A Mixture Method for Robust Detection HCV Early Diagnosis Biomarker with ML Approach and Molecular Docking.

Given the substantial correlation between early diagnosis and prolonged patient survival in HCV patients, it is vital to identify a reliable and accessible biomarker. The purpose of this research was to identify accurate miRNA biomarkers to aid in the early diagnosis of HCV and to identify key target genes for anti-hepatic fibrosis therapeutics. The expression of 188 miRNAs in 42 HCV liver patients with different functional states and 23 normal livers were determined using RT-qPCR. After screening out differentially expressed miRNA (DEmiRNAs), the target genes were predicted. To validate target genes, an HCV microarray dataset was subjected to five machine learning algorithms (Random Forest, Adaboost, Bagging, Boosting, XGBoost) and then, based on the best model, importance features were selected. After identification of hub target genes, to evaluate the potency of compounds that might hit key hub target genes, molecular docking was performed. According to our data, eight DEmiRNAs are associated with early stage and eight DEmiRNAs are linked to a deterioration in liver function and an increase in HCV severity. In the validation phase of target genes, model evaluation revealed that XGBoost (AUC = 0.978) outperformed the other machine learning algorithms. The results of the maximal clique centrality algorithm determined that CDK1 is a hub target gene, which can be hinted at by hsa-miR-335, hsa-miR-140, hsa-miR-152, and hsa-miR-195. Because viral proteins boost CDK1 activation for cell mitosis, pharmacological inhibition may have anti-HCV therapeutic promise. The strong affinity binding of paeoniflorin (-6.32 kcal/mol) and diosmin (-6.01 kcal/mol) with CDK1 was demonstrated by molecular docking, which may result in attractive anti-HCV compounds. The findings of this study may provide significant evidence, in the context of the miRNA biomarkers, for early-stage HCV diagnosis. In addition, recognized hub target genes and small molecules with high binding affinity may constitute a novel set of therapeutic targets for HCV.

Detection of key mRNAs in liver tissue of hepatocellular carcinoma patients based on machine learning and bioinformatics analysis.

One methodology extensively used to develop biomarkers is the precise detection of highly responsive genes that can distinguish cancer samples from healthy samples. The purpose of this study was to screen for potential hepatocellular carcinoma (HCC) biomarkers based on non-fusion integrative multi-platform meta-analysis method. The gene expression profiles of liver tissue samples from two microarray platforms were initially analyzed using a meta-analysis based on an empirical Bayesian method to robust discover differentially expressed genes in HCC and non-tumor tissues. Then, using the bioinformatics technique of weighted correlation network analysis, the highly associated prioritized Differentially Expressed Genes (DEGs) were clustered. Co-expression network and topological analysis were utilized to identify sub-clusters and confirm candidate genes. Next, a diagnostic model was developed and validated using a machine learning algorithm. To construct a prognostic model, the Cox proportional hazard regression analysis was applied and validated. We identified three genes as specific biomarkers for the diagnosis of HCC based on accuracy and feasibility. The diagnostic model's area under the curve was 0.931 with confidence interval of 0.923-0.952.•Non-fusion integrative multi-platform meta-analysis method.•Classification methods and biomarkers recognition via machine learning method.•Biomarker validation models.

Minimally invasive and invasive liver surgery based on augmented reality training: a review of the literature.

A liver surgeon's knowledge of anatomy is critical. Due to the patient's small field of vision, patient specific, complex nerve system, and other factors, even a minor loss can result in irreversible damage. Surgeons could benefit from the use of augmented reality (AR) technology, which would bring three-dimensional image data into the operating room. AR visualization can improve surgical procedures, facilitate intraoperative planning, and enhance surgical guidance for the anatomy of interest, all of which contribute to the application's minimal invasiveness. This literature review on image guidance in liver surgery provides the reader with information about AR techniques. To ascertain the current state of Augmented reality technology's application in liver surgery, a PubMed and Embase search were conducted using the following keywords: < (Augmented reality) AND (liver surgery) > and < 'Augmented reality' AND 'liver surgery' > (publication date from January 1991 until Jun 2022). The query yielded a total of 205 publications-excluded papers in other languages, virtual reality (VR), and reviews leaving 135 studies for review. After removing duplication, the titles and abstracts of those studies were manually reviewed. Finally, 31 pertinent studies were determined to be pertinent to the subject. Generally, augmented reality technology includes preoperative planning and three-dimensional reconstruction, intraoperative three-dimensional navigation, and registration. Visualization may be aided by virtual three-dimensional reconstruction models of the liver from Computed Tomography/Magnetic Resonance Imaging scans. The results demonstrate that by utilizing augmented reality technology, blood vessels and tumor structures in the liver can be visualized during surgery, allowing for precise navigation during complicated surgical procedures. Augmented reality has been demonstrated to be safe and effective in both minimally invasive and invasive liver surgery. With recent advancements and significant effort by liver surgeons, augmented reality technologies have the potential to increase hepatobiliary surgical procedures dramatically. However, further clinical trials will be necessary to evaluate augmented reality as a tool for reducing post-operative morbidity and mortality. The impact of these cutting-edge computerized image guidance techniques on clinically relevant outcome parameters should be assessed in the future.