Aims Chinese men who have sex with men (MSM) are at high risk for depression, anxiety and suicide. The estimated prevalence of these problems is essential to guide public health policy, but published results vary. This meta-analysis aimed to estimate the prevalence of depressive symptoms, anxiety symptoms and suicide among Chinese MSM. Methods Systematic searches of EMBASE, MEDLINE, PsycINFO, PubMed, CNKI and Wanfang databases with languages restricted to Chinese and English for studies published before 10 September 2019 on the prevalence of depressive symptoms, anxiety symptoms, suicidal ideation, suicide plans and suicide attempts among Chinese MSM. Studies that were published in the peer-reviewed journals and used validated instruments to assess depression and anxiety were included. The characteristics of studies and the prevalence of depression and anxiety symptoms, suicidal ideation, suicide plans and suicide attempts were independently extracted by authors. Random-effects modelling was used to estimate the pooled rates. Subgroup analysis and univariate meta-regression were conducted to explore potential sources of heterogeneity. This study followed the PRISMA and MOOSE. Results Sixty-seven studies were included. Fifty-two studies reported the prevalence of depressive symptoms, with a combined sample of 37 376 people, of whom 12 887 [43.2%; 95% confidence interval (CI), 38.9–47.5] reported depressive symptoms. Twenty-seven studies reported the prevalence of anxiety symptoms, with a combined sample of 10 531 people, of whom 3187 (32.2%; 95% CI, 28.3–36.6) reported anxiety symptoms. Twenty-three studies reported the prevalence of suicidal ideation, with a combined sample of 15 034 people, of whom 3416 (21.2%; 95% CI, 18.3–24.5) had suicidal ideation. Nine studies reported the prevalence of suicide plans, with a combined sample of 5271 people, of whom 401 (6.2%; 95% CI, 3.9–8.6) had suicide plans. Finally, 19 studies reported the prevalence of suicide attempts, with a combined sample of 27 936 people, of whom 1829 (7.3%; 95% CI, 5.6–9.0) had attempted suicide. Conclusions The mental health of Chinese MSM is poor compared with the general population. Efforts are warranted to develop interventions to prevent and alleviate mental health problems among this vulnerable population.

The effect of ultraviolet-B radiation (UV-B; 280-320 nm) on induction of nitric oxide was estimated in the suspensions of green alga Chlorella pyrenoidosa with or without the NO scavenger N-acetyl-L-cysteine, and reductants such as 1,4-dithiothreitol, glutathione (reduced form), and ascorbic acid. Exogenously added sodium nitroprusside (NO donor), glutathione, 1,4-dithiothreitol, and ascorbic acid were able to prevent chlorophyll loss mediated by UV-B. Addition of NO to algal suspensions irradiated by UV-B increased the activity of catalase and superoxide dismutase but lowered the activity of phenylalanine ammonia-lyase. UV-B thus appears to be a strong inducer of NO production, exogenously added NO and reductants protecting the green alga against UV-B-induced oxidative damage.

Retrosynthesis prediction is a crucial task for organic synthesis. In this work, we propose a template-free and Transformer-based method dubbed RetroPrime, integrating chemists’ retrosynthetic strategy of (1) decomposing a molecule into synthons then (2) generating reactants by attaching leaving groups. These two steps are accomplished with versatile Transformer models, respectively. While RetroPrime performs competitively against all state-of-the art models on the standard USPTO-50K dataset, it manifests remarkable generalizability and outperforms the only published result by a non-trivial margin of 4.8% for the Top-1 accuracy on the large-scale USPTO-full dataset. It is known that outputs of Transformer-based retrosynthesis model tend to suffer from insufficient diversity and high invalidity. These problems may limit the potential of Transformer-based methods in real practice, yet no prior works address both issues simultaneously. RetroPrime is designed to tackle these challenges. Finally, we provide convincing results to support the claim that RetromPrime can more effectively generalize across chemical space.

Many deep learning (DL)-based molecular generative models have been proposed to design novel molecules. These models may perform well on benchmarks, but they usually do not take real-world constraints into account, such as available training data set, synthetic accessibility, and scaffold diversity in drug discovery. In this study, a new algorithm, ChemistGA, was proposed by combining the traditional heuristic algorithm with DL, in which the crossover of the traditional genetic algorithm (GA) was redefined by DL in conjunction with GA, and an innovative backcrossing operation was implemented to generate desired molecules. Our results clearly show that ChemistGA not only retains the strength of the traditional GA but also greatly enhances the synthetic accessibility and success rate of the generated molecules with desired properties. Calculations on the two benchmarks illustrate that ChemistGA achieves impressive performance among the state-of-the-art baselines, and it opens a new avenue for the application of generative models to real-world drug discovery scenarios.