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.

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.

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.

Immune and physiological responses of Mytilus unguiculatus to Alexandrium spp. with varying paralytic shellfish toxin profiles.

The innate immunity of bivalves serves as the initial defense mechanism against environmental pollutants, ultimately impacting genetic regulatory networks through synergistic interactions. Previous research has demonstrated variations in the accumulation and tolerance capacities of bivalves; however, the specific mechanism underlying the low accumulation of PSTs in M. unguiculatus remains unclear. This study examined the alterations in feeding behavior and transcriptional regulation of M. unguiculatus following exposure to two Alexandrium strains with distinct toxin profiles, specifically gonyautoxin (AM) and N-sulfocarbamoyl toxin (AC). The total accumulation rate of PSTs in M. unguiculatus was 43.64 % (AC) and 27.80 % (AM), with highest PSTs content in the AM group (455.39 µg STXeq/kg). There were significant variations (P < 0.05) in physiological parameters, such as total hemocyte count, antioxidant superoxide activity and tissue damage in both groups. The absorption rate was identified as the key factor influencing toxin accumulation. Transcriptomic analyses demonstrated that PSTs triggered upregulation of endocytosis, lysosome, and immune-related signaling pathways. Furthermore, PSTs induced a nucleotide imbalance in the AC group, with total PSTs content serving as the most toxic indicator. These results suggested that protein-like substances had a crucial role in the stress response of M. unguiculatus to PSTs. This study provided novel perspectives on the impacts of intricate regulatory mechanisms and varying immune responses to PSTs in bivalves.

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 with HIV (PWH) 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 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 PWH in the AIDS stage and 203 PWH in the non-AIDS stage. The α-diversity of gut microbiota was downregulated in the AIDS stage ( P  < 0.05). Several machine learning 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 decreased key bacteria ASV1 ( Bacteroides sp.), ASV8 ( Fusobacterium sp.), ASV30 ( Roseburia sp.), ASV37 ( Bacteroides sp.), and ASV41 ( Lactobacillus sp.) in the AIDS stage were positively correlated with the CD4 + T cell count, the EndoCAb IgM level, 4-hydroxyphenylpyruvic acid abundance, and the predicted cell growth pathway, and negatively correlated with the CD3 + CD4 + CD38 + HLA-DR + T cell count and the sCD14 level. CONCLUSION: Machine learning 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.

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.