HBV infection profoundly escalates hepatocellular carcinoma (HCC) susceptibility, responsible for a majority of HCC cases. HBV-driven immune-mediated hepatocyte impairment significantly fuels HCC progression. Regrettably, inconspicuous early HCC symptoms often culminate in belated diagnoses. Nevertheless, surgically treated early-stage HCC patients relish augmented five-year survival rates. In contrast, advanced HCC exhibits feeble responses to conventional interventions like radiotherapy, chemotherapy, and surgery, leading to diminished survival rates. This investigation endeavors to unearth diagnostic hallmark genes for HBV-HCC leveraging a bioinformatics framework, thus refining early HBV-HCC detection. Candidate genes were sieved via differential analysis and Weighted Gene Co-Expression Network Analysis (WGCNA). Employing three distinct machine learning algorithms unearthed three feature genes (HHIP, CXCL14, and CDHR2). Melding these genes yielded an innovative Artificial Neural Network (ANN) diagnostic blueprint, portending to alleviate patient encumbrance and elevate life quality. Immunoassay scrutiny unveiled accentuated immune damage in HBV-HCC patients relative to solitary HCC. Through consensus clustering, HBV-HCC was stratified into two subtypes (C1 and C2), the latter potentially indicating milder immune impairment. The diagnostic model grounded in these feature genes showcased robust and transferrable prognostic potentialities, introducing a novel outlook for early HBV-HCC diagnosis. This exhaustive immunological odyssey stands poised to expedite immunotherapeutic curatives' emergence for HBV-HCC. 

Construction of a diagnostic model for hepatitis B-related hepatocellular carcinoma using machine learning and artificial neural networks and revealing the correlation by immunoassay.

Background: Hepatitis B-related liver cirrhosis (HBV-LC) is a common clinical disease that evolves from chronic hepatitis B (CHB). The development of cirrhosis can be suppressed by pharmacological treatment. When CHB progresses to HBV-LC, the patient’s quality of life decreases dramatically and drug therapy is ineffective. Liver transplantation is the most effective treatment, but the lack of donor required for transplantation, the high cost of the procedure and post-transplant rejection make this method unsuitable for most patients.Methods: The aim of this study was to find potential diagnostic biomarkers associated with HBV-LC by bioinformatics analysis and to classify HBV-LC into specific subtypes by consensus clustering. This will provide a new perspective for early diagnosis, clinical treatment and prevention of HCC in HBV-LC patients. Two study-relevant datasets, GSE114783 and GSE84044, were retrieved from the GEO database. We screened HBV-LC for feature genes using differential analysis, weighted gene co-expression network analysis (WGCNA), and three machine learning algorithms including least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF) for a total of five methods. After that, we constructed an artificial neural network (ANN) model. A cohort consisting of GSE123932, GSE121248 and GSE119322 was used for external validation. To better predict the risk of HBV-LC development, we also built a nomogram model. And multiple enrichment analyses of genes and samples were performed to understand the biological processes in which they were significantly enriched. And the different subtypes of HBV-LC were analyzed using the Immune infiltration approach.Results: Using the data downloaded from GEO, we developed an ANN model and nomogram based on six feature genes. And consensus clustering of HBV-LC classified them into two subtypes, C1 and C2, and it was hypothesized that patients with subtype C2 might have milder clinical symptoms by immune infiltration analysis.Conclusion: The ANN model and column line graphs constructed with six feature genes showed excellent predictive power, providing a new perspective for early diagnosis and possible treatment of HBV-LC. The delineation of HBV-LC subtypes will facilitate the development of future clinical treatment of HBV-LC.

Uncovering the immune microenvironment and molecular subtypes of hepatitis B-related liver cirrhosis and developing stable a diagnostic differential model by machine learning and artificial neural networks

Causal relationship between immune cells and hepatocellular carcinoma: a Mendelian randomisation study

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.

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

Glioma is a common intracranial tumor and is generally associated with poor prognosis. Recently, numerous studies illustrated the importance of 5-methylcytosine (m5C) RNA modification to tumorigenesis. However, the prognostic value and immune correlation of m5C in glioma remain unclear. We obtained RNA expression and clinical information from The Cancer Genome Atlas (TCGA) and The Chinese Glioma Genome Atlas (CGGA) datasets to analyze. Nonnegative matrix factorization (NMF) was used to classify patients into two subgroups and compare these patients in survival and clinicopathological characteristics. CIBERSORT and single-sample gene-set algorithm (ssGSEA) methods were used to investigate the relationship between m5C and the immune environment. The Weighted correlation network analysis (WGCNA) and univariate Cox proportional hazard model (CoxPH) were used to construct a m5C-related signature. Most of m5C RNA methylation regulators presented differential expression and prognostic values. There were obvious relationships between immune infiltration cells and m5C regulators, especially NSUN7. In the m5C-related module from WGCNA, we found SEPT3, CHI3L1, PLBD1, PHYHIPL, SAMD8, RAP1B, B3GNT5, RER1, PTPN7, SLC39A1, and MXI1 were prognostic factors for glioma, and they were used to construct the signature. The great significance of m5C-related signature in predicting the survival of patients with glioma was confirmed in the validation sets and CGGA cohort.

pdf/identification-of-m5c-regulator-medicated-methylation-4xp1y9j4gu.pdf

Identification of M5c regulator-medicated methylation modification patterns for prognosis and immune microenvironment in glioma.

Thymidine kinase 1 appears to be a marker for the prognosis of hepatocellular carcinoma based on a large-scale, multicenter study

To investigate the risk factors and intervention strategies for the development of acute kidney injury (AKI) after cardiac valve replacement with extracorporeal circulation.Retrospective analysis of the clinical data of a total of 106 patients diagnosed with heart valve disease and undergoing extracorporeal circulation heart valve replacement surgery from January 2021 to December 2021 in the Department of Cardiac and Major Vascular Surgery of our hospital. The two groups were divided into AKI and non-AKI groups according to whether acute kidney injury occurred after surgery, and the preoperative, intraoperative and postoperative clinical data were compared. Single-factor analysis and multi-factor logistic regression analysis were used to explore the risk factors for acute kidney injury after extracorporeal heart valve replacement, and to improve the prognosis by giving kidney function protection strategies as early as possible.Univariate analysis showed that age, preoperative blood creatinine >130umol/L, LVEF <45%, presence of subacute infective endocarditis (SIE), concurrent coronary artery bypass grafting (CABG), time to extracorporeal circulation, time to surgery, MAP <70mmHg, urine output <0.5mL/(kg-h), pulmonary infection, low cardiac output, and bacteraemia were risk factors for postoperative AKI. Multi-factor regression analysis showed that preoperative blood creatinine >130umol/l, LVEF <45%, combined infective endocarditis, extracorporeal circulation time >2h, intraoperative and postoperative hypotension, low cardiac output, and postoperative bacteremia were independent risk factors for postoperative AKI.Active intervention strategies in the perioperative period can reduce the occurrence of postoperative AKI and improve patient prognosis. 

Analysis of Risk Factors and Intervention Strategies for Acute Kidney Injury After Cardiac Valve Replacement

Purpose Information about dynamic changes occurring in the parameters and morphology of erythrocytes and platelets during the coronavirus disease 2019 (COVID-19) infection and convalescence is scarce. To explore potential associations between dynamic erythrocyte and platelet parameters, morphological changes, and the course or severity of the disease is essential. Patients and Methods From January 17th, 2020, to February 20th, 2022, we followed up on 35 patients with non-severe and 11 patients with severe COVID-19 following their discharge. We collected clinical features, dynamic complete blood count (CBC), and peripheral blood smears (PBS) and analyzed parameter and morphological changes of erythrocytes and platelets depending on the course or severity of the disease. The course of the disease included four periods, namely onset (T1), discharge (T2), 1-year follow-up (T3), and 2-year follow-up (T4). Results Red blood cell (RBC) counts and hemoglobin were the lowest in T2, followed by T1, and lower in T1 and T2 than in T3 and T4. Inversely, the red blood cell distribution width (RDW) was the highest in T2, followed by T1, and higher than in T3 and T4. Compared to non-severe patients, the platelet of severe patients was lower in T1 and T2. In contrast, the mean platelet volume (MPV) and platelet distribution width (PDW) tended to be higher in severe patients. Similarly, anisocytosis was more common in peripheral blood smears at early stages and in severe patients. Finally, large platelets were more common in severe patients. Conclusion Anisocytosis of erythrocytes and large platelets are found in patients with severe COVID-19, these changes may help primary hospitals to identify patients with a high risk of severe COVID-19 at an early stage.

Parameters and Morphological Changes of Erythrocytes and Platelets of COVID-19 Subjects: A Longitudinal Cohort Study

Background TACE and TACE with or without targeted immunotherapy are crucial comprehensive therapies for middle and advanced HCC. However, a reasonable and concise score is needed to evaluate TACE and TACE combined with systemic therapy in HCC treatment. Methods The HCC patients were grouped into two groups: training group (n = 778) (treated with TACE) and verification group (n = 333). The predictive value of baseline variables on overall survival was analyzed using COX model, and easy-to-use ALR (AST and Lym-R) scores. The best cut-off value of AST and Lym-R were determined using X-Tile software based on total survival time (OS) and further verified via a restricted three-spline method. Meanwhile, the score was further verified using two independent valid sets: TACE combined with targeted therapy and TACE with targeted combined immunotherapy. Results In multivariate analysis, baseline serum AST>57.1 (p < 0.001) and Lym-R≤21.7 (p < 0.001) were identified as independent prognostic factors. The OS of patients in the TACE pooled cohort with 0, 1, and 2 scores were 28.1 (95% CI 24–33.8) months, 15 (95% CI 12.4–18.6) months, and 7.4 (95% CI 5.7–9.1) months, respectively. The time-varying ROC curve based on ALR showed that the AUC values for predicting 1, -2-and 3-year OS were 0.698, 0.718, and 0.636, respectively. These results are confirmed in two independent valid sets of TACE combined with targeted therapy and TACE with targeted combined immunotherapy. And we established a nomogram after COX regression to predict the 1 -, 2- and 3-year survival time. Conclusion Our study confirmed that ALR score can predict the prognosis of HCC treated with TACE or TACE combined with systemic therapy.

Construction and Validation of TACE Therapeutic Efficacy by ALR Score and Nomogram: A Large, Multicenter Study

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Thymidine kinase 1 appears to be a marker for the prognosis of hepatocellular carcinoma based on a large-scale, multicenter study

Analysis of Risk Factors and Intervention Strategies for Acute Kidney Injury After Cardiac Valve Replacement

Parameters and Morphological Changes of Erythrocytes and Platelets of COVID-19 Subjects: A Longitudinal Cohort Study

Construction and Validation of TACE Therapeutic Efficacy by ALR Score and Nomogram: A Large, Multicenter Study