Background Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 infection, has been spreading globally. We aimed to develop a clinical model to predict the outcome of patients with severe COVID-19 infection early. Methods Demographic, clinical and first laboratory findings after admission of 183 patients with severe COVID-19 infection (115 survivors and 68 non-survivors from the Sino-French New City Branch of Tongji Hospital, Wuhan) were used to develop the predictive models. Machine learning approaches were used to select the features and predict the patients’ outcomes. The area under the receiver operating characteristic curve (AUROC) was applied to compare the models’ performance. A total of 64 with severe COVID-19 infection from the Optical Valley Branch of Tongji Hospital, Wuhan, were used to externally validate the final predictive model. Results The baseline characteristics and laboratory tests were significantly different between the survivors and non-survivors. Four variables (age, high-sensitivity C-reactive protein level, lymphocyte count and d-dimer level) were selected by all five models. Given the similar performance among the models, the logistic regression model was selected as the final predictive model because of its simplicity and interpretability. The AUROCs of the external validation sets were 0.881. The sensitivity and specificity were 0.839 and 0.794 for the validation set, when using a probability of death of 50% as the cutoff. Risk score based on the selected variables can be used to assess the mortality risk. The predictive model is available at [https://phenomics.fudan.edu.cn/risk_scores/]. Conclusions Age, high-sensitivity C-reactive protein level, lymphocyte count and d-dimer level of COVID-19 patients at admission are informative for the patients’ outcomes.

ObjectiveTo describe the prevalence and variations of non-alcoholic fatty liver disease/non-alcoholic steatohepatitis (NAFLD/NASH) among children and adolescents (CADs) and young adults (YADs).DesignA population-based observational study.SettingAnnual cases and prevalence of NAFLD/NASH from 1990 to 2017, by sex, region and country were collected from the Global Burden of Disease database.Main outcome measuresThe estimated annual percentage change, which was calculated by a regression line, was used to quantify the temporal trends in NAFLD/NASH burden among young people at the global, regional and national levels.ResultsGlobally, NAFLD/NASH incidence increased from 19.34 million in 1990 to 29.49 million in 2017 among CADs, with an annual increase of 1.35%. Additionally, in YADs, the number of cases and NAFLD/NASH prevalence significantly increased during this period, independent of sex and region. The greatest NAFLD/NASH increase was in North Africa and the Middle East. Almost all countries showed an increasing trend from 1990 to 2017, with the most pronounced increase observed in the developed regions.ConclusionsThe epidemiology of NAFLD/NASH in young people has changed considerably over the last three decades. Both the prevalence and number of cases have increased irrespective of sex, age and region. This phenomenon can result in a predictable increase in chronic liver disease burden in the near future. Understanding the prevalence of NAFLD/NASH and its variations is of paramount importance to develop strategies to implement public health policy.

Death prediction of COVID-19 patients specificity were 0.892 and 0.687 for the derivation set and 0.839 and 0.794 for the validation set, respectively, when using a probability of death of 50% as the cutoff. The individual risk score based on the four selected variables and the corresponding probability of death can serve as indexes to assess the mortality risk of COVID-19 patients. The predictive model is freely available at https://phenomics.fudan.edu.cn/risk_scores/. ConclusionsAge, high-sensitivity C-reactive protein level, lymphocyte count, and d-dimer level of COVID-19 patients at admission are informative for the patients' outcomes.

Men who have sex with men (MSM) represent one of the major risk groups for HIV-1 infection in China, and the predominant subtypes among this population has changed over the last two decades. The objective of this study was to determine the evolutionary characteristics and transmission patterns of the dominant HIV-1 strains in the Chinese MSM population. Methods: A total of 4980 published HIV-1 polgene sequences from MSM in China were retrieved and comprehensive evolutionary and transmission analyses were then conducted. Bayesian coalescent-based methods and selection pressure analyses were used to reconstruct the time-scale and demographic history and to estimate other evolutionary parameters. Transmission patterns were characterized using network analyses. Results: There were 2546 (51.12%) CRF01_AE, 1263 (25.36%) CRF07_BC, and 623 (12.51%) subtype B, accounting for 88.99% of the total sequences. From 2000 to 2016, the prevalence of CRF01_AE was stable, comprising nearly half of all sequences over time (58.33-45.38%, p = 0.071). CRF07_BC increased slightly from 13.3% to 22.49% (p < 0.001), while subtype B decreased dramatically from 41.67% to 9.04% (p < 0.001). Demographic reconstruction showed that the greatest expansion of the HIV epidemic occurred between 1999 and 2005. CRF01_AE had a higher estimated evolutionary rate (2.97 Â 10 À3 substitutions/site/year) and exhibited more sites under positive selection (25/351 codons) compared to the other subtypes. Network analyses showed that CRF07_BC (68.29%, 84/123) had a higher proportion of cross-region networks than CRF01_AE (49.1%, 174/354) and subtype B (36.46%, 35/96) (p < 0.001). Conclusions: The predominant subtypes of HIV-1 in Chinese MSM have different evolutionary characteristics and transmission patterns, which poses a significant challenge to HIV treatment and disease prevention.

HIV with transmitted drug-resistance (TDR) limits the therapeutic options available for treatment-naive HIV patients. This study aimed to further our understanding of the prevalence and transmission characteristics of HIV with TDR for the application of rst-line antiretroviral regimens. A total of 6578 HIV-1 protease/reverse-transcriptase sequences from treatment-naive individuals in China between 2000 and 2016, were obtained from the Los Alamos HIV Sequence Database and were analyzed for TDR. Transmission networks were constructed to determine genetic relationships. The spreading routes of large TDR clusters were identi ed using a Bayesian phylogeographic framework. TDR mutations were detected in 274 (4.51%) individuals, with 1.40% harboring TDR to nucleoside reverse transcriptase inhibitors, 1.52% to non-nucleoside reverse transcriptase inhibitors, and 1.87% to protease inhibitors. The most frequent mutation was M46L (58, 0.89%), followed by K103N (36, 0.55%), M46I (36, 0.55%), and M184V (26, 0.40%). The prevalence of total TDR initially decreased between 2000 and 2010 (OR = 0.83, 95% CI 0.73-0.95), and then increased thereafter (OR = 1.50, 95% CI 1.13-1.97). The proportion of sequences in a cluster (clustering rate) among HIV with TDR sequences was lower than that of sequences without TDR (40.5% vs. 48.8%, P = 0.023) and increased from 27.3% in 2005-2006 to 63.6% in 2015-2016 (P < 0.001). While most TDR mutations were associated with reduced relative transmission tness, mutation M46I was associated with higher relative transmission tness than the wild-type strain.This study identi ed a low-level prevalence of TDR HIV in China during the last two decades. However, the increasing TDR HIV rate sicn 2010, the persistent circulation of drug resistance mutations, and the expansion of self-sustaining drug resistance reservoirs may compromise the e cacy of antiretroviral therapy programs.