Xu, Yu, Hongyan, Jin, Xi, Fu, Liyun, Zhang, Yanliang, Ye, Junzhao, Liu, Feng, Chen, Qin-Fen, Zhou, Yong-Hai, Duan, Xiaohua, Zhang, Qun, Zhang, Faming, Cao, Zhujun, Li, Yingxu, Sun, Dan-Qin, Hu, Ai-Rong, Liu, Fenghua, Chen, Yuanwen, Zhang, Dianbao, Gao, Feng, Ye, Hua, Rao, Huiying, Luo, Kaizhong, Dai, Zhijuan, Wang, Chia-Chi, Tang, Shanhong, Hua, Jing, Deng, Cunliang, Zhou, Ling, Fan, Yu-Chen, Wu, Mingyue, Lu, Hongyan, Zhang, Xiaoxun, Zhang, Huai, Ni, Yan, Kei Ng, Stephen Ka, Li, Chunming, Liu, Chang, Zhang, Xia, Shi, Yu, Yan, Hongmei, Xu, Jinghang, Zhou, Yu-Jie, Cheng, Yuan, Bai, Honglian, Hu, Xiang, Gao, Yufeng, Lin, Biaoyang, Gu, Guangxiang, Chen, Jin, Hu, Xiaoli, Yuan, Xiwei, Wang, Jie, Chen, Qiang, Yiling, Li, Zhu, Xiao Jia, Chen, Xu, Zhu, Yongfen, Liu, Xiaolin, Wang, Bing, Cai, Mingyan, Chen, Enguang, Chen, Jun, Chen, Jingshe, Deng, Hong, Chen, Xiaoxin, Chen, Yingxiao, Cheng, Xinran, Chen, Fei, Ding, Yang, Dong, Zhixia, Ding, Yanhua, Qingxian, Cai, Deng, Zerun, Cai, Tingchen, Chen, Yaxi, Chen, Zhongwei, Chen, Xing, Huang, Jiaofeng, Huang, Mingxing, Fu, Lei, Jin, Jianhong, Geng, Bin, Chen, Yu, Chen, Ruicong, Jin, Weimin, Li, Dongliang, Jin, Xianghong, Li, Jian-Jun, Zhang, Jie, Matsiyit, Alimjan, Wang, Guiqi, Gao, Tian, Zhang, Shu, Yan, Wenmao, Liu, Jie, Chen, Peng, Hu, Hao, Li, Ming, Yuan, Ping Ge, Chen, Yi, Dong, Zhiyong, Li, Xiaopeng, Lin, Su, Li, Jie, Li Ang, Xujing, Liu, Xin, Liu, Shousheng, Li, Min-Dian, Qian, Hui, Qi, Minghua, Peng, Liang, Luo, Fei, Dang, Shuangsuo, Mao, Xianhua, Sheng, Qiyue, Lyu, Jiaojian, Liu, Chenghai, Qi, Kemin, Ma, Honglei, Lu, Zhonghua, Pan, Qiong, Miao, Qing, Li, Xiaosong, Lin, Huapeng, Shui, Guanghou, Qu, Shen, Fei, Wang, Liu, Chang-Hai, Xia, Fan, Wang, Dan, Pan, Ziyan, Hu, Fangzheng, Xu, Long, Xiong, Qing-Fang, Yang, Rui-Xu, Wang, Qi, Chen, Ligang, W Ang, Danny, Ren, Wanhua, Tong, Xiaofei, You, Ningning, Xing, Yanqing, Sun, Chao, Yu, Zhuo, Shuangxu, Xu, Honghai, Sun, Yi, Zhang, Taotao, Wu, Wei, Zhang, Yingmei, Ye, Qing, Zhang, Zhongheng, Yan, Jie, Zhou, Bengjie, Liu, Weiqiang, Li, Yongguo, Zhao, Lili, Lei, Siyi, Zhu, Guangqi, Ouyang, Huang, Zhou, Yaoyao, Yin, Jianhui, Xia, Yongsheng, He, Qiancheng, Zhang, Xiaoyong, Yang, Qiao, Yao, Libin, Pan, Xiazhen, Wang, Xiaodong, Li, Yangyang, Zhu, Shenghao, Zhao, Xinyan, Chen, Sui-Dan, Zhu, Jiansheng, Zeng, Jing, Tang, Liangjie, Hu, Kunpeng, Yang, Wanshui, Huang, Bingyuan, Zhuang, Chengle, Xun, Yunhao, Zhou, Jianghua, Xu, Wenjing, Wu, Bian, Zhang, Xuewu, He, Yong, Mei, Zubing, Xia, Zefeng, Lu, Bin Feng, Li, Qiang, Li, Jia, Yan, Xuebing, Wen, Zhengrong, Liu, Wenyue, Xu, Dongsheng, Chen, Huiting, Wang, Jing, Song, Juan, Peng, Jie, Chen, Jionghuang, Li, Shuchen, Zheng, Yongping, Zhi-Zhi, Xing, Tang, Jieting, Liu, Chuan, Chen, Chao, Guicheng, Wu, Ye, Quanzhong, Ka, Li, Zhou, Yuping, Jia, Xiaoli, Zou, Ziyuan, Zu, Fuqiang, Cai, Yongqian, Chen, Yunzhi, Chu, Jinguo, Yan, Bing, Wang, Tie, Pan, Qiuwei, Xie, Lingling, Zeng, Xufen, Liu, Bingrong, Su, Minghua, Mu, Yibing, Zeng, Menghua, Guo, Yuntong, Yang, Yongfeng, Zhang, Xiaoguan, Wu, Shike, Pan, Jin-Shui, Cao, Li, Feng, Wenhuan, Yubin, Yang, Wang, Na, Lu, Xiaolan, Lu, Guanhua, Xiong, Jianbo, Zhuang, Jianbin, Shi, Guojun, Zhu, Yanfei, Ying, Xing, Qiao, Zengpei, Zhang, Rui, Li, Yuting, Lei, Yuanli, Xixi, Wu, Tian, Na, Lian, Liyou, Zhang, Binbin, Xiaozhu, Huang, Yan, Chen, Wenying, Liu, Kun, Zhang, Ruinan, Lai, Qintao, Wang, Fudi, Wen, Caiyun, Zhang, Xinlei, Wu, Lili, Liang, Yaqin, Jie, You, Xinzhejin, Zeng, Qiqiang, Zhu, Qiang, Chao, Zheng, Shou, Lan, Jin, Wei-Lin, Ye, Chenhui, Han, Yu, Xie, Gangqiao, Zhao, Jing, Ye, Chunyan, Wang, Hua, Song, Lintao, Feng, Juan, Huang, Yubei, Su, Wen, Bai, Juli, Wong, Vincent, Wang, Huifeng, Ming, Wai-Kit, Yu, Yue-Cheng, Jin, Yan, Zhao, Yan, Gao, Lilian, Liangwang, Chen, Hanbin, Ruifangwang, Tang, Yuhan, Chen, Gang, Liu, Dabin, Cai, Xiaobo, Xue, Feng, Yang, Qinhe, Sun, Guangyong, Zhu, Chunxia, Huang, Zhifeng, Zhou, Hongwen, Xiao, Xiao, Hou, Xin, He, Jie, Ji, Dong, Xiao, Huanming, Chi, Xiaoling, Zou, Huaibin, Shi, Yiwen, Fan, Xingliang, Hu, Xiaoyu, Huang, Zhouqin, Cao, Haixia, Jiang, Jingjing, Zhao, Qiang, Chen, Wei, Li, Shi Bo, Zhang, Fan, Chen, Zhiyun, Liu, Jinfeng, Li, Shibo, Liu, Jing, Li, Li, Li, Ruyu, Kun, Ya, Xiao, ErHui, Wang, Tingyao, Wang, Chunjiong, Aili, Aikebaier, Liu, Xiaoxia, Ding, Ran, Zhu, Chonggui, Zeng, Xin, Wu, Miao, Li, Zhen, Yang, Tao, Qin, Yunfei, Sun, Lihua, Xu, Ying, Fu, Xianghui, Li, Yongyin, and Ye, Shumian

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14. A Retrospective Data Audit of Outcome of Moderate and Severe Covid-19 Patients Who Had Received MP and Dex: A Single Center Study.

Author

Li Y, Dong C, Xing Y, Ma X, Ma Z, Zhang L, Du X, Feng L, Huo R, Wu QN, Li P, Hu F, Liu D, Dong Y, Cheng E, Tian X, and Tian X

Abstract

Purpose: To evaluate the necessity of the application of glucocorticoid (GC) in moderate COVID-19 patients, and which is the optimal choice between methylprednisolone (MP) and dexamethasone (DEX) in the clinical use of GC in different types of COVID-19 patients., Patients and Methods: The study included patients with COVID-19 in Shanxi, China, from December 18, 2022, to March 1, 2023. The main clinical outcomes were 30-day mortality, disease exacerbations, and hospitalization days. Secondary outcomes included the demand for non-invasive ventilator-assisted ventilation (NIPPV)/invasive mechanical ventilation (IMV), the need for GC regimen escalation in follow-up treatment, duration of GC treatment, and complications including hyperglycemia and fungal infection., Results: In moderate patients (N = 351), the rate of exacerbation and the need for GC regimen escalation in follow-up treatment was highest in the no-use GC group (P = 0.025, P = 0.01), the rate of fungal infections was highest in the DEX group (P = 0.038), and MP 40 mg/day or DEX 5 mg/day reduced exacerbations with consistent effects. In severe patients (N = 371), the two GC regimens do not affect their 30-day mortality and exacerbation rate, but the number of hospital days was significantly lower in the MP group compared with the DEX group (P < 0.001)., Conclusion: GC use is beneficial in mitigating exacerbations in moderate patients and in patients with moderate COVID-19. In severe patients, MP reduces the number of hospitalization days compared with DEX and may be a superior choice., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2024 Li et al.)

Published
2024
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