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As medicinal plants can accumulate harmful metals from the native soil, people's consumption of these materials may cause the human body to accumulate toxic metal elements. This has given rise to people’s concerns about the quality and safety of Chinese medicinal materials. This research aims to determine the levels of Cr, Ni, Cu, Zn, As, Cd, Hg and Pb in four medicinal plant species (Aster tataricus L.f., Salvia miltiorrhiza Bge, Radix Aucklandiae, Scutellaria baicalensis Georgi) and their native soil. All samples were collected from Qian’an city, beside Yanshan Mountain Range in Tangshan city, east Hebei Province, north China. The contents of heavy metals we detected in the soil conformed to the current limits. However, the Cd and Hg in the soil had a very high potential ecological risk because of their contents higher than the base level of local soil. The contents of Cu, Cd, Hg and Pb in some medicinal herbs exceeded the standards. The content of Cu in Radix Aucklandiae exceeded the standard by 3 times, and others exceeded the standard by less than one time. The comprehensive health risk assessment of heavy metals with chronic non-carcinogenic effects for human body showed that none of the four medicinal herbs can create a health risk. Thus, there is no strong positive correlation between heavy metal pollution in medicinal herbs and that in the native soil. Further research should be investigated to the connection between the heavy metal levels in the soil and plants, and the comprehensive effects of soil, air and irrigation water on heavy metal pollution of Chinese herbal medicines. We also recommend that Chinese herbal medicines should be cultivated and gathered only from controlled or uncontaminated areas.

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 βWQS (95% CI) = 0.066 (0.018, 0.114). Among different groups, PM 2.5 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 βWQS (95% CI) = − 0.0355 (− 0.0695, − 0.0016). Lower levels of PM 2.5 and PM 10 were associated with heightened risk of viral hepatitis incidence with a lag of five months, whereas elevated levels of PM 2.5 (100–120 μg/m 3 ) 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 PM 10 and CO, whereas SO 2 and PM 2.5 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.

Background The pathogenesis of anti-tuberculosis (TB) drug-induced liver injury (ADLI) is complicated and remains unclear. We aimed to analyse the relationship between the characteristics of gut microbiota and ADLI in Mongolian and Han patients with pulmonary TB and identify the most notable bacteria related to the occurrence of liver injury in those populations. Methods Patients with concurrent liver injury (LI) and no liver injury (ULI) before receiving first-line anti-TB drug treatment (T1) from the Han population in Tangshan and the Mongolian population in Inner Mongolia were selected as research subjects. At the time of liver injury (T2), stool samples were measured by bacterial 16S rRNA gene high-throughput sequencing to analyse and compare the differences in the gut microbiota of the LI and ULI Mongolian and Han patients at T1 and T2 and identify the differences between those patients. Results A total of 45 Mongolian and 37 Han patients were enrolled in our study. A dynamic comparison from T1 to T2 showed that the microbiota of the LI and ULI groups changed significantly from T1 to T2 in both the Mongolian and Han populations. However, there were commonalities and personality changes in the microbiota of the two ethnic groups. Conclusion Differences in gut microbes in ADLI were found among the Han and Mongolian patients in our study. Ekmania and Stenotrophomonas were related to the occurrence of ADLI in Mongolian patients, while Ekmania and Ruminococcus__gnavus_group were related to the occurrence of ADLI in the Han population.

We examined the effect and relative contributions of different types of stress on the risk of hypertension. Using cluster sampling, 5,976 community-dwelling individuals aged 40-60 were selected. Hypertension was defined according to the Seventh Report of the Joint National Committee, and general psychological stress was defined as experiencing stress at work or home. Information on known risk factors of hypertension (e.g., physical activity levels, food intake, smoking behavior) was collected from participants. Logistic regression analysis was used to determine the associations between psychological stress and hypertension, calculating population-attributable risks and 95% confidence intervals (CIs). General stress was significantly related to hypertension (odds ratio [OR] = 1.247, 95% CI [1.076, 1.446]). Additionally, after adjustment for all other risk factors, women showed a greater risk of hypertension if they had either stress at work or at home: OR = 1.285, 95% CI (1.027, 1.609) and OR = 1.231, 95% CI (1.001, 1.514), respectively. However, this increased risk for hypertension by stress was not found in men. General stress contributed approximately 9.1% (95% CI [3.1, 15.0]) to the risk for hypertension. Thus, psychological stress was associated with an increased risk for hypertension, although this increased risk was not consistent across gender.

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.

To investigate the humoral immunity and clinical characteristics of Chinese college students after experiencing a BA.5/BF.7 and/or XBB.1.5 wave. We enrolled 876 college students who received 2–3 vaccination doses of COVID-19 and followed by BA.5/BF.7 and/or XBB.1.5 breakthrough infections between January 2022 and October 2023. IgG and total antibodies against SARS-CoV-2 were measured by chemiluminescent immunoassay. Neutralizing antibodies were detected using a pseudovirus neutralization assay. Meanwhile, we created an Enterprise WeChat link for college students to self-report SARS-CoV-2 infections and clinical symptoms of COVID-19. We observed that among college students, the most common symptoms upon SARS-CoV-2 infection were fever, fatigue, and sore throat. Moreover, reinfected college students had higher levels of total antibodies and neutralizing antibodies against BA.5, XBB.1.5 and EG.5.1, especially after experiencing the XBB.1.5 wave. Finally, the neutralizing effect against the newly emerged Omicron subvariants XBB.1.5 and EG.5.1 is limited among the college students. Our study demonstrates that hybrid immunity, built from breakthrough infections and reinfections, enhances total antibody levels and bolsters neutralizing activity, contributing to milder clinical presentations upon reinfection. However, neutralization efficacy against newer subvariants, such as XBB.1.5 and EG.5.1, remains compromised.

This study aimed to explore the distinct characteristics of the gut microbiota in tuberculosis (TB) patients who experienced liver injury following anti-TB treatment compared with those who did not. We employed a nested case-control study design, recruiting newly diagnosed pulmonary TB patients at Tangshan Infectious Disease Hospital. Participants were categorized into the Antituberculosis Drug-Induced Liver Injury (ADLI) group and the Non-ADLI group based on the occurrence of liver injury after treatment. Both groups received identical anti-TB regimens. Stool samples were collected from patients who developed liver injury within 2-3 weeks of starting treatment, alongside matched controls during the same timeframe. The samples underwent 16S rDNA sequencing, and clinical data and blood samples were also collected for further analysis. At the same time, we constructed mouse models to explore the effects of different anti-tuberculosis drugs on gut microbiota. Following anti-TB treatment, we observed a decrease in microbial diversity and significant structural changes in the gut microbiota of TB patients (P < 0.05). At T1, the Non_ADLI_T1 group presented relatively high levels of , and . In contrast, the ADLI_ T1 group presented elevated levels of , , , , , , and . At T2, the ADLI_T2 group presented increased levels of , , , , and than did the Non_ADLI_T2 group. Additionally, the ADLI_T2 group presented decreased levels of , , and than did the Non_ADLI_T2 group. In animal experiments, similar changes to those in the human population were observed in the mouse model compared to the control group. Any single anti-tuberculosis drug or two-drug combination or three-drug combination can cause dysbiosis of the mouse gut microbiota. The signature genera between groups are different and related to the type of anti-tuberculosis drug. Anti-tuberculosis treatment induces dysbiosis in the gut microbiota of TB patients. Notably, there are significant differences in microbiota characteristics between TB patients with and without liver injury at both onset and during treatment. There are some differences in the characteristics of bacterial flora in liver injury caused by different drugs.

It has been reported that calpain/caspase-mediated apoptosis induced by endoplasmic reticulum stress (ERS) in hepatic stellate cells (HSCs) by previous studies. At present, the activation of HSC is an important cause of liver fibrosis, and the induction of HSC apoptosis plays an irreplaceable role in reversing liver fibrosis. Therefore, it is of great significance to explore mechanisms of action that can induce HSC apoptosis for the reversal of hepatic fibrosis and the clinical prevention and treatment of hepatic-fibrosis-related diseases such as hepatitis, cirrhosis, and liver cancer. In the current study, we demonstrated that tunicamycin (a novel ERS inducer) can induce the apoptosis of HSCs and increase the concentration of intracellular Ca 2+ and the expression of ERS protein GRP78, apoptosis protein caspase-12, and Bax, while it can decrease the antiapoptosis protein expression of Bcl-2. Our findings indicate that tunicamycin can induce HSCs apoptosis through calpain-2/Ca 2+ -dependent ERS pathway.

Anti-tuberculosis (anti-TB) drug-induced liver injury (ADLI) is one of the most common adverse effects associated with TB treatment. Cytochrome P450 (CYP) 1A1 and glutathione S-transferase (GST) P1 are important phase I/II metabolizing enzymes involved in drug metabolism and detoxification. Genetic polymorphism and CpG island methylation have been reported as factors influencing the expression of CYP1A1 and GSTP1. This study aimed to determine the potential relationships of CYP1A1 and GSTP1 polymorphisms and CpG island methylation with ADLI risk. This was a population-based one-to-one matched case-control study. The subjects were patients with TB receiving treatment in China from December 2010 to June 2013. In total, 127 patients with TB and ADLI (case group) and 127 patients with TB but without liver injury (control group) were included in this study. Subjects were matched in terms of sex, age, and therapeutic regimen. The general condition of each patient was assessed using questionnaires. The CYP1A1 MspI and GSTP1 Ile105Val polymorphisms as well as methylation status were detected by polymerase chain reaction (PCR)-restriction fragment length polymorphism and the methylation-specific PCR method. We found no significant difference in GSTP1 and CYP1A1 genotypes between the two groups, probably because the sample size was not large enough; however, patients with ADLI had significantly higher GSTP1 and CYP1A1 promoter methylation rates than control subjects [odds ratio (OR) = 2.467 and 2.000, respectively]. After adjusting for drinking, which significantly differed between the groups as per univariate analysis, we found that hypermethylation of GSTP1 and CYP1A1 promoters was associated with ADLI (OR = 2.645 and 2.090, respectively). Hypermethylation of CpG islands of GSTP1 and CYP1A1 promoters may thus play important roles in the development of ADLI and provide evidence of being used as novel markers for ADLI risk prediction.