Pollutants-mediated viral hepatitis in different types: assessment of different algorithms and time series models.

The escalating frequency of environmental pollution incidents has raised significant concerns regarding the potential health impacts of pollutant fluctuations. Consequently, a comprehensive study on the role of pollutants in the prevalence of viral hepatitis is indispensable for the advancement of innovative prevention strategies. Monthly incidence rates of viral hepatitis from 2005 to 2020 were sourced from the Chinese Center for Disease Control and Prevention Infectious Disease Surveillance Information System. Pollution data spanning 2014-2020 were obtained from the National Oceanic and Atmospheric Administration (NOAA), encompassing pollutants such as CO, NO2, and O3. Time series analysis models, including seasonal auto-regressive integrated moving average (SARIMA), Holt-Winters model, and Generalized Additive Model (GAM), were employed to explore prediction and synergistic effects related to viral hepatitis. Spearman correlation analysis was utilized to identify pollutants suitable for inclusion in these models. Concurrently, machine learning (ML) algorithms were leveraged to refine the prediction of environmental pollutant levels. Finally, a weighted quantile sum (WQS) regression framework was developed to evaluate the singular and combined impacts of pollutants on viral hepatitis cases across different demographics, age groups, and environmental strata. The incidence of viral hepatitis in Beijing exhibited a declining trend, primarily characterized by HBV and HCV types. In predicting hepatitis prevalence trends, the Holt-Winters additive seasonal model outperformed the SARIMA multiplicative model ((1,1,0) (2,1,0) [12]). In the prediction of environmental pollutants, the SVM model demonstrated superior performance over the GPR model, particularly with Polynomial and Besseldot kernel functions. The combined pollutant risk effect on viral hepatitis was quantified as ßWQS (95% CI) = 0.066 (0.018, 0.114). Among different groups, PM2.5 emerged as the most sensitive risk factor, notably impacting patients with HCV and HEV, as well as individuals aged 35-64. CO predominantly affected HAV patients, showing a risk effect of ßWQS (95% CI) = - 0.0355 (- 0.0695, - 0.0016). Lower levels of PM2.5 and PM10 were associated with heightened risk of viral hepatitis incidence with a lag of five months, whereas elevated levels of PM2.5 (100-120 µg/m3) and CO correlated with increased hepatitis incidence risk with a lag of six months. The Holt-Winters model outperformed the SARIMA model in predicting the incidence of viral hepatitis. Among machine learning algorithms, SVM and GPR models demonstrated superior performance for analyzing pollutant data. Patients infected with HAV and HEV were primarily influenced by PM10 and CO, whereas SO2 and PM2.5 significantly impacted others. Individuals aged 35-64 years appeared particularly susceptible to these pollutants. Mixed pollutant exposures were found to affect the development of viral hepatitis with a notable lag of 5-6 months. These findings underscore the importance of long-term monitoring of pollutants in relation to viral hepatitis incidence.

Identification of novel resistance-associated mutations and discrimination within whole-genome sequences of fluoroquinolone-resistant Mycobacterium tuberculosis isolates.

This study aims to elucidate additional mutation loci associated with fluoroquinolone (FQ) resistance and evaluate the discriminatory capacity of mutation loci and allele mutation frequencies in identifying FQ-resistant Mycobacterium tuberculosis (MTB) isolates. A random selection of isolates was extracted from an ongoing collection. Drug resistance was determined using the resazurin microtiter assay (REMA) as the gold standard. Mutation loci and the burden of mutations in the quinolone resistance-determining region (QRDR) were elucidated through whole-genome sequencing (WGS). Novel amino acid mutations, namely, G520D and G520T, were identified in the gyrB and associated with FQ resistance. In the context of distinguishing FQ-resistant isolates, the AUC for the QRDR mutation frequency burden (0.969) surpassed that of the mutation locus (0.929), and this difference was statistically significant (P = 0.03). Furthermore, using the resistance mutation locus as a reference, setting the QRDR mutation frequency burden threshold at 1.31% resulted in a 3.60% increase in the accuracy of classifying FQ-resistant isolates (NRI = 3.60%, P < 0.001). The QRDR mutation frequency burden appears to offer superior diagnostic efficacy in discriminating FQ-resistant isolates compared to qualitative detection of mutant loci.IMPORTANCEFluoroquinolone (FQ) drugs are recommended as second-line drugs for the treatment of multidrug-resistant tuberculosis. With the massive use of FQ drugs in the clinical treatment of tuberculosis (TB), there is an increasing rate of drug resistance to FQ drugs. In this study, we identified and demonstrated novel amino acid mutations associated with FQ resistance in Mycobacterium tuberculosis (MTB), and we quantified the mutation sites and identified the quinolone resistance-determining region (QRDR) mutation frequency burden as a novel diagnostic method for FQ resistance. We hope that the results of this study will provide data support and a theoretical basis for the rapid diagnosis of FQ-resistant MTB.

Transcriptomics-based investigation of manganese dioxide nanoparticle toxicity in rats' choroid plexus.

Manganese dioxide nanoparticles (MnO2-NPs) have a wide range of applications in biomedicine. Given this widespread usage, it is worth noting that MnO2-NPs are definitely toxic, especially to the brain. However, the damage caused by MnO2-NPs to the choroid plexus (CP) and to the brain after crossing CP epithelial cells has not been elucidated. Therefore, this study aims to investigate these effects and elucidate potential underlying mechanisms through transcriptomics analysis. To achieve this objective, eighteen SD rats were randomly divided into three groups: the control group (control), low-dose exposure group (low-dose) and high-dose exposure group (high-dose). Animals in the two treated groups were administered with two concentrations of MnO2-NPs (200 mg kg-1 BW and 400 mg kg-1 BW) using a noninvasive intratracheal injection method once a week for three months. Finally, the neural behavior of all the animals was tested using a hot plate tester, open-field test and Y-type electric maze. The morphological characteristics of the CP and hippocampus were observed by H&E stain, and the transcriptome of CP tissues was analysed by transcriptome sequencing. The representative differentially expressed genes were quantified by qRT-PCR. We found that treatment with MnO2-NPs could induce learning capacity and memory faculty decline and destroy the structure of hippocampal and CP cells in rats. High doses of MnO2-NPs had a more obvious destructive capacity. For transcriptomic analysis, we found that there were significant differences in the numbers and types of differential genes in CP between the low- and high-dose groups compared to the control. Through GO terms and KEGG analysis, high-dose MnO2-NPs significantly affected the expression of transporters, ion channel proteins, and ribosomal proteins. There were 17 common differentially expressed genes. Most of them were transporter and binding genes on the cell membrane, and some of them had kinase activity. Three genes, Brinp, Synpr and Crmp1, were selected for qRT-PCR to confirm their expression differences among the three groups. In conclusion, high-dose MnO2-NPs exposure induced abnormal neurobehaviour, impaired memory function, destroyed the structure of the CP and changed its transcriptome in rats. The most significant DEGs in the CP were within the transport system.

TANC1 methylation as a novel biomarker for the diagnosis of patients with anti-tuberculosis drug-induced liver injury.

We aimed to elucidate the differences in genomic methylation patterns between ADLI and non-ADLI patients to identify DNA methylation-based biomarkers. Genome-wide DNA methylation patterns were obtained using Infinium MethylationEPIC (EPIC) BeadChip array to analyze 14 peripheral blood samples (7 ADLI cases, 7 non-ADLI controls). Changes in the mRNA and DNA methylation in the target genes of another 120 peripheral blood samples (60 ADLI cases, 60 non-ADLI controls) were analyzed by real-time polymerase chain reaction and pyrosequencing, respectively. A total of 308 hypermethylated CpG sites and 498 hypomethylated CpG sites were identified. Significantly, hypermethylated CpG sites cg06961147 and cg24666046 in TANC1 associated with ADLI was identified by genome-wide DNA methylation profiling. The mRNA expression of TANC1 was lower in the cases compared to the controls. Pyrosequencing validated these two differentially methylated loci, which was consistent with the results from the EPIC BeadChip array. Receiver operating characteristic analysis indicated that the area under the curve of TANC1 (cg06961147, cg24666046, and their combinations) was 0.812, 0.842, and 0.857, respectively. These results indicate that patients with ADLI have different genomic methylation patterns than patients without ADLI. The hypermethylated differentially methylated site cg06961147 combined with cg24666046 in TANC1 provides evidence for the diagnosis of ADLI.