Advancing therapeutic frontiers in urothelial carcinoma: targeted strategies and clinical implications.

Urothelial carcinoma poses significant challenges in clinical management due to its aggressive nature and high prevalence. While most diagnoses involve localized disease, advanced urothelial carcinoma (aUC) often leads to short overall survival (OS). Historically, platinum-based chemotherapy has been the primary treatment for aUC, although its efficacy is limited.

Artificial intelligence in triage of COVID-19 patients.

In 2019, COVID-19 began one of the greatest public health challenges in history, reaching pandemic status the following year. Systems capable of predicting individuals at higher risk of progressing to severe forms of the disease could optimize the allocation and direction of resources. In this work, we evaluated the performance of different Machine Learning algorithms when predicting clinical outcomes of patients hospitalized with COVID-19, using clinical data from hospital admission alone.

Exploring transparency: A comparative analysis of explainable artificial intelligence techniques in retinography images to support the diagnosis of glaucoma.

Machine learning models are widely applied across diverse fields, including nearly all segments of human activity. In healthcare, artificial intelligence techniques have revolutionized disease diagnosis, particularly in image classification. Although these models have achieved significant results, their lack of explainability has limited widespread adoption in clinical practice.

Optimized pipeline for personalized neurobiological insights from single patient-derived Neurospheres.

This pipeline presents a refined approach for deriving personalized neurobiological insights from iPSC-derived neurospheres. By employing Tandem Mass Tag (TMT) labeling, we optimized sample pooling and multiplexing for robust comparative analysis across experimental conditions, maximizing data yield per sample. Through single-patient-derived neurospheres-composed of neural progenitor cells, early neurons, and radial glia-this study explores proteomic profiling to mirror the cellular complexity of neurodevelopment more accurately than traditional 2D cultures.