Jiegang Huang

Gut microbial dysbiosis contributes to alcoholic liver disease (ALD), but the pathogenic bacteria and mechanisms remain unclear. We investigated the role of Streptococcus salivarius (S. salivarius) in ALD mice using a modified 12-week NIAAA chronic-plus-binge ethanol regimen with oral gavage. S. salivarius aggravated hepatic steatosis, inflammation, fibrosis, and serum ALT elevation, while impairing colonic mucus and tight-junction integrity. Viable bacteria translocated from the gut to the liver. Integrated hepatic metabolomics and transcriptomics revealed disturbances in bile acid, glycerophospholipid, and choline metabolism and upregulation of Tnfrsf12a, encoding Fn14. S. salivarius activated NF-κB signaling without altering TLR2 or TLR4 abundance. Fn14 silencing or NF-κB inhibition attenuated the inflammatory response in vitro. Thus, our findings suggest that S. salivarius may contribute to ALD progression by impairing the intestinal barrier, translocating to the liver, and activating Fn14-dependent NF-κB signaling. The gut–liver translocation pathway and Fn14/NF-κB axis may represent therapeutic targets for ALD.

Background: Liver cirrhosis (LC) is a chronic liver disease with global prevalence. Current diagnostic methods for LC still face limitations in safety and accessibility. We aimed to develop an interpretable machine learning (ML) prediction model for LC using gut microbes and deploy it as a web-based clinical decision support tool. Methods: Data were retrieved from PubMed and BioProject databases. Bioinformatics re-analysis and discriminant analysis effect size (LEfSe) analysis was conducted to preliminarily identify key genera associated with LC. Further feature selection was performed using Least Absolute Shrinkage and Selection Operator (LASSO) regression. The independent datasets were combined to form an integrated dataset, which was then subjected to five-fold cross-validation and leave-one-dataset-out (LODO) analysis. Model performance was evaluated using metrics such as the area under the receiver operating characteristic curve (AUC), and the optimal model was selected. The decision mechanism of the optimal model was interpreted using SHapley Additive exPlanations (SHAP), and the model was deployed as a web application using the Streamlit framework. Results: We ultimately included 11 datasets related to LC. The genera Veillonella , Lachnospira , Romboutsia , Akkermansia , Erysipelatoclostridium , Prevotella , UCG.005 , and Streptococcus were identified as key predictors distinguishing LC patients from healthy controls. The Random Forest (RF) model demonstrated the best predictive performance (AUC in five-fold cross-validation: 0.875, 95% CI: 0.823–0.905; AUC in LODO analysis: 0.793, 95% CI: 0.702–0.940) and was deployed as an online LC prediction tool. Conclusion: The interpretable RF model, along with its web-based implementation, has the potential to provide decision support for healthcare professionals and shows promise as a valuable auxiliary tool for LC screening and early clinical intervention.

Background: Social media intervention may enhance HIV prevention among men who have sex with men, but the effect of this intervention in resource-limited settings remains unclear. Objective: This randomized controlled trial evaluated whether a social media intervention grounded in the information-motivation-behavioral skills (IMB) model could be beneficial for HIV prevention among men who have sex with men in resource-limited settings. Methods: Participants were recruited in Nanning, China, between April 2023 and April 2024. Eligible participants were randomly assigned to either the social media intervention group or the routine HIV prevention services control group. Participants in the intervention group received a 3-month social media intervention, which included completing video-based tasks. Baseline surveys were conducted, followed by follow-up surveys every 3 months, for a total of 2 follow-ups. Outcomes included HIV testing uptake, high-risk behavior, AIDS-related knowledge, safe sex self-efficacy, and attitude. Results: A total of 180 eligible men who have sex with men were enrolled (90 per group). Follow-up rates were 97.8% (88/90) and 95.5% (86/90) for the intervention and control groups, respectively. At the follow-ups, the intervention group demonstrated significantly higher uptake of HIV testing, a lower proportion of participants reporting high-risk sexual behaviors, and higher condom use self-efficacy compared to the control group (all P <.05). After controlling for sociodemographic variables, generalized estimating equations analysis revealed that the intervention group had significantly higher odds of HIV testing (risk ratio [RR] 1.739, 95% CI 1.110‐2.730), HIV self-testing (RR 2.306, 95% CI 1.593‐3.340), and consistent condom use (RR 2.457, 95% CI 1.636‐3.690) than the control group. Cochran-Armitage trend tests within the intervention group revealed that with increasing intervention duration, both HIV testing and HIV self-testing significantly increased, while high-risk sexual behaviors significantly decreased (all P <.05). Conclusions: The social media intervention guided by the IMB model demonstrated a positive effect on expanding HIV testing coverage, reducing high-risk behavior, enhancing AIDS-related knowledge, and improving safer sex self-efficacy among men who have sex with men in resource-limited settings. These findings provide valuable guidance for future HIV prevention and control efforts targeting this population.

Background: People living with HIV(PLWH) are a high-risk population for cancer. We conducted a pioneering study on the gut microbiota of PLWH with various types of cancer, revealing key microbiota.Methods: We collected stool samples from 54 PLWH who have cancer (PLWH-C), including Kaposi’s sarcoma (KS, n=7), lymphoma (L, n=22), lung cancer (LC, n=12), and colorectal cancer (CRC, n=13), 55 PLWH who do not have cancer (PLWH-NC), and 49 people living without HIV (Ctrl). The gut microbiota in fecal samples was analyzed using 16S rRNA sequencing. We compared the microbial diversity among groups and identified key microbiota and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways using random forest. Furthermore, we analyzed the correlation between microbiota and KEGG pathways and constructed microbiota Receiver Operating Characteristic (ROC) diagnostic models.Results: Compared with PLWH-NC and Ctrl, PLWH with any type of cancer exhibited significantly lower alpha diversity and significant alterations in beta diversity of the gut microbiota. The significantly decreased abundance of Bacteroides and Bacteroides vulgatus in PLWH-C showed a negative correlation with the Pathways in cancer pathway, and a positive correlation with Choline metabolism in cancer, Central carbon metabolism in cancer, and Proteoglycans in cancer pathways. Bacteroides (AUC≥0.84) and Bacteroides vulgatus (AUC≥0.78) exhibited discriminatory diagnostic capabilities for PLWH-C in patients with different cancers compared with PLWH-NC and Ctrl.Discussion: We confirmed a more severe dysbiosis of the gut microbiota in PLWH with KS, L, LC, or CRC. Bacteroides may be associated with disruptions in cancer-related metabolic pathways and serve as diagnostic biomarkers for PLWH with various cancers.

Cigarette smoking among people with HIV is a leading preventable cause of morbidity and mortality, particularly in China given its immense burden of smoking. This pilot study aimed to evaluate the feasibility, acceptability, and efficacy on smoking cessation of a community-informed WeChat-based messaging and behavioral counseling intervention for people with HIV who smoke called Quit for Life. The study design was a 2-group randomized clinical trial. Data collection occurred between February 2022 and August 2023 within a tertiary infectious disease hospital in Nanning city, Guangxi, China. Participants included adults with HIV who smoked, were willing to set a quit date, and received HIV care at the hospital. Participants were randomized to the 8-week Quit for Life intervention group (nicotine replacement therapy gum, self-help quitting smoking guide, behavioral counseling, and WeChat-based messaging), or the control group (nicotine replacement therapy gum and self-help guide only). Complete case analysis was performed. Of 219 people assessed for eligibility, 109 participants were randomized (mean [SD] age, 45.3 [15.1] years; men [96.3%]) and 98 completed the 12-week assessment (89.9% retention rate). At 12-week follow-up, the biochemically verified smoking cessation rate (primary outcome) was significantly higher in the intervention group compared to the control group (59.1% abstinence vs. 25.6%, adjusted odds ratio 5.3 [1.5,19.2]. Implementation and feasibility metrics indicated most participants receiving the intervention as intended gave high ratings of the usefulness of counseling sessions and WeChat-based messaging. Given these findings, subsequent studies should investigate implementation and scale-up of this intervention for people with HIV in China. Clinical trial registration details: Trial registration number: ClinicalTrials.gov Identifier: NCT05020899. Date of trial registration: 8-19-2021.

Hepatitis C virus (HCV) has become a serious global public health issue. Existing study has confirmed the potential role of internet search data in the prediction of infectious diseases. This study aims to explore predictive models suitable for HCV, providing references and recommendations for actively responding to the World Health Organization’s strategy to eliminate HCV by 2030. We collected the publicly available daily reported case numbers of HCV and the daily search volume of HCV-related keywords in Baidu Search Index (BSI) from January 2011 to September 2023. Identify keywords highly correlated with HCV using Spearman rank correlation and time series cross-correlation analysis to construct a comprehensive search index. Finally, seasonal autoregressive integrated moving average (SARIMA) and seasonal autoregressive integrated moving average with exogenous variables (SARIMAX) models were developed based on monthly HCV case numbers and the comprehensive search index (CSI). The performance of these predictive models was evaluated using mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE). From January 2011 to September 2023, China reported a cumulative total of 2,949,160 HCV cases, with a monthly average case number of 19,276. The incidence of HCV is highest in March each year, with a slight decrease in February, exhibiting a seasonal cycle pattern. Influenced by the COVID-19 pandemic, the incidence of HCV showed a notable decrease in 2020 and 2022, but overall, it has been on an upward trend. After correlation analysis, a total of five keywords were included, with a potential time lag of 0 months for the keywords. The selected SARIMA(0,1,1)(2,1,1)(12) + CSI(Lag = 0) model produced lower errors in both fitting and forecasting than the SARIMA(0,1,1)(2,1,1)(12) model: MAE (706.7052 versus 759.1066 and 1754.05 versus 3940.86), RMSE (973.3811 versus 1123.343 and 2733.02 versus 4846.49), MAPE (3.72% versus 4.08% and 0.12% versus 0.24%). The findings suggest that the SARIMA(0,1,1)(2,1,1)(12) + CSI(Lag = 0) model exhibits superior predictive performance, maintaining accuracy even when faced with the public health emergency of COVID-19. The proposed SARIMAX model in the study will provide more robust support for achieving the 2030 HCV dynamic zero strategy.

Background and Aims: To study the unknown influencing factors of delayed medical treatment behavior in oral cancer patients in western China and to develop a prediction model on the risk of delayed medical treatment in oral cancer patients. Method: We investigated oral cancer patients attending a tertiary Grade A dental hospital in western China from June 2022 to July 2023. The logistic regression and four machine learning models (nearest neighbors, the RBF SVM, random forest, and QDA) were used to identify risk factors and establish a risk prediction model. We used the established model to predict the data before and after the COVID-19 pandemic and test whether the prediction effect can still remain stable and accurate under the interference of COVID-19. Result: Out of the 495 patients included in the study, 122 patients (58.65%) delayed seeking medical treatment before the lifting of the restrictions of the pandemic, while 153 patients (53.13%) did so after the lifting of restrictions. The logistic regression model revealed that living with adult children was a protective factor for patients in delaying seeking medical attention, regardless of the implementation of pandemic control measures. After comparing each model, it was found that the statistical indicators of the random forest algorithm such as the AUC score (0.8380) and specificity (0.8077) ranked first, with the best prediction performance and stable performance. Conclusions: This study systematically elucidates the critical factors influencing patient delay behavior in oral cancer diagnosis and treatment, employing a comprehensive risk prediction model that accurately identifies individuals at an elevated risk of delay. It represents a pioneering large-scale investigation conducted in western China, focusing explicitly on the multifaceted factors affecting the delayed medical treatment behavior of oral cancer patients. The findings underscore the imperative of implementing early intervention strategies tailored to mitigate these delays. Furthermore, this study emphasizes the pivotal role of robust social support systems and positive family dynamics in facilitating timely access to healthcare services for oral cancer patients, thereby potentially improving outcomes and survival rates.

SHP-1, a nonreceptor protein tyrosine phosphatase encoded by ptpn6, has been regarded as a regulatory protein of hematopoietic cell biology for years. However, there is now increasing evidence to support its role in tumors. Thus, the role of ptpn6 for prognosis and immune regulation across 33 tumors was investigated, aiming to explore its functional heterogeneity and clinical significance in pan-cancer. Differential expression of ptpn6 was found between cancer and adjacent normal tissues, and its expression was significantly correlated with the prognosis of tumor patients. In most cancers, ptpn6 expression was significantly associated with immune infiltration. This was further confirmed by ptpn6-related genes/proteins enrichment analysis. Additionally, genetic alterations in ptpn6 was observed in most cancers. As for epigenetic changes, it’s phosphorylation levels significantly altered in 6 tumors, while methylation levels significantly altered in 12 tumors. Notably, the methylation levels of ptpn6 were significantly decreased in 11 tumors, accompanied by its increased expression in 8 of them, suggesting that the hypomethylation may be related to its increased expression. Our results show that ptpn6 plays a specific role in tumor immunity and exerts a pleiotropic effect in a variety of tumors. It can serve as a prognostic factor for some cancers. Especially in LGG, KIRC, UCS and TGCT, the increased expression of ptpn6 is associated with poor prognosis and high immune infiltration. This aids in understanding the role of ptpn6 in tumor biology, and can provide insight into presenting a potential biomarker for poor prognosis and immune infiltration in cancers.