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Autoimmune hepatitis (AIH) is a common cause of liver cirrhosis. To identify the characteristics of the oral microbiome in patients with AIH, we collected 204 saliva samples including 68 AIH patients and 136 healthy controls and performed microbial MiSeq sequencing after screening. All samples were randomly divided into discovery cohorts (46 AIH and 92 HCs) and validation cohorts (22 AIH and 44 HCs). Moreover, we collected samples of 12 AIH patients from Hangzhou for cross-regional validation. We described the oral microbiome characteristics of AIH patients and established a diagnostic model. In the AIH group, the oral microbiome diversity was significantly increased. The microbial communities remarkably differed between the two groups. Seven genera, mainly Fusobacterium, Actinomyces and Capnocytophaga, were dominant in the HC group, while 51 genera, Streptococcus, Veillonella and Leptotrichia, were enriched in the AIH group. Notably, we found 23 gene functions, including Membrane Transport, Carbohydrate Metabolism, and Glycerolipid metabolism that were dominant in AIH and 31 gene functions that prevailed in HCs. We further investigated the correlation between the oral microbiome and clinical parameters. The optimal 5 microbial markers were figured out through a random forest model, and the distinguishing potential achieved 99.88% between 46 AIH and 92 HCs in the discovery cohort and 100% in the validation cohort. Importantly, the distinguishing potential reached 95.55% in the cross-regional validation cohort. In conclusion, this study is the first to characterize the oral microbiome in AIH patients and to report the successful establishment of a diagnostic model and the cross-regional validation of microbial markers for AIH. Importantly, oral microbiota-targeted biomarkers may be able to serve as powerful and noninvasive diagnostic tools for AIH.

Objective: The intestinal microbiome is associated with various autoimmune diseases. Regional difference is the main influencing factor of intestinal microbial difference. This study aimed to identify the differences in fecal microbiome between autoimmune hepatitis (AIH) patients and healthy controls (HCs) in Central China, and to validate the efficacy of fecal microbiome as a diagnostic tool for AIH. Design: We collected 115 fecal samples from AIH patients (N = 37) and HCs (N = 78) in Central China and performed gene sequencing. Fecal microbiomes were characterized and microbial markers for AIH were identified. Results: Fecal microbial diversity showed a downward trend in AIH compared with HCs. Fecal microbial communities significantly differed between both groups. At the phylum level, Verrucomicrobia abundance was significantly increased, while Lentisphaerae and Synergistetes were significantly decreased in the AIH patients vs. the HCs. Compared to the HCs, 15 genera, including Veillonella, Faecalibacterium, and Akkermansia, were enriched, while 19 genera, such as Pseudobutyrivibrio, Lachnospira, and Ruminococcaceae, were decreased in the AIH patients. Ten genera, including Veillonella, Faecalibacterium, and Akkermansia, predominated in the AIH patients. Five microbial biomarkers were deemed optimal diagnostic tools for AIH. The probability of disease was significantly increased in AIH group vs. HCs, achieving 83.25% value of area under the curve. Conclusion: We present the characteristics of AIH patients in Central China for the first time. Five microbial biomarkers, including Lachnospiraceae, Veillonella, Bacteroides, Roseburia, and Ruminococcaceae, achieved a high potential distinguishing AIH patients from HCs.

Background: Acute paraquat (PQ) poisoning is characterized by multi-organ failure and lacking effective therapies. Therefore, identifying risk factors and developing model that could predict early prognosis for patients with PQ poisoning is of great importance. Methods: This was a retrospective cohort study employed with patients suffered from acute PQ poisoning (n=1199). Patients (n=913) with PQ poisoning from 2011 to 2018 were randomly divided into 2 mutually exclusive groups: training (609 patients) and test (304 patients). Another 2 external cohorts containing 207 cases from Zhengzhou 2019 were used as validation from different time and 79 from Shenyang as validation from different site. Risk factors were identified by a logistic model with Markov Chain Monte Carlo (MCMC) simulation and further evaluated by a latent class analysis. The prediction score of this model was developed based on the training sample and was further evaluated using the testing and validation samples. Findings: Eight risk factors including age, ingestion volume, CK-MB, platelet (PLT), white blood cell (WBC), neutrophil counts (N), gamma-glutamyl transferase (GGT) and serum creatinine (Cr) were identified as independent risk indicators of in-hospital death events. The risk model had a C statistic of 0.895 (95% CI 0.855-0.928), 0.891 (95% CI 0.848-0.932) and 0.829 (95% CI 0.455-1.000) and a predictive range of 4.6%-98.2%, 2.3%-94.9% and 0%-12.5% for the test, validation_time and validation_site group, respectively. In the training group, the risk model classified 18.4%, 59.9% and 21.7% of patients into the high, average and low-risk groups, with corresponding probabilities of 0.985, 0.365, and 0.03 for in-hospital death events. Interpretation: Eight risk factors were identified in this study. And we developed and evaluated a simple risk model to predict the prognosis of patients with acute PQ poisoning. This simple and reliable risk score system could be helpful in recognizing high-risk patients and reducing in-hospital death rate due to PQ poisoning. Funding Statement: This work was supported by the National Natural Science Foundation of China (81701893 and 81600506), National S&T Major Project of China (2018ZX10301201-008), Key Scientific Research Projects of Higher Education Institutions in Henan Province (20A320046 and 20A320056), Joint Construction Project of Henan Province Medical S&T Research (SB201901006). Declaration of Interests: All authors declare no conflict of interest. Ethics Approval Statement: The Institutional Review Board of the First Affiliated Hospital of Zhengzhou University (2017-XY-002) approved this study and exemption of the patient’s consent.

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide. The prognosis of HCC remains very poor; thus, an effective treatment remains urgent. Herein, a type of nanomedicine is developed by conjugating Fe@Fe3 O4 nanoparticles with ginsenoside Rg3 (NpRg3), which achieves an excellent coupling effect. In the dimethylnitrosamine-induced HCC model, NpRg3 application significantly prolongs the survival of HCC mice. Further research indicates that NpRg3 application significantly inhibits HCC development and eliminates HCC metastasis to the lung. Notably, NpRg3 application delays HCC-induced ileocecal morphology and gut microbial alterations more than 12 weeks during HCC progression. NpRg3 administration elevates the abundance of Bacteroidetes and Verrucomicrobia, but decreases Firmicutes. Twenty-nine predicted microbial gene functions are enriched, while seven gene functions are reduced after NpRg3 administration. Moreover, the metabolomics profile presents a significant progression during HCC development, but NpRg3 administration corrects tumor-dominant metabolomics. NpRg3 administration decreases 3-indolepropionic acid and urea, but elevates free fatty acids. Importantly, NpRg3 application remodels the unbalanced correlation networks between gut microbiota and metabolism during HCC therapy. In conclusion, nanoparticle conjugation of ginsenoside Rg3 inhibits HCC development and metastasis via the remodeling of unbalanced gut microbiota and metabolism in vivo, providing an antitumor therapy strategy.

Lung cancer is one of the deadliest types of cancer worldwide due to its high mortality rate. Adenocarcinoma constitutes 20%–30% of all lung cancers. In recent years, studies on the mechanisms of lung tumorigenesis and development have in part focused on the microRNAs for their crucial role in the progress of different cancers. As for our study, we demonstrated that miR-519d was differently downregulated and eIF4H was significantly overexpressed in lung adenocarcinoma via the detection of quantitative real-time polymerase chain reaction compared with the adjacent normal tissues. Furthermore, Cell Counting Kit-8 assay, colony formation assay, xenograft tumor experiment, Ki67 immunohistochemistry assay and transwell assay were performed to explain that the upregulated miR-519d could inhibit the proliferation and invasion of A549 and H1299 cells. To further advance our understanding of the mechanisms of miR-519d, we performed the bioinformatics analysis and the luciferase report assay. The results from these procedures revealed eIF4H to be one of the targets of miR-519d. Downregulated eIF4H was analogous to the overexpressed miR-519d obtained from miR-519d agomir and si-eIF4H transfection. In summary, it can be concluded that miR-519d targets eIF4H in lung adenocarcinoma to inhibit cell proliferation and invasion. This mechanism may offer new insights into the tumorigenesis and development of lung adenocarcinoma.