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647 results on '"Zheng, Junhua"'

164. Corticosteroid therapy for corona virus disease 2019-related acute respiratory distress syndrome: a cohort study with propensity score analysis

Author

Wu, Chaomin, primary, Hou, Dongni, additional, Du, Chunling, additional, Cai, Yanping, additional, Zheng, Junhua, additional, Xu, Jie, additional, Chen, Xiaoyan, additional, Chen, Cuicui, additional, Hu, Xianglin, additional, Zhang, Yuye, additional, Song, Juan, additional, Wang, Lu, additional, Chao, Yen-cheng, additional, Feng, Yun, additional, Xiong, Weining, additional, Chen, Dechang, additional, Zhong, Ming, additional, Hu, Jie, additional, Jiang, Jinjun, additional, Bai, Chunxue, additional, Zhou, Xin, additional, Xu, Jinfu, additional, Gong, Fengyun, additional, and Song, Yuanlin, additional

Published
2020
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166. Risk Factors Associated With Acute Respiratory Distress Syndrome and Death in Patients With Coronavirus Disease 2019 Pneumonia in Wuhan, China

Author

Wu, Chaomin, primary, Chen, Xiaoyan, additional, Cai, Yanping, additional, Xia, Jia’an, additional, Zhou, Xing, additional, Xu, Sha, additional, Huang, Hanping, additional, Zhang, Li, additional, Zhou, Xia, additional, Du, Chunling, additional, Zhang, Yuye, additional, Song, Juan, additional, Wang, Sijiao, additional, Chao, Yencheng, additional, Yang, Zeyong, additional, Xu, Jie, additional, Zhou, Xin, additional, Chen, Dechang, additional, Xiong, Weining, additional, Xu, Lei, additional, Zhou, Feng, additional, Jiang, Jinjun, additional, Bai, Chunxue, additional, Zheng, Junhua, additional, and Song, Yuanlin, additional

Published
2020
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170. Mental health status and related influencing factors of COVID‐19 survivors in Wuhan, China

Author

Wu, Chaomin, primary, Hu, Xianglin, additional, Song, Jianxin, additional, Yang, Dong, additional, Xu, Jie, additional, Cheng, Kebin, additional, Chen, Dechang, additional, Zhong, Ming, additional, Jiang, Jinjun, additional, Xiong, Weining, additional, Lang, Ke, additional, Tao, Yan, additional, Lin, Xiaoqin, additional, Shi, Guohua, additional, Lu, Liwen, additional, Pan, Longci, additional, Xu, Lei, additional, Zhou, Xin, additional, Song, Yuanlin, additional, Wei, Ming, additional, Zheng, Junhua, additional, and Du, Chunling, additional

Published
2020
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174. Potential Predictors for Disease Progression and Medication Evaluation of 2019 Novel Coronavirus-Infected Pneumonia in Wuhan, China

Author

Zhang, Sheng, primary, Zhao, Jingya, additional, Wu, Zhixiong, additional, Shang, You, additional, Zheng, Junhua, additional, Meng, Mei, additional, Liu, Jiao, additional, Xu, Yanping, additional, Li, Guang, additional, Shi, Mengmeng, additional, Diao, Mengyuan, additional, Chen, Limin, additional, Wang, Tao, additional, Huang, Sisi, additional, Li, Wenzhe, additional, Yang, Qingyuan, additional, Wen, Zhenliang, additional, Zhang, Lidi, additional, Dong, Xuan, additional, Qu, Jieming, additional, and Chen, Dechang, additional

Published
2020
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175. Mental Health Status of Survivors Following COVID-19 in Wuhan, China: A Descriptive Study

Author

Wu, Chaomin, primary, Hu, Xianglin, additional, Song, Jianxin, additional, Du, Chunling, additional, Song, Yuanlin, additional, Yang, Dong, additional, Xu, Jie, additional, Cheng, Kebin, additional, Chen, Dechang, additional, Zhong, Ming, additional, Jiang, Jinjun, additional, Xiong, Weining, additional, Lang, Ke, additional, Tao, Yan, additional, Lin, Xiaoqin, additional, Shi, Guohua, additional, Lu, Liwen, additional, Pan, Longci, additional, Xu, Lei, additional, Zhou, Xin, additional, Wei, Ming, additional, and Zheng, Junhua, additional

Published
2020
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176. Diagnosis and treatment of 471 patients with 2019 novel coronavirus disease (COVID-19)

Author

Jin-Fu Xu, Zheng Junhua, Xin Zhou, Ke-Bin Cheng, Huiping Li, and Weining Xiong

Subjects
medicine.medical_specialty, Coronavirus disease 2019 (COVID-19), medicine.diagnostic_test, business.industry, Lymphocyte, medicine.medical_treatment, Nucleic acid test, Outbreak, General Medicine, Disease, 030204 cardiovascular system & hematology, medicine.disease, Gastroenterology, 03 medical and health sciences, 0302 clinical medicine, medicine.anatomical_structure, Internal medicine, Oxygen therapy, medicine, Original Article, Median body, 030212 general & internal medicine, Lymphocytopenia, business
Abstract

BACKGROUND: While the 2019 novel coronavirus disease (COVID-19) outbreak has been largely kept under control in China, it remains a global pandemic, and the source, transmission route, and treatments of SARS-COV-2 are still being investigated. Here, we summarized the clinical features, diagnosis, treatment, and prognosis of COVID-19 patients based on our clinical practice. METHODS: The clinical and imaging findings, treatments, and follow-up data of 471 patients with COVID-19 who were discharged from the Wuhan Jinyintan Hospital prior to February 6, 2020, were retrospectively analyzed. RESULTS: Among these patients, there were 2 mild cases, 282 moderate cases, 181 severe cases, and 6 critical cases. There were 250 males and 221 females aged 17 to 90 years. The median age was 54 years in the severe/critical group, which was significantly older than in the mild/moderate group (P

Published
2021

183. Clinical use of a machine learning histopathological image signature in diagnosis and survival prediction of clear cell renal cell carcinoma.

Author

Chen, Siteng, Zhang, Ning, Jiang, Liren, Gao, Feng, Shao, Jialiang, Wang, Tao, Zhang, Encheng, Yu, Hong, Wang, Xiang, and Zheng, Junhua

Subjects
RENAL cell carcinoma, MACHINE learning, FORECASTING, KIDNEY tumors, RENAL cancer
Abstract

Due to the complicated histopathological characteristics of renal neoplasms, traditional distinguishing of clear cell renal cell carcinoma (ccRCC) by naked eyes of experienced pathologist remains labor intensive and time consuming. Here, we extracted quantitative features of hematoxylin‐eosin‐stained images using CellProfiler and performed machine learning method to develop and verify a novel computational recognition of digital pathology for diagnosis and prognosis of ccRCC patients in the training, test and external validation cohort. The diagnostic model based on digital pathology could accurately distinguish ccRCC from normal renal tissues, with area under the curve (AUC) of 96.0%, 94.5% and 87.6% in the training, test and external validation cohorts, respectively. It could also accurately distinguish ccRCC from other pathological types of renal cancer, with AUC values of 97.0% and 81.4% in the Cancer Genome Atlas (TCGA) cohort and General cohort. We next developed and verified a computational recognition prognosis model with risk score. There was a significant difference in disease‐free survival comparing patients with high vs low risk score in training cohort (hazard ratio = 2.72, P <.0001) and validation cohort (hazard ratio = 9.50, P =.0091). The integrated nomogram based on our computational recognition risk score and clinicopathologic factors demonstrated excellent survival prediction for ccRCC patients, with increased accuracy by 6.6% in patients from Shanghai General Hospital and by 2.5% in patients from TCGA cohort when compared to current tumor stages/grade systems. These results indicate the potential clinical use of our machine learning histopathological image signature in diagnosis and survival prediction of ccRCC. What's new? Traditional microscopic histopathology to distinguish malignant neoplasms in clear cell renal cell carcinoma (ccRCC) struggles to keep pace with diagnostic needs. Boosting diagnostic efficiency in ccRCC may be possible through the use of computerized image processing technology. Here, the authors extracted quantitative features from hematoxylin‐eosin stained images and used the features to construct ccRCC diagnostic and prognostic models based on computational recognition of digital pathology. A machine learning histopathological image signature derived from digital pathology demonstrated high accuracy in ccRCC diagnosis and survival prediction. The findings highlight the potential clinical utility of machine learning for histopathologic image analysis in ccRCC. [ABSTRACT FROM AUTHOR]

Published
2021
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186. Mixture modeling for industrial soft sensor application based on semi-supervised probabilistic PLS.

Author

Zheng, Junhua and Song, Zhihuan

Subjects
*EXPECTATION-maximization algorithms, *LEAST squares, *PARTIAL least squares regression, *DETECTORS, *MODEL railroads, *MIXTURES
Abstract

• The basic semi-supervised probabilistic PLS model is extended to the mixture form. • Expectation-Maximization algorithm is newly designed for model training. • Industrial soft sensor application is made based on the developed mixture model. • A detailed industrial case study is carried out for performance evaluation. Due to the difficulty in measuring key performance indices in the process, only a small portion of collected data may have values for both routinely recorded variables and key performance indices, while a large portion of data only has values for routinely recorded variables. In order to improve the performance of data-driven soft sensor modeling, the idea of semi-supervised learning is incorporated with the traditional partial least squares modeling method. Furthermore, the single semi-supervised model structure is extended to the mixture form, in order to handle more complex data characteristics. An efficient Expectation-Maximization algorithm is designed for model training. An industrial case study is presented for performance evaluation of the developed method, with a Bayesian inference approach developed for results integration of different local models. [ABSTRACT FROM AUTHOR]

Published
2019
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187. MiR-532-5p suppresses renal cancer cell proliferation by disrupting the ETS1-mediated positive feedback loop with the KRAS-NAP1L1/P-ERK axis

Author

Zhai, Wei, primary, Ma, Junjie, additional, Zhu, Rujian, additional, Xu, Chen, additional, Zhang, Jin, additional, Chen, Yonghui, additional, Chen, Zhiguo, additional, Gong, Dongkui, additional, Zheng, Jiayi, additional, Chen, Chen, additional, Li, Saiyang, additional, Li, Butang, additional, Huang, Yiran, additional, Xue, Wei, additional, and Zheng, Junhua, additional

Published
2018
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196. Clinical correlation between serum YKL-40 protein level and recurrence of non-muscle invasive bladder cancer

Author

Yan, Jiasheng, Shen, Peijun, Zheng, Junhua, and Liu, Min

Subjects
Original Article
Abstract

To explore the clinical correlation between serum YKL-40 protein level and recurrence of non-muscle invasive bladder cancer (NMIBC).Totally 76 NMIBC patients (including 34 patients with confirmed recurrence and 42 patients without recurrence during the 2-year post-operative follow-up) and 31 healthy volunteers were recruited in this study. Blood samples were collected early in the morning, and serum YKL-40 protein levels of all these patients were analysed by using enzyme-linked immunosorbent assay (ELISA).Serum YKL-40 protein levels were significantly higher in NMIBC patients than in healthy controls (P0.001). Meanwhile, serum YKL-40 protein levels were found to be significantly higher in patients with recurrent NMIBC than those without tumor recurrence (P=0.001).Serum YKL-40 protein can be a reliable molecular marker for the clinical diagnosis of NMIBC recurrence. In particular, it can inform the post-operative management.

Published
2015

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