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Neural tube defects (NTDs) constitute the second most common congenital malformation of the central nervous system. The pathogenesis of NTDs is not entirely clear. In recent years, microRNAs (miRNAs) have become a hot spot in genetic and developmental biology research. The present study aimed to explore the potential role of miRNA-26a in NTDs and the underlying pathogenesis thereof. First, we found significantly increased miRNA-26a expression in fetuses with NTDs (p < 0.0001), which significantly downregulated EphA2 and ERK1 mRNA and protein expression levels in fetuses with NTDs compared to normal controls (p < 0.01). In addition, a dual-luciferase reporter assay showed that miR-26a negatively regulated EphA2 by directly binding with the 3′-untranslated region of EphA2. Second, the upregulation of miRNA-26a expression increased caspase 3 and 9 protein expression levels (p < 0.01) and decreased EphA2 mRNA and protein expression levels (p < 0.01), as well as ERK1 and SRF protein expression levels (p < 0.01) in mouse neural stem cells (NE-4C) and human astroblastoma cells (U87MG). Furthermore, the upregulation of miRNA-26a inhibited cell proliferation and enhanced apoptosis of NE-4C and U87MG cells (p < 0.05). Similar results were observed with the MAPK inhibitor PD98059 (p < 0.01). These results suggest that miR-26a targets EphA2, modulates phosphorylation of the MAPK/ERK (MEK) pathway, regulates SRF, and participates in regulating nervous cell proliferation and apoptosis. Dysregulation of the aforementioned mechanism may be involved in the pathogenesis of NTDs. [ABSTRACT FROM AUTHOR]

BACKGROUND: Bemisia tabaci (Gennadius) is a major damaging agricultural pest that exhibits high resistance to pyrethroid insecticides. L925I (TTA to ATA) and T929V (ACT to GTT) mutations in the para‐type voltage‐gated sodium channel (VGSC) are associated with resistance of B. tabaci to pyrethroids. Amplicon sequencing is a reliable and highly efficient method to detect the frequency of mutations linked with insecticide resistance. RESULTS: Similar frequencies of L925I and T929V mutations were obtained by amplicon sequencing and Sanger sequencing (L925I: 0.3548 vs 0.3619; T929V: 0.6140 vs 0.6381) with overlap of 95% confidence interval in the SX population of B. tabaci. In five populations of B. tabaci from China, the maximum and minimum frequencies of the two mutations were found in the LN (L925I: 0.1126; T929V: 0.8834) and JS (L925I: 0.8776; T929V: 0.1166) populations by amplicon sequencing. However, there was no significant difference in frequencies between the L925I and T929V mutations. The sum frequency of L925I and T929V exceeded 0.9688 in all populations. In addition, a combining mutation, L925 + T929V (L925I and T929V located in same allele), was found in five populations by amplicon sequencing even though its highest frequency was only 0.0157. CONCLUSION: We established an efficient approach for detecting frequency of mutation by amplicon sequencing. The frequencies of L925I and T929V in VGSC associated with pyrethroid resistance were detected in this study, which could provide foundational data for resistance management of B. tabaci. © 2021 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Background: Uterine Corpus Endometrial Carcinoma (UCEC) stands as one of the prevalent malignancies impacting women globally. Given its heterogeneous nature, personalized therapeutic approaches are increasingly significant for optimizing patient outcomes. This study investigated the prognostic potential of cellular senescence genes(CSGs) in UCEC, utilizing machine learning techniques integrated with large-scale genomic data., Methods: A comprehensive analysis was conducted using transcriptomic and clinical data from 579 endometrial cancer patients sourced from the Cancer Genome Atlas (TCGA). A subset of 503 CSGs was assessed through weighted gene co-expression network analysis (WGCNA) alongside machine learning algorithms, including Gaussian Mixture Model (GMM), support vector machine - recursive feature elimination (SVM-RFE), Random Forest, and eXtreme Gradient Boosting (XGBoost), to identify key differentially expressed cellular senescence genes. These genes underwent further analysis to construct a prognostic model., Results: Our analysis revealed two distinct molecular clusters of UCEC with significant differences in tumor microenvironment and survival outcomes. Utilizing cellular senescence genes, a prognostic model effectively stratified patients into high-risk and low-risk categories. Patients in the high-risk group exhibited compromised overall survival and presented distinct molecular and immune profiles indicative of tumor progression. Crucially, the prognostic model demonstrated robust predictive performance and underwent validation in an independent patient cohort., Conclusion: The study emphasized the significance of cellular senescence genes in UCEC progression and underscored the efficacy of machine learning in developing reliable prognostic models. Our findings suggested that targeting cellular senescence holds promise as a strategy in personalized UCEC treatment, thus warranting further clinical investigation., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Wei, Lin, Huang, Wei, Mao and Fan.)