Identification of (E)-1-((1H-indol-3-yl)methylene)-4-substitute-thiosemicarbazones as potential anti-hepatic fibrosis agents.

Liver fibrosis remains a global health challenge due to its rapidly rising prevalence and limited treatment options. The orphan nuclear receptor Nur77 has been implicated in regulation of autophagy and liver fibrosis. Targeting Nur77-mediated autophagic flux may thus be a new promising strategy against hepatic fibrosis. In this study, we synthesized four types of Nur77-based thiourea derivatives to determine their anti-hepatic fibrosis activity. Among the synthesized thiourea derivatives, 9e was the most potent inhibitor of hepatic stellate cells (HSCs) proliferation and activation. This compound could directly bind to Nur77 and inhibit TGF-ß1-induced α-SMA and COLA1 expression in a Nur77-dependent manner. In vivo, 9e significantly reduced CCl4-mediated hepatic inflammation response and extracellular matrix (ECM) production, revealing that 9e is capable of blocking the progression of hepatic fibrosis. Mechanistically, 9e induced Nur77 expression and enhanced autophagic flux by inhibiting the mTORC1 signaling pathway in vitro and in vivo. Thus, the Nur77-targeted lead 9e may serve as a promising candidate for treatment of chronic liver fibrosis.

Identification of potential biomarkers in the peripheral blood of neonates with bronchopulmonary dysplasia using WGCNA and machine learning algorithms.

Bronchopulmonary dysplasia (BPD) is often seen as a pulmonary complication of extreme preterm birth, resulting in persistent respiratory symptoms and diminished lung function. Unfortunately, current diagnostic and treatment options for this condition are insufficient. Hence, this study aimed to identify potential biomarkers in the peripheral blood of neonates affected by BPD. The Gene Expression Omnibus provided the expression dataset GSE32472 for BPD. Initially, using this database, we identified differentially expressed genes (DEGs) in GSE32472. Subsequently, we conducted gene set enrichment analysis on the DEGs and employed weighted gene co-expression network analysis (WGCNA) to screen the most relevant modules for BPD. We then mapped the DEGs to the WGCNA module genes, resulting in a gene intersection. We conducted detailed functional enrichment analyses on these overlapping genes. To identify hub genes, we used 3 machine learning algorithms, including SVM-RFE, LASSO, and Random Forest. We constructed a diagnostic nomogram model for predicting BPD based on the hub genes. Additionally, we carried out transcription factor analysis to predict the regulatory mechanisms and identify drugs associated with these biomarkers. We used differential analysis to obtain 470 DEGs and conducted WGCNA analysis to identify 1351 significant genes. The intersection of these 2 approaches yielded 273 common genes. Using machine learning algorithms, we identified CYYR1, GALNT14, and OLAH as potential biomarkers for BPD. Moreover, we predicted flunisolide, budesonide, and beclomethasone as potential anti-BPD drugs. The genes CYYR1, GALNT14, and OLAH have the potential to serve as diagnostic biomarkers for BPD. This may prove beneficial in clinical diagnosis and prevention of BPD.