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Background and Objectives: IgA nephropathy represents the most prevalent form of primary glomerulonephritis around the world, with significant heterogeneity in management strategies and outcomes. We conducted a systematic review and meta-analysis to evaluate the efficacy and safety of pharmacological interventions for IgA nephropathy. Materials and Methods: We searched multiple databases through June 2025, identifying randomized controlled trials and observational studies evaluating pharmacological treatments in biopsy-proven IgA nephropathy. Primary outcomes included proteinuria reduction and estimated glomerular filtration ration (eGFR) preservation. Secondary outcomes included hard kidney endpoints and safety parameters. Random-effects meta-analyses were performed with comprehensive risk–benefit assessments. Results: Twenty-five studies were included. B-cell/plasma-cell-targeted therapies showed significant proteinuria reduction (−34.0% [95% CI: −45.7, −22.3%]), complement pathway inhibitors demonstrated superior eGFR preservation (+5.8 mL/min/1.73 m2/year [95% CI: 2.4, 9.2]). Systemic corticosteroids showed observed hard outcome benefits (HR 0.37 [95% CI: 0.26, 0.52]) but highest adverse event risk (RR 3.28 [95% CI: 2.11, 5.09]). Novel agents showed projected favorable effects (B-cell: HR 0.38; complement: HR 0.42) pending validation. Conclusions: Novel targeted therapies, especially B-cell/plasma-cell-targeted agents and complement pathway inhibitors, show promising risk–benefit profiles. However, longer-term data and standardized eGFR slope reporting are needed to confirm these findings compared to other immunosuppressive agents.

Background/Objectives: Diabetic macular edema (DME) is a leading cause of vision loss in diabetic patients, with anti-vascular endothelial growth factor (anti-VEGF) therapy being the standard management. However, treatment response varies significantly among patients, necessitating predictive tools. This systematic review and meta-analysis evaluated the diagnostic accuracy of artificial intelligence (AI) models in predicting anti-VEGF treatment response in DME patients. Methods: We conducted a dedicated literature review following PRISMA 2020 guidelines, searching PubMed, Web of Science, Embase, Scopus, and Cochrane Library databases from inception up to 30 September 2025. Studies evaluating AI-based prediction models for anti-VEGF response in DME patients were included. The primary outcomes were sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). A bivariate random-effects meta-analysis was performed using available diagnostic accuracy data. Results: From 3107 participants across 18 studies, six studies with 427 participants provided complete diagnostic accuracy data for meta-analysis. The pooled sensitivity was 86.4% (95% CI: 82.1–90.1%) and the specificity was 77.6% (95% CI: 72.8–82.0%). The summary AUC was 0.89 with a diagnostic odds ratio of 22.0 (95% CI: 12.8–37.9). AI models demonstrated superior performance compared to other methods in 87.5% of comparative studies. Moderate heterogeneity was observed (I2 = 45.2%). Conclusions: AI models demonstrate good diagnostic accuracy for predicting anti-VEGF treatment response in DME patients, with a promising role for improving personalized management strategies and improved outcomes.