Identification of the shared hub gene signatures and molecular mechanisms between HIV-1 and pulmonary arterial hypertension.

The close link between HIV-1 infection and the occurrence of pulmonary arterial hypertension (PAH). However, the underlying molecular mechanisms of their interrelation remain unclear. The microarray data of HIV-1 and PAH were downloaded from GEO database. We utilized WGCNA to identify shared genes between HIV-1 and PAH, followed by conducting GO and pathway enrichment analyses. Subsequently, differentially genes analysis was performed using external validation datasets to further filter hub genes. Immunoinfiltration analysis was performed using CIBERSORT. Finally, hub gene expression was validated using scRNA-seq data. We identified 109 shared genes through WGCNA, primarily enriched in type I interferon (IFN) pathways. By taking the intersection of WGCNA important module genes and DEGs, ISG15 and IFI27 were identified as pivotal hub genes. Immunoinfiltration analysis and scRNA-seq results indicated the significant role of monocytes in the shared molecular mechanisms of HIV-1 and PAH. In summary, our study illustrated the possible mechanism of PAH secondary to HIV-1 and showed that the heightened IFN response in HIV-1 might be a crucial susceptibility factor for PAH, with monocytes being pivotal cells involved in the type I IFN response pathway. This provides potential new insights for further investigating the molecular mechanisms connecting HIV-1 and PAH.

Dysbiosis of gut microbiota and metabolites during AIDS: implications for CD4+ T cell reduction and immune activation.

OBJECTIVE: Identifying the gut microbiota associated with host immunity in the AIDS stage. DESIGN: We performed a cross-sectional study. METHODS: We recruited people living with HIV (PLWH) in the AIDS or non-AIDS stage and evaluated their gut microbiota and metabolites by using 16S ribosomal RNA (rRNA) sequencing and liquid chromatography-mass spectrometry (LC-MS). Machine learning (ML) models were used to analyze the correlations between key bacteria and CD4+ T cell count, CD4+ T cell activation, bacterial translocation, gut metabolites, and KEGG functional pathways. RESULTS: We recruited 114 PLWH in the AIDS stage and 203 PLWH in the non-AIDS stage. The α-diversity of gut microbiota was downregulated in the AIDS stage (P < 0.05). Several ML models could be used to identify key gut microbiota associated with AIDS, including the logistic regression model with area under the curve (AUC), sensitivity, specificity, and Brier scores of 0.854, 0.813, 0.813, and 0.160, respectively. The key bacteria ASV1 (Bacteroides sp.), ASV8 (Fusobacterium sp.), ASV30 (Roseburia sp.), ASV37 (Bacteroides sp.), and ASV41 (Lactobacillus sp.) decreased in the AIDS stage and were positively correlated with the CD4+ T cell count, the EndoCAb IgM level, 4-hydroxyphenylpyruvic acid abundance, and the predicted cell growth pathway were negatively correlated with the CD3+CD4+CD38+HLA-DR+ T cell count and the sCD14 level. CONCLUSIONS: ML has the potential to recognize key gut microbiota related to AIDS. The key five bacteria in the AIDS stage and their metabolites might be related to CD4+ T cell reduction and immune activation.

Human microbiota dysbiosis after SARS-CoV-2 infection have the potential to predict disease prognosis.

BACKGROUND: The studies on SARS-CoV-2 and human microbiota have yielded inconsistent results regarding microbiota α-diversity and key microbiota. To address these issues and explore the predictive ability of human microbiota for the prognosis of SARS-CoV-2 infection, we conducted a reanalysis of existing studies. METHODS: We reviewed the existing studies on SARS-CoV-2 and human microbiota in the Pubmed and Bioproject databases (from inception through October 29, 2021) and extracted the available raw 16S rRNA sequencing data of human microbiota. Firstly, we used meta-analysis and bioinformatics methods to reanalyze the raw data and evaluate the impact of SARS-CoV-2 on human microbial α-diversity. Secondly, machine learning (ML) was employed to assess the ability of microbiota to predict the prognosis of SARS-CoV-2 infection. Finally, we aimed to identify the key microbiota associated with SARS-CoV-2 infection. RESULTS: A total of 20 studies related to SARS-CoV-2 and human microbiota were included, involving gut (n = 9), respiratory (n = 11), oral (n = 3), and skin (n = 1) microbiota. Meta-analysis showed that in gut studies, when limiting factors were studies ruled out the effect of antibiotics, cross-sectional and case-control studies, Chinese studies, American studies, and Illumina MiSeq sequencing studies, SARS-CoV-2 infection was associated with down-regulation of microbiota α-diversity (P < 0.05). In respiratory studies, SARS-CoV-2 infection was associated with down-regulation of α-diversity when the limiting factor was V4 sequencing region (P < 0.05). Additionally, the α-diversity of skin microbiota was down-regulated at multiple time points following SARS-CoV-2 infection (P < 0.05). However, no significant difference in oral microbiota α-diversity was observed after SARS-CoV-2 infection. ML models based on baseline respiratory (oropharynx) microbiota profiles exhibited the ability to predict outcomes (survival and death, Random Forest, AUC = 0.847, Sensitivity = 0.833, Specificity = 0.750) after SARS-CoV-2 infection. The shared differential Prevotella and Streptococcus in the gut, respiratory tract, and oral cavity was associated with the severity and recovery of SARS-CoV-2 infection. CONCLUSIONS: SARS-CoV-2 infection was related to the down-regulation of α-diversity in the human gut and respiratory microbiota. The respiratory microbiota had the potential to predict the prognosis of individuals infected with SARS-CoV-2. Prevotella and Streptococcus might be key microbiota in SARS-CoV-2 infection.

Predicting pulmonary tuberculosis incidence in China using Baidu search index: an ARIMAX model approach.

BACKGROUND: Existing researches have established a correlation between internet search data and the epidemics of numerous infectious diseases. This study aims to develop a prediction model to explore the relationship between the Pulmonary Tuberculosis (PTB) epidemic trend in China and the Baidu search index. METHODS: Collect the number of new cases of PTB in China from January 2011 to August 2022. Use Spearman rank correlation and interaction analysis to identify Baidu keywords related to PTB and construct a PTB comprehensive search index. Evaluate the predictive performance of autoregressive integrated moving average (ARIMA) and ARIMA with explanatory variable (ARIMAX) models for the number of PTB cases. RESULTS: Incidence of PTB had shown a fluctuating downward trend. The Spearman rank correlation coefficient between the PTB comprehensive search index and its incidence was 0.834 (P < 0.001). The ARIMA model had an AIC value of 2804.41, and the MAPE value was 13.19%. The ARIMAX model incorporating the Baidu index demonstrated an AIC value of 2761.58 and a MAPE value of 5.33%. CONCLUSIONS: The ARIMAX model is superior to ARIMA in terms of fitting and predicting accuracy. Additionally, the use of Baidu Index has proven to be effective in predicting cases of PTB.

Early Warning and Prediction of Scarlet Fever in China Using the Baidu Search Index and Autoregressive Integrated Moving Average With Explanatory Variable (ARIMAX) Model: Time Series Analysis.

BACKGROUND: Internet-derived data and the autoregressive integrated moving average (ARIMA) and ARIMA with explanatory variable (ARIMAX) models are extensively used for infectious disease surveillance. However, the effectiveness of the Baidu search index (BSI) in predicting the incidence of scarlet fever remains uncertain. OBJECTIVE: Our objective was to investigate whether a low-cost BSI monitoring system could potentially function as a valuable complement to traditional scarlet fever surveillance in China. METHODS: ARIMA and ARIMAX models were developed to predict the incidence of scarlet fever in China using data from the National Health Commission of the People's Republic of China between January 2011 and August 2022. The procedures included establishing a keyword database, keyword selection and filtering through Spearman rank correlation and cross-correlation analyses, construction of the scarlet fever comprehensive search index (CSI), modeling with the training sets, predicting with the testing sets, and comparing the prediction performances. RESULTS: The average monthly incidence of scarlet fever was 4462.17 (SD 3011.75) cases, and annual incidence exhibited an upward trend until 2019. The keyword database contained 52 keywords, but only 6 highly relevant ones were selected for modeling. A high Spearman rank correlation was observed between the scarlet fever reported cases and the scarlet fever CSI (rs=0.881). We developed the ARIMA(4,0,0)(0,1,2)(12) model, and the ARIMA(4,0,0)(0,1,2)(12) + CSI (Lag=0) and ARIMAX(1,0,2)(2,0,0)(12) models were combined with the BSI. The 3 models had a good fit and passed the residuals Ljung-Box test. The ARIMA(4,0,0)(0,1,2)(12), ARIMA(4,0,0)(0,1,2)(12) + CSI (Lag=0), and ARIMAX(1,0,2)(2,0,0)(12) models demonstrated favorable predictive capabilities, with mean absolute errors of 1692.16 (95% CI 584.88-2799.44), 1067.89 (95% CI 402.02-1733.76), and 639.75 (95% CI 188.12-1091.38), respectively; root mean squared errors of 2036.92 (95% CI 929.64-3144.20), 1224.92 (95% CI 559.04-1890.79), and 830.80 (95% CI 379.17-1282.43), respectively; and mean absolute percentage errors of 4.33% (95% CI 0.54%-8.13%), 3.36% (95% CI -0.24% to 6.96%), and 2.16% (95% CI -0.69% to 5.00%), respectively. The ARIMAX models outperformed the ARIMA models and had better prediction performances with smaller values. CONCLUSIONS: This study demonstrated that the BSI can be used for the early warning and prediction of scarlet fever, serving as a valuable supplement to traditional surveillance systems.

Alterations of the gut microbiota associated with the occurrence and progression of viral hepatitis.

Background: Gut microbiota is the largest population of microorganisms and is closely related to health. Many studies have explored changes in gut microbiota in viral hepatitis. However, the correlation between gut microbiota and the occurrence and progression of viral hepatitis has not been fully clarified. Methods: PubMed and BioProject databases were searched for studies about viral hepatitis disease and 16S rRNA gene sequencing of gut microbiota up to January 2023. With bioinformatics analyses, we explored changes in microbial diversity of viral hepatitis, screened out crucial bacteria and microbial functions related to viral hepatitis, and identified the potential microbial markers for predicting risks for the occurrence and progression of viral hepatitis based on ROC analysis. Results: Of the 1389 records identified, 13 studies met the inclusion criteria, with 950 individuals including 656 patient samples (HBV, n = 546; HCV, n = 86; HEV, n = 24) and 294 healthy controls. Gut microbial diversity is significantly decreased as the infection and progression of viral hepatitis. Alpha diversity and microbiota including Butyricimonas, Escherichia-Shigella, Lactobacillus, and Veillonella were identified as the potential microbial markers for predicting the risk of development of viral hepatitis (AUC>0.7). Microbial functions including tryptophan metabolism, fatty acid biosynthesis, lipopolysaccharide biosynthesis, and lipid metabolism related to the microbial community increased significantly as the development of viral hepatitis. Conclusions: This study demonstrated comprehensively the gut microbiota characteristics in viral hepatitis, screened out crucial microbial functions related to viral hepatitis, and identified the potential microbial markers for predicting the risk of viral hepatitis.

Using gut microbiota as a diagnostic tool for colorectal cancer: machine learning techniques reveal promising results.

Introduction. Increasing evidence suggests a correlation between gut microbiota and colorectal cancer (CRC).Hypothesis/Gap Statement. However, few studies have used gut microbiota as a diagnostic biomarker for CRC.Aim. The objective of this study was to explore whether a machine learning (ML) model based on gut microbiota could be used to diagnose CRC and identify key biomarkers in the model.Methodology. We sequenced the 16S rRNA gene from faecal samples of 38 participants, including 17 healthy subjects and 21 CRC patients. Eight supervised ML algorithms were used to diagnose CRC based on faecal microbiota operational taxonomic units (OTUs), and the models were evaluated in terms of identification, calibration and clinical practicality for optimal modelling parameters. Finally, the key gut microbiota was identified using the random forest (RF) algorithm.Results. We found that CRC was associated with the dysregulation of gut microbiota. Through a comprehensive evaluation of supervised ML algorithms, we found that different algorithms had significantly different prediction performance using faecal microbiomes. Different data screening methods played an important role in optimization of the prediction models. We found that naïve Bayes algorithms [NB, accuracy=0.917, area under the curve (AUC)=0.926], RF (accuracy=0.750, AUC=0.926) and logistic regression (LR, accuracy=0.750, AUC=0.889) had high predictive potential for CRC. Furthermore, important features in the model, namely s__metagenome_g__Lachnospiraceae_ND3007_group (AUC=0.814), s__Escherichia_coli_g__Escherichia-Shigella (AUC=0.784) and s__unclassified_g__Prevotella (AUC=0.750), could each be used as diagnostic biomarkers of CRC.Conclusions. Our results suggested an association between gut microbiota dysregulation and CRC, and demonstrated the feasibility of the gut microbiota to diagnose cancer. The bacteria s__metagenome_g__Lachnospiraceae_ND3007_group, s__Escherichia_coli_g__Escherichia-Shigella and s__unclassified_g__Prevotella were key biomarkers for CRC.

The role of gut microbiota in the occurrence and progression of non-alcoholic fatty liver disease.

Background: Non-alcoholic fatty liver disease (NAFLD) is the most prevalent cause of chronic liver disease worldwide, and gut microbes are associated with the development and progression of NAFLD. Despite numerous studies exploring the changes in gut microbes associated with NAFLD, there was no consistent pattern of changes. Method: We retrieved studies on the human fecal microbiota sequenced by 16S rRNA gene amplification associated with NAFLD from the NCBI database up to April 2023, and re-analyzed them using bioinformatic methods. Results: We finally screened 12 relevant studies related to NAFLD, which included a total of 1,189 study subjects (NAFLD, n = 654; healthy control, n = 398; obesity, n = 137). Our results revealed a significant decrease in gut microbial diversity with the occurrence and progression of NAFLD (SMD = -0.32; 95% CI -0.42 to -0.21; p < 0.001). Alpha diversity and the increased abundance of several crucial genera, including Desulfovibrio, Negativibacillus, and Prevotella, can serve as an indication of their predictive risk ability for the occurrence and progression of NAFLD (all AUC > 0.7). The occurrence and progression of NAFLD are significantly associated with higher levels of LPS biosynthesis, tryptophan metabolism, glutathione metabolism, and lipid metabolism. Conclusion: This study elucidated gut microbes relevance to disease development and identified potential risk-associated microbes and functional pathways associated with NAFLD occurrence and progression.

Factors Related to Smoking and Perceptions of a Behavioral Counseling and Messenger Service-Delivered Smoking Cessation Intervention for People With HIV in China: Qualitative Study.

BACKGROUND: China, where half of the adult male population smoke tobacco, has one of the highest global burdens of smoking. Smoking rates are even higher among people with HIV. People with HIV can be affected by smoking in multiple ways, including more severe HIV-related symptoms and worse antiretroviral therapy treatment outcomes. However, smoking cessation services targeted for people with HIV are not routinely integrated into HIV care in China. Given the widespread mobile phone ownership, an exploration of factors related to smoking among people with HIV in China who smoke could inform the design and implementation of mobile smoking cessation interventions that target the needs of this vulnerable population. OBJECTIVE: This study aims to explore the perspectives of smoking, barriers and facilitators to quitting, and perceptions related to a smoking cessation intervention delivered through behavioral counseling sessions and brief daily messenger service (WeChat)-delivered messages. METHODS: We recruited people with HIV from the People's 4th Hospital of Nanning, Guangxi, China, and conducted semistructured face-to-face interviews. All interviews were audio-recorded, transcribed verbatim in Chinese, and translated into English for data analysis. We conducted a thematic analysis using a codebook, which was guided by a team-based consensus approach to identify 5 main themes. We also explored themes according to the demographic groups. RESULTS: A total of 24 participants were enrolled in the study. The mean age was 37.2 (SD=13.5) years. The participants had lived with HIV for a mean of 2.4 years. The majority were male (18/24, 75%) and lived in urban or metropolitan settings (19/24, 79%). We identified five main themes: variable knowledge of the harms of smoking, both related and unrelated to HIV; willpower perceived as the primary quitting strategy; a duality of the effect of social factors on quitting; perceptions about optimal features of the smoking cessation intervention (eg, messages should be brief and most frequent during the first few weeks); and the largely negative impact of their HIV diagnosis on smoking behaviors. In addition, some themes differed according to participant demographic characteristics such as age, sex, and education level. CONCLUSIONS: We identified barriers to and facilitators of smoking cessation among people with HIV in China by conducting semistructured qualitative interviews. Owing to the adverse impact of smoking on HIV outcomes, targeting cessation interventions to the unique needs and preferences of people with HIV in China may be needed to increase the effectiveness of future interventions. A pilot clinical trial will be conducted in the future to evaluate this behavioral counseling and brief daily messenger service (WeChat)-delivered messages approach among people with HIV who smoke in China.

Prevalence of Drug Resistance and Genetic Transmission Networks Among Human Immunodeficiency Virus/Acquired Immunodeficiency Syndrome Patients with Antiretroviral Therapy Failure in Guangxi, China.

Prevalence of drug resistance (DR) challenges the epidemic control of human immunodeficiency virus (HIV)-1. However, little is known about DR among patients with antiretroviral therapy (ART) failure in Guangxi province, China. This cross-sectional study was aimed to investigate the prevalence of DR and the characteristics of DR sequences in the genetic transmission network among HIV-1 patients with ART failure in Guangxi. We enrolled 358 eligible patients between 2012 and 2018. Blood samples were subjected to reverse transcription polymerase chain reaction, followed by sequencing of the HIV-1 polymerase (pol) gene. An online subtyping tool and neighbor-joining phylogenetic tree were used to determine the genotype. HIV-TRACE tool was used to constructed transmission network with a pairwise genetic distance of 0.013. DR was analyzed using the Stanford University HIV Drug Resistance Database. We obtained 293 pol-sequences from participants; CRF01_AE (75.4%), CRF 08_BC (15.7%), and CRF07_BC (8.5%) were the main subtypes, and an A1 subtype was detected in Guangxi for the first time. The overall prevalence of DR was 32.4% (95/293). Among those with identified DR, 25.6% were against non-nucleoside analog reverse-transcriptase inhibitors (NNRTIs), 17.7% were against nucleoside analog reverse-transcriptase inhibitors (NRTIs), and 14.3% were against both NRTIs and NNRTIs. The common drug-resistant mutations were M184V (10.2%), K103N (10.6%) and V179D (6.1%). The patients located in the southern Guangxi [adjust odds ratio (AOR) = 10.87], or whose blood plasma were taken in 2017-2018 (AOR = 3.98) had an increased risk of DR. Of the CRF01_AE, CRF07_BC, and CRF08_BC sequences, 18.6%, 8.0%, and 13.0% fell into clusters, respectively. Nine (9.7%) sequences from patients with DR fell into three clusters. The largest cluster containing 11 individuals was the CRF01_AE subtype, 27.3% of whom were DR patients. Although the prevalence of DR among ART failure patients in Guangxi was at a low level, the continuous surveillance of DR in ART patients is necessary.

The Emergence of Resistance Under Firstline INSTI Regimens.

We reported an HIV-naïve patient from a resource-limited area who was detected with multiple resistance sites associated with nucleoside reverse transcriptase inhibitors (NRTIs) and integrase strand transfer inhibitors (INSTIs) after the failure of the initial antiviral regimen dolutegravir/lamivudine (DTG/3TC) and subsequent Bictegravir/emtricitabine/tenofovir alafenamide (BIC/FTC/TAF). On May 8, 2021, a 53-year-old man was diagnosed with AIDS, Marneffei talaromycosis and fungal esophagitis, and was suspected of having tuberculosis (TB) in Guangxi, China. His baseline HIV RNA was 559,000 copies/mL and the CD4 count was 12 cells/µL, but resistance genotype testing was not performed. The patient remained immunosuppressed (CD4 count 3 cells/µL) after 12 weeks of initial antiviral treatment (ART) with DTG/3TC. After he was switched to BIC/FTC/TAF and started anti-TB treatment, the viral load (HIV RNA 163,200 copies/mL) was not effectively controlled, and there were multiple NRTIs drug-resistant mutations (D67N, K70R, M184V, T215V, K219Q) and INSTIs mutations (E138K, G140A, S147SG, Q148R). This suggested that in resource-limited areas, for HIV-naïve patients in advanced stages with active opportunistic infections, HIV RNA>500,000 copies/mL, and low CD4 count, baseline resistance testing and increased HIV RNA testing frequency should be recommended, DTG/3TC was not recommended as initiation, and opportunistic infections should be treated promptly. In addition, switching to other INSTIs was not recommended in the absence of resistance testing and ineffective use of DTG.

Differences in Alpha Diversity of Gut Microbiota in Neurological Diseases.

Background: Neurological diseases are difficult to diagnose in time, and there is currently a lack of effective predictive methods. Previous studies have indicated that a variety of neurological diseases cause changes in the gut microbiota. Alpha diversity is a major indicator to describe the diversity of the gut microbiota. At present, the relationship between neurological diseases and the alpha diversity of the gut microbiota remains unclear. Methods: We performed a systematic literature search of Pubmed and Bioproject databases up to January 2021. Six indices were used to measure alpha diversity, including community richness (observed species, Chao1 and ACE), community diversity (Shannon, Simpson), and phylogenetic diversity (PD). Random-effects meta-analyses on the standardized mean difference (SMD) were carried out on the alpha diversity indices. Subgroup analyses were performed to explore the sources of interstudy heterogeneity. Meta-analysis was performed on articles by matching the age, sex, and body mass index (BMI) of the disease group with the control group. Meanwhile, subgroup analysis was performed to control the variability of the sequencing region, platform, geographical region, instrument, and diseases. The area under the curve (AUC) value of the receiver operating characteristic (ROC) curve was calculated to assess the prediction effectiveness of the microbial alpha diversity indices. Results: We conducted a meta-analysis of 24 published studies on 16S rRNA gene amplified sequencing of the gut microbiota and neurological diseases from the Pubmed and Bioproject database (patients, n = 1,469; controls, n = 1,289). The pooled estimate demonstrated that there was no significant difference in the alpha diversity between patients and controls (P < 0.05). Alpha diversity decreased only in Parkinson's disease patients, while it increased in anorexia nervosa patients compared to controls. After adjusting for age, sex, BMI, and geographical region, none of the alpha diversity was associated with neurological diseases. In terms of Illumina HiSeq 2000 and the V3-V5 sequencing region, the results showed that alpha diversity increased significantly in comparison with the controls, while decreased in Illumina HiSeq 2500. ROC curves suggested that alpha diversity could be used as a biomarker to predict the AD (Simpson, AUC= 0.769, P = 0.0001), MS (observed species, AUC= 0.737, P = 0.001), schizophrenia (Chao1, AUC = 0.739, P = 0.002). Conclusions: Our review summarized the relationship between alpha diversity of the gut microbiota and neurological diseases. The alpha diversity of gut microbiota could be a promising predictor for AD, schizophrenia, and MS, but not for all neurological diseases.

Machine learning-based in-hospital mortality prediction of HIV/AIDS patients with Talaromyces marneffei infection in Guangxi, China.

OBJECTIVE: Talaromycosis is a serious regional disease endemic in Southeast Asia. In China, Talaromyces marneffei (T. marneffei) infections is mainly concentrated in the southern region, especially in Guangxi, and cause considerable in-hospital mortality in HIV-infected individuals. Currently, the factors that influence in-hospital death of HIV/AIDS patients with T. marneffei infection are not completely clear. Existing machine learning techniques can be used to develop a predictive model to identify relevant prognostic factors to predict death and appears to be essential to reducing in-hospital mortality. METHODS: We prospectively enrolled HIV/AIDS patients with talaromycosis in the Fourth People's Hospital of Nanning, Guangxi, from January 2012 to June 2019. Clinical features were selected and used to train four different machine learning models (logistic regression, XGBoost, KNN, and SVM) to predict the treatment outcome of hospitalized patients, and 30% internal validation was used to evaluate the performance of models. Machine learning model performance was assessed according to a range of learning metrics, including area under the receiver operating characteristic curve (AUC). The SHapley Additive exPlanations (SHAP) tool was used to explain the model. RESULTS: A total of 1927 HIV/AIDS patients with T. marneffei infection were included. The average in-hospital mortality rate was 13.3% (256/1927) from 2012 to 2019. The most common complications/coinfections were pneumonia (68.9%), followed by oral candida (47.5%), and tuberculosis (40.6%). Deceased patients showed higher CD4/CD8 ratios, aspartate aminotransferase (AST) levels, creatinine levels, urea levels, uric acid (UA) levels, lactate dehydrogenase (LDH) levels, total bilirubin levels, creatine kinase levels, white blood-cell counts (WBC) counts, neutrophil counts, procaicltonin levels and C-reactive protein (CRP) levels and lower CD3+ T-cell count, CD8+ T-cell count, and lymphocyte counts, platelet (PLT), high-density lipoprotein cholesterol (HDL), hemoglobin (Hb) levels than those of surviving patients. The predictive XGBoost model exhibited 0.71 sensitivity, 0.99 specificity, and 0.97 AUC in the training dataset, and our outcome prediction model provided robust discrimination in the testing dataset, showing an AUC of 0.90 with 0.69 sensitivity and 0.96 specificity. The other three models were ruled out due to poor performance. Septic shock and respiratory failure were the most important predictive features, followed by uric acid, urea, platelets, and the AST/ALT ratios. CONCLUSION: The XGBoost machine learning model is a good predictor in the hospitalization outcome of HIV/AIDS patients with T. marneffei infection. The model may have potential application in mortality prediction and high-risk factor identification in the talaromycosis population.

Individual and network factors associated with HIV self-testing among men who have sex with men in resource-limited settings in China.

BACKGROUND: HIV self-testing (HIVST) is a potential strategy to overcome challenges of HIV testing among men who have sex with men (MSM). However, for resource-limited settings, technology and diagnostic devices are lagging. Hence, we estimated the status and correlates of HIVST among MSM in resource-limited settings in China to inform the development of HIVST to reach United Nations Programme on HIV and AIDS (UNAIDS) targets to end HIV by 2030. METHODS: A cross-sectional study was conducted among MSM in Nanning, Guangxi, China, between August 2019 and January 2020. The HIVST status was collected and data on social network features, sociodemographic information, risk behaviours, etc. were compared between prior- and non-HIVST MSM. Logistic regression analyses were conducted to examine the correlates of HIVST. RESULTS: The prevalence of HIVST among 446 MSM was 40.4% (95% confidence interval [CI] 35.8-44.9%). The main component of sociocentric network contains more prior-HIVST MSM (38.3%) than non-HIVST MSM (28.6%, P =0.031). More MSM with individual features such as substance use during anal sex (22.8% vs 15.4%, P =0.049) and multiple sexual partners (76.1% vs 59.4%, P <0.001) were detected among prior-HIVST MSM. In multivariable analysis, prior HIVST was associated with the strong strength of ego-alter ties in the egocentric network (adjusted odds ratio [aOR] 1.72; 95% CI 1.09-2.71), HIV-infected partners (aOR, 7.17; 95% CI, 1.40-36.60), and vaginal intercourse (aOR, 0.38; 95% CI, 0.17-0.85). CONCLUSIONS: HIVST coverage among MSM in resource-limited settings is suboptimal. Integrating social networks into testing services may be viable to promote HIVST in MSM within resource-limited settings.

Integrated bioinformatics analysis for the identification of hub genes and signaling pathways related to circANRIL.

Background: Antisense noncoding RNA in the INK4 locus (ANRIL) is located on human chromosome 9p21, and modulation of ANRIL expression mediates susceptibility to some important human disease, including atherosclerosis (AS) and tumors, by affecting the cell cycle circANRIL and linear ANRIL are isoforms of ANRIL. However, it remains unclear whether these isoforms have distinct functions. In our research, we constructed a circANRIL overexpression plasmid, transfected it into HEK-293T cell line, and explored potential core genes and signaling pathways related to the important differential mechanisms between the circANRIL-overexpressing cell line and control cells through bioinformatics analysis. Methods: Stable circANRIL-overexpressing (circANRIL-OE) HEK-293T cells and control cells were generated by infection with the circANRIL-OE lentiviral vector or a negative control vector, and successful transfection was confirmed by conventional flurescence microscopy and quantitative real-time PCR (qRT-PCR). Next, differentially expressed genes (DEGs) between circANRIL-OE cells and control cells were detected. Subsequently, Gene Ontology (GO) biological process (BP) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to explore the principal functions of the significant DEGs. A protein-protein interaction (PPI) network and competing endogenous RNA (ceRNA) network were constructed in Cytoscape to determine circularRNA (circRNA)- microRNA(miRNA)-messenger RNA (mRNA) interactions and hub genes, and qRT-PCR was used to verify changes in the expression of these identified target genes. Results: The successful construction of circANRIL-OE cells was confirmed by plasmid sequencing, visualization with fluorescence microscopy and qRT-PCR. A total of 1745 DEGs between the circANRIL-OE group and control were identified, GO BP analysis showed that these genes were mostly related to RNA biosynthesis and processing, regulation of transcription and signal transduction. The KEGG pathway analysis showed that the up regulated DEGs were mainly enriched in the MAPK signaling pathway. Five associated target genes were identified in the ceRNA network and biological function analyses. The mRNA levels of these five genes and ANRIL were detected by qRT-PCR, but only COL5A2 and WDR3 showed significantly different expression in circANRIL-OE cells.

The altered metabolites contributed by dysbiosis of gut microbiota are associated with microbial translocation and immune activation during HIV infection.

Background: The immune activation caused by microbial translocation has been considered to be a major driver of HIV infection progression. The dysbiosis of gut microbiota has been demonstrated in HIV infection, but the interplay between gut microbiota and its metabolites in the pathogenesis of HIV is seldom reported. Methods: We conducted a case-controlled study including 41 AIDS patients, 39 pre-AIDS patients and 34 healthy controls. Both AIDS group and pre-AIDS group were divided according to clinical manifestations and CD4 + T cell count. We collected stool samples for 16S rDNA sequencing and untargeted metabolomics analysis, and examined immune activation and microbial translocation for blood samples. Results: The pre-AIDS and AIDS groups had higher levels of microbial translocation and immune activation. There were significant differences in gut microbiota and metabolites at different stages of HIV infection. Higher abundances of pathogenic bacteria or opportunistic pathogen, as well as lower abundances of butyrate-producing bacteria and bacteria with anti-inflammatory potential were associated with HIV severity. The metabolism of tryptophan was disordered after HIV infection. Lower level of anti-inflammatory metabolites and phosphonoacetate, and higher level of phenylethylamine and polyamines were observed in HIV infection. And microbial metabolic pathways related to altered metabolites differed. Moreover, disrupted metabolites contributed by altered microbiota were found to be correlated to microbial translocation and immune activation. Conclusions: Metabolites caused by dysbiosis of gut microbiota and related metabolic function are correlated to immune activation and microbial translocation, suggesting that the effect of microbiota on metabolites is related to intestinal barrier disruption in HIV infection.

Using Molecular Transmission Networks to Reveal the Epidemic of Pretreatment HIV-1 Drug Resistance in Guangxi, China.

INTRODUCTION: Pretreatment drug resistance (PDR) is becoming an obstacle to the success of ART. This study investigated the prevalence of PDR and the transmission clusters (TCs) of drug resistance mutations (DRMs) in two cities where drug abuse used to be high to describe the local HIV-1 transmission dynamics. METHODS: Plasma samples were obtained from 1,027 ART-naïve patients in Guangxi. Viral subtypes and DRMs were identified. Transmission network and related factors were also determined. RESULTS: A total of 1,025 eligible sequences were obtained from Qinzhou (65.8%) and Baise (34.2%) cities. The predominant HIV-1 genotype was CRF08_BC (45.0%), followed by CRF01_AE (40.9%). The overall prevalence of PDR was 8.3%, and resistance to NNRTI was the most common. Putative links with at least one other sequence were found in 543/1,025 (53.0%) sequences, forming 111 clusters (2-143 individuals). The most prevalent shared DRMs included V106I (45.35%), V179D (15.1%), and V179E (15.1%). Clusters related to shared DRMs were more frequent and larger in CRF08_BC. The prevalence of shared DRMs increased with time, while the proportion of PDR gradually decreased. Age > 50 years was associated with clustering. Subtype CRF08_BC was more likely to have DRMs, PDR propagation, and DRM sharing. CONCLUSION: PDR prevalence is moderate in this region. The association between PDR and subtype CRF08_BC suggested that DRMs spreading from injection drug users (IDUs) to heterosexuals (HETs) might be the major source of PDR in this region. Our findings highlight the significance of continuous surveillance of PDR.

Talaromyces marneffei promotes M2-like polarization of human macrophages by downregulating SOCS3 expression and activating the TLR9 pathway.

Little is known about how Talaromyces marneffei, a thermally dimorphic fungus that causes substantial morbidity and mortality in Southeast Asia, evades the human immune system. Polarization of macrophages into fungal-inhibiting M1-like and fungal-promoting M2-like types has been shown to play an important role in the innate immune response against fungal pathogens. This mechanism has not been defined for T. marneffei. Here, we demonstrated that T. marneffei promotes its survival in human macrophages by inducing them toward M2-like polarization. Our investigations of the mechanism revealed that T. marneffei infection led to SOCS3 protein degradation by inducing tyrosine phosphorylation, thereby relieving the inhibitory effect of SOCS3 on p-STAT6, a key factor for M2-like polarization. Our SOCS3-overexpression experiments showed that SOCS3 is a positive regulator of M1-like polarization and plays an important role in limiting M2-like polarization. Furthermore, we found that inhibition of the TLR9 pathway partially blocked T. marneffei-induced M2-like polarization and significantly enhanced the killing activity of macrophages against T. marneffei. Collectively, these results reveal a novel mechanism by which T. marneffei evades the immune response of human macrophages.