Rhabdomyolysis is a syndrome potentially fatal and has been associated with selective serotonin reuptake inhibitors (SSRIs) treatment in a few case reports. Herein, we purpose to establish the correlation between SSRIs use and rhabdomyolysis using the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) database. We conducted an analysis on reports that were submitted to the FAERS database during the period between January 1, 2004, and December 31, 2022. Four algorithms, including reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian confidence propagation neural network (BCPNN), and empirical Bayes geometric mean (EBGM), were employed to quantify the signals of rhabdomyolysis associated with SSRIs. In total, 16,011,277 non-duplicated reports were obtained and analyzed. Among 33,574 reports related to rhabdomyolysis, SSRIs were classified as primary suspected drug in 889 cases. Disproportionality analysis identified a positive signal between rhabdomyolysis and SSRIs (ROR: 2.86, 95% CI 2.67–3.05; PRR: 2.84, χ²: 1037.16; IC_0.25 = 1.39; EBGM_0.5 = 2.64). Among six SSRIs, fluvoxamine had the strongest signal (ROR: 11.64, 95% CI 8.00–16.93; PRR: 11.38, χ²: 265.51; IC_0.25 = 2.41; EBGM_0.5 = 8.31), whereas no significant signal of rhabdomyolysis was detected for paroxetine (ROR: 1.83, 95% CI 1.55–2.15; PRR: 1.82, χ²: 53.82; IC_0.25 = 0.73; EBGM_0.5 = 1.59). After excluding cases co-administered with statins, the signal of rhabdomyolysis associated with SSRIs remains significant. Our analysis reveals that there are differences in safety signals among six SSRIs in respect to the risk of rhabdomyolysis, with fluvoxamine displaying the highest risk signal, while paroxetine did not show a significant signal. Given the potentially lethal nature of rhabdomyolysis, healthcare professionals should inform patients of the potential risk of rhabdomyolysis associated with SSRIs prior to initiating treatment.

MicroRNA (miR)‑29b is a key tumor regulator. It can inhibit tumor cell proliferation, induce apoptosis, suppress tumor invasion and migration, thus delaying tumor progression. Our previous studies revealed an increased level of miR‑29b in hepatoma 22 (H22) cells in ascites tumor‑bearing mice. The present study investigated the effect of miR‑29b on proliferation and apoptosis of hepatocellular carcinoma ascites H22 cells and its association with the transforming growth factor‑β1 (TGF‑β1) signaling pathway and p53‑mediated apoptotic pathway. Briefly, H22 cells were transfected with miR‑29b‑3p (hereinafter referred to as miR‑29b) mimic or miR‑29b inhibitor. MTS cell proliferation assay and flow cytometry were used to analyze cell viability and apoptosis. The expression change of the TGF‑β1 signaling pathway and p53‑mediated apoptotic pathway were detected by reverse transcription‑quantitative PCR, western blotting and immunofluorescence. Furthermore, cells were treated with exogenous TGF‑β1 and TGF‑β1 small interfering RNA to evaluate the crosstalk between TGF‑β1 and p53 under miR‑29b regulation. The overexpression of miR‑29b decreased cell viability, increased cell apoptosis, activated the TGF‑β1 signaling pathway and p53‑mediated apoptotic pathway. Conversely, these effects were reversed by the miR‑29b inhibitor. Moreover, the effect of miR‑29b mimic was further increased after treating cells with exogenous TGF‑β1. The activation of the TGF‑β1 signaling pathway and p53‑mediated apoptotic pathway induced by miR‑29b overexpression were reversed by TGF‑β1 inhibition. In summary, these data indicated that miR‑29b has an important role in proliferation and apoptosis of H22 cells by regulating the TGF‑β1 signaling pathway, the p53‑dependent apoptotic pathway, and the crosstalk between TGF‑β1 and p53.

Background: Ankylosing spondylitis (AS), a chronic inflammatory disorder affecting axial joints, is frequently complicated by uveitis. However, the molecular mechanisms linking AS to secondary uveitis remain poorly understood. Methods: We integrated transcriptomic datasets from AS (GSE73754) and uveitis (GSE194060) cohorts to identify shared molecular pathways. Differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning (LASSO, SVM-RFE, random forest) were combined to prioritize biomarkers. Molecular docking was performed to evaluate drug-target interactions using Vina scores (≤−7 kcal/mol threshold). Results: Cross-disease analysis revealed 697 overlapping dysregulated genes (481 upregulated, 216 downregulated), enriched in GTPase signaling and immune pathways. WGCNA identified disease-specific co-expression modules (AS: brown/tan modules, r = 0.39/0.35; uveitis: brown module, r = 0.49). Machine learning nominated HLX and SLC25A20 as core biomarkers, demonstrating robust diagnostic accuracy in discovery (AS AUC: 0.688/0.700; uveitis AUC: 0.867/0.838) and validation cohorts (AS AUC: 0.653/0.667; uveitis AUC: 0.662/0.736). Immune profiling linked HLX to neutrophil infiltration (r = 0.55, p < 0.01) and SLC25A20 to T helper cell regulation (r = 0.36, p < 0.01). Molecular docking identified high-affinity ligands for SLC25A20, including amiodarone (−8.0 kcal/mol) and estradiol (−7.7 kcal/mol), with folic acid showing dual binding potential (HLX: −7.5 kcal/mol; SLC25A20: −8.2 kcal/mol). Conclusion: HLX and SLC25A20 emerge as immunologic regulators bridging AS and uveitis pathogenesis. These findings provide actionable targets for precision diagnostics and therapeutic development in AS-associated uveitis.