A Comparison Between Intravesical Gemcitabine Plus Docetaxel and Intravesical Bacillus Calmette-Guérin in the Treatment of Nonmuscle Invasive Naive Urinary Bladder Cancer: A Systematic Review and Meta-analysis of Oncological Outcomes.

This study evaluated the efficacy, recurrence rates, and safety of intravesical gemcitabine plus docetaxel compared to standard Bacillus Calmette-Guérin (BCG) therapy for treating naïve non-muscle-invasive bladder cancer (NMIBC), with a focus on reducing recurrence and progression concerns associated with transurethral resection (TURBT). A systematic review and meta-analysis of three original studies were conducted, assessing recurrence rates and safety profiles. The meta-analysis revealed no heterogeneity in recurrence rates between the gemcitabine plus docetaxel and BCG groups, with an overall Odds Ratio for recurrence of 0.

Machine learning identifies cytokine signatures of disease severity and autoantibody profiles in systemic lupus erythematosus - a pilot study.

Disrupted cytokine networks and autoantibodies play an important role in the pathogenesis of systemic lupus erythematosus. However, conflicting reports and non-reproducibility have hindered progress regarding the translational potential of cytokines in SLE. This study attempts to address the existing knowledge gap using multiplex cytokine assay and machine learning.

An explainable graph neural network approach for effectively integrating multi-omics with prior knowledge to identify biomarkers from interacting biological domains.

The rapid growth of multi-omics datasets, in addition to the wealth of existing biological prior knowledge, necessitates the development of effective methods for their integration. Such methods are essential for building predictive models and identifying disease-related molecular markers. We propose a framework for supervised integration of multi-omics data with biological priors represented as knowledge graphs.