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 Cerebrovascular disease ■ Atherosclerosis ■ Stroke ■ Arteriovenous malformation Conclusions and perspectives

Despite recent progress in our understanding of the association between the gut microbiome and colorectal cancer (CRC), multi-kingdom gut microbiome dysbiosis in CRC across cohorts is unexplored. We investigated four-kingdom microbiota alterations using CRC metagenomic datasets of 1,368 samples from 8 distinct geographical cohorts. Integrated analysis identified 20 archaeal, 27 bacterial, 20 fungal and 21 viral species for each single-kingdom diagnostic model. However, our data revealed superior diagnostic accuracy for models constructed with multi-kingdom markers, in particular the addition of fungal species. Specifically, 16 multi-kingdom markers including 11 bacterial, 4 fungal and 1 archaeal feature, achieved good performance in diagnosing patients with CRC (area under the receiver operating characteristic curve (AUROC) = 0.83) and maintained accuracy across 3 independent cohorts. Coabundance analysis of the ecological network revealed associations between bacterial and fungal species, such as Talaromyces islandicus and Clostridium saccharobutylicum. Using metagenome shotgun sequencing data, the predictive power of the microbial functional potential was explored and elevated D-amino acid metabolism and butanoate metabolism were observed in CRC. Interestingly, the diagnostic model based on functional EggNOG genes achieved high accuracy (AUROC = 0.86). Collectively, our findings uncovered CRC-associated microbiota common across cohorts and demonstrate the applicability of multi-kingdom and functional markers as CRC diagnostic tools and, potentially, as therapeutic targets for the treatment of CRC.

Objective China concentrates a large part of the global burden of HBV infection, playing a pivotal role in achieving the WHO 2030 global hepatitis elimination target. Methods We searched for studies reporting HBV surface antigen (HBsAg) seroprevalence in five databases until January 2023. Eligible data were pooled using a generalised linear mixed model with random effects to obtain summary HBsAg seroprevalence. Linear regression was used to estimate annual percentage change (APC) and HBsAg prevalence in 2021. Results 3740 studies, including 231 million subjects, were meta-analysed. HBsAg seroprevalence for the general population decreased from 9.6% (95% CI 8.4 to 10.9%) in 1973–1984 to 3.0% (95% CI 2.1 to 3.9%) in 2021 (APC=−3.77; p<0.0001). Decreases were more pronounced in children <5 years (APC=−7.72; p<0.0001) and 5–18 years (−7.58; p<0.0001), than in people aged 19–59 years (−2.44; p<0.0001), whereas HBsAg seroprevalence increased in persons ≥60 years (2.84; p=0.0007). Significant decreases were observed in all six major Chinese regions, in both men (APC=−3.90; p<0.0001) and women (−1.82; p<0.0001) and in high-risk populations. An estimated 43.3 million (95% uncertainty interval 30.7–55.9) persons remained infected with HBV in China in 2021 (3.0%), with notable heterogeneity by region (6% in Taiwan and Hong Kong) and age (0.3%, 1.0%, 4.7% and 5.6% for <5 years, 5–18 years, 19–59 years and ≥60 years, respectively). Conclusions China has experienced remarkable decreases in HBV infection over the last four decades, but variations in HBsAg prevalence persist in subpopulations. Ongoing prevention of HBV transmission is needed to meet HBV elimination targets by 2030. Trial registration number PROSPERO (CRD42021284217)

Background: The incidence and mortality of esophageal cancer are high, with 5.90 new cases and 5.48 deaths per 100 000 people worldwide in 2017. The prognosis of esophageal cancer is poor, with an overall 5‐year survival rate of less than 20%. Esophageal cancer in different geographical locations has different etiologies, and the incidence and mortality of esophageal cancer continue to rise in some regions.Methods: We collected incidence and mortality data by age and gender for 195 countries and territories from 1990 to 2017 in the Global Burden of Disease (GBD) database. And we used these data to calculate the estimated annual percentage change (EAPC) to quantify trends in morbidity and mortality. Then we analyzed the gender‐ and age‐specific incidence and mortality in esophageal cancer to targeted high‐risk populations. Finally, we analyzed the correlation between the age‐standardized mortality rate (ASMR) and both the EAPC and social‐demographic index (SDI), and we calculated the Pearson correlation coefficient.Results: We found that Malawi, East Asia, and high‐middle SDI regions had the highest age‐standardized incidence rate (ASIR) and ASMR, and the ASIR and ASMR in western Sub‐Saharan Africa showed an upward trend. Our study also showed that the incidence and mortality in esophageal cancer were highest in men and in the 70+ years age group, and they presented a decreasing trend in most regions, but the 15‐49 years age groups in Australasia, Caribbean, and Oceania and the 70+ years age group in High‐Income North America, Oceania and high‐SDI regions presented an increasing trend. There were significant negative associations between ASMR at baseline and EAPC and between ASMR and SDI in 2017.Conclusion: By analyzing the global distribution of incidence and mortality in esophageal cancer, trends over time, and gender and age specificity, we can understand the heterogeneity of its global trends. This heterogeneity can help us to identify high‐risk groupsand to provide clues for the exploration of the etiology and early prevention of the disease.

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.