The Decisive Case-Control Study Elaborates the Null Association between ESR1 XbaI and Osteoarthritis in Asians: A Case-Control Study and Meta-Analysis

Exploration of the molecular mechanism of Wufu Yin in the treatment of knee osteoarthritis based on network pharmacology and experimental validation

Wufu Yin (WFY) exhibits significant clinical effectiveness in knee osteoarthritis (KOA) treatment, yet its therapeutic mechanisms are still unclear. This study aimed to explore the active ingredients and potential mechanism of WFY in the treatment of KOA. The network pharmacology-based approach was adopted to investigate the underlying mechanism of WFY in treating KOA. Molecular docking analysis was performed using Auto Vina software. An in vitro model of KOA inflammation was established by inducing chondrocyte cultures with interleukin-1 beta (IL-1β). Cell viability was quantified through the cell counting kit-8 assay, inflammatory cytokine levels were measured via ELISA, and protein expressions were assessed by Western blot analysis. A total of 225 active ingredients and 265 targets of WFY were identified, of which 88 were identified as potential targets against KOA. Enrichment analysis showed that these targets were associated with oxidative stress, cell proliferation and apoptosis, and inflammatory response, and were involved in the regulation of Th17 cell differentiation, IL-17 signaling pathway, tumor necrosis factor signaling pathway, and other signaling pathways. Topology analysis showed that PTGS2, NOS2, ESR11, PPARG, and MAPK14 had higher degree values and were key targets of WFY in the treatment of KOA. Molecular docking analysis showed that these key targets and active ingredients had low binding energies, indicating that they had potential binding activity. Furthermore, IL-1β-induced elevation of inflammatory cytokines, PTGS2 protein expression, and phosphorylated p38/p38 ratios in chondrocytes were significantly attenuated upon WFY intervention. Our study systematically elucidated the pharmacological basis and molecular mechanism underlying WFY's therapeutic effects in KOA, substantiating its ability to suppress inflammation and regulate PTGS2 expression and p38 phosphorylation.

Diagnostic accuracy of GeneXpert in the diagnosis of spinal tuberculosis: A systematic review and meta-analysis

Tuberculosis remains a significant global health issue, with spinal tuberculosis being a severe form of extrapulmonary tuberculosis. Despite the high morbidity associated with spinal tuberculosis, effective and rapid diagnostic methods are limited. The aim of this study was to evaluate the diagnostic accuracy of the GeneXpert compared to other microbiological methods in diagnosing spinal tuberculosis. A systematic review and meta-analysis were conducted following the PRISMA guidelines. Six databases (PubMed, Scopus, EBSCO, EMBASE, ScienceDirect, and Cochrane Central) were searched for relevant studies as of August 31, 2023. Studies were selected based on predefined inclusion criteria, focusing on patients diagnosed with spinal tuberculosis and comparing GeneXpert to microbiological culture, acid-fast bacilli (AFB) staining, and polymerase chain reaction (PCR). Two authors independently performed data extraction and quality assessment, and the meta-analysis was conducted using Meta-DiSc 2.0. Fourteen studies comprising retrospective cohort, prospective cohort, and cross-sectional designs were included. GeneXpert demonstrated a pooled sensitivity of 92% (85-96%) and specificity of 71% (51-86%) compared to culture. AFB smear had the highest specificity at 80% (70- 88%) but the lowest sensitivity at 27% (20-35%). The PCR had sensitivity and specificity of 83% (67-92%) and 58% (31-81%), respectively. Substantial heterogeneity was noted across the studies. This study highlighted that GeneXpert had high sensitivity and moderate specificity in diagnosing spinal tuberculosis, making it an alternative to conventional methods. However, further validation through larger, interventional studies is necessary to standardize its use in clinical practice.

Bioinformatics and machine learning driven key genes screening for hepatocellular carcinoma

Liver cancer, a global menace, ranked as the sixth most prevalent and third deadliest cancer in 2020. The challenge of early diagnosis and treatment, especially for hepatocellular carcinoma (HCC), persists due to late-stage detections. Understanding HCC's complex pathogenesis is vital for advancing diagnostics and therapies. This study combines bioinformatics and machine learning, examining HCC comprehensively. Three datasets underwent meticulous scrutiny, employing various analytical tools such as Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, protein interaction assessment, and survival analysis. These rigorous investigations uncovered twelve pivotal genes intricately linked with HCC's pathophysiological intricacies. Among them, CYP2C8, CYP2C9, EPHX2, and ESR1 were significantly positively correlated with overall patient survival, while AKR1B10 and NQO1 displayed a negative correlation. Moreover, the Adaboost prediction model yielded an 86.8 % accuracy, showcasing machine learning's potential in deciphering complex dataset patterns for clinically relevant predictions. These findings promise to contribute valuable insights into the elusive mechanisms driving liver cancer (HCC). They hold the potential to guide the development of more precise diagnostic methods and treatment strategies in the future. In the fight against this global health challenge, unraveling HCC's intricacies is of paramount importance.