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Artificial Intelligence and Data Resources Pave the Way for Personalized Kidney Disease Treatments

Artificial Intelligence and Data Resources Pave the Way for Personalized Kidney Disease Treatments

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Innovative AI tools and comprehensive datasets are transforming kidney disease treatment, enabling personalized therapies through cellular-level analysis and molecular profiling.

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Advancements in artificial intelligence (AI) and comprehensive datasets are revolutionizing the approach to kidney disease management. Traditionally, clinicians relied on trial-and-error methods to determine the most effective therapies for individual patients. Now, researchers from the Perelman School of Medicine and the Wharton School at the University of Pennsylvania have developed innovative AI tools capable of analyzing kidney cells at an individual level, significantly enhancing treatment precision. Their study, published in Nature Genetics, introduces the SISKA 1.0 Atlas—an extensive compilation derived from over a million cells across 140 human, mouse, and rat kidney samples.

This groundbreaking dataset enables the identification of 70 distinct kidney cell types shared among humans and model organisms, facilitating more reliable research and potential targeted therapies. Coupled with a new statistical approach focusing on gene programs—sets of co-regulated genes—these tools help uncover biological pathways involved in kidney diseases. The open-source platform, called CellSpectra, allows for individual patient analysis, considering species, disease, and therapy contexts, fostering personalized medicine.

 director of the Penn/CHOP Kidney Innovation Center, Dr. Katalin Susztak, emphasized that AI-driven analysis marks a shift from guesswork towards precision medicine in nephrology. Their methods addressed challenges in single-cell RNA sequencing, which previously faced issues like inconsistent cell type classification and differences between lab models and human disease.

Furthermore, the team created a protein catalog differentiating protein abundance from gene activity, revealing discordance that highlights the importance of studying protein levels directly. This approach may open new avenues for therapies tailored to specific molecular profiles, connecting protein data with clinical traits such as blood pressure and kidney function. The development of these accessible AI tools aims to enable clinicians, researchers, and scientists worldwide to implement personalized treatment strategies effectively.

Source: https://medicalxpress.com/news/2025-08-ai-tools-datasets-tailored-treatments.html

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