There is increasing evidence showing that the dynamic changes in the gut microbiota can alter brain physiology and behavior. Cognition was originally thought to be regulated only by the central nervous system. However, it is now becoming clear that many non-nervous system factors, including the gut-resident bacteria of the gastrointestinal tract, regulate and influence cognitive dysfunction as well as the process of neurodegeneration and cerebrovascular diseases. Extrinsic and intrinsic factors including dietary habits can regulate the composition of the microbiota. Microbes release metabolites and microbiota-derived molecules to further trigger host-derived cytokines and inflammation in the central nervous system, which contribute greatly to the pathogenesis of host brain disorders such as pain, depression, anxiety, autism, Alzheimer's diseases, Parkinson's disease, and stroke. Change of blood-brain barrier permeability, brain vascular physiology, and brain structure are among the most critical causes of the development of downstream neurological dysfunction. In this review, we will discuss the following parts: Overview of technical approaches used in gut microbiome studies Microbiota and immunity Gut microbiota and metabolites Microbiota-induced blood-brain barrier dysfunction Neuropsychiatric diseases ■ Stress and depression ■ Pain and migraine ■ Autism spectrum disorders Neurodegenerative diseases ■ Parkinson's disease ■ Alzheimer's disease ■ Amyotrophic lateral sclerosis ■ Multiple sclerosis

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.

Background Predictions of primary liver cancer (PLC) incidence rates and case numbers are critical to understand and plan for PLC disease burden. Methods Data on PLC incidence rates and case numbers from 1990 to 2017 were retrieved from the Global Burden of Disease database. The estimated average percentage change (EAPC) was calculated to quantify the trends of PLC age-standardized incidence rates (ASRs). Bayesian age-period-cohort models were constructed to project PLC incidence rates and case numbers through 2030. Results Globally, the PLC case number doubled from 472 300 in 1990 to 953 100 in 2017. The case number will further increase to 1 571 200 in 2030, and the ASR will increase from 11.80 per 100 000 in 2018 to 14.08 per 100 000 in 2030. The most pronounced increases are observed in people afflicted by non-alcoholic steatohepatitis (NASH) and in older people. The trends of PLC incidence rates between 1990 and 2030 are heterogeneous among countries and can be summarized as five scenarios: (i) 46 countries that have and will continue to experience a persistent increase (e.g. Australia); (ii) 21 countries that experienced an initial decrease (or remained stable) but are predicted to increase (e.g. China); (iii) 7 countries that experienced an initial increase but are predicted to remain stable (e.g. USA); (iv) 29 countries that experienced an initial increase but are predicted to decrease (e.g. Egypt); and (v) 82 countries that have and will continue to experience a persistent decrease (e.g. Japan). Conclusion PLC incidence rates and case numbers are anticipated to increase at the global level through 2030. The increases in people afflicted by NASH and among older people suggest a dearth of attention for these populations in current prevention strategies and highlight their priority in future schedules for global control of PLC.

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.

China has the largest number of patients with dementia in the world. However, dementia in the Chinese population is still poorly understood and under-researched. Given the differences in genetic, demographic, sociocultural, lifestyle, and health profiles among Chinese and other ethnic/racial groups, it is crucial to build appropriate infrastructure for long-term longitudinal studies to advance Chinese cognitive aging and dementia research. We initiated a community-based prospective cohort-the Taizhou Imaging Study (TIS)-to accelerate the understanding of dementia and cerebrovascular diseases in Chinese. This article presents the rationale, aims, study design, and organization of TIS. In addition, we described some examples of the types of studies such a resource might support. The TIS provides a new framework for facilitating Chinese dementia research, encompassing invaluable resources including detailed epidemiological, sociocultural, neuroimaging, and omics data.