Publications by authors named "M Di Stefano"

Protein kinases are key regulators of cellular processes and critical therapeutic targets in diseases like cancer, making them a focal point for drug discovery efforts. In this context, we developed KinasePred, a robust computational workflow that combines machine learning and explainable artificial intelligence to predict the kinase activity of small molecules while providing detailed insights into the structural features driving ligand-target interactions. Our kinase-family predictive tool demonstrated significant performance, validated through virtual screening, where it successfully identified six kinase inhibitors.

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Fibro-adipogenic progenitor cells (FAPs) are a heterogeneous population of multipotent mesenchymal cells that give rise to fibroblasts and adipocytes. In response to muscle injury, FAPs are activated and cooperate with inflammatory and muscle stem cells to promote muscle regeneration. In pathological conditions, such as muscular dystrophies, this coordinated response is partially lost and an accumulation of FAPs is observed which is responsible for a maladaptive fibrosis, ectopic fat deposition and impaired muscle regeneration.

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Background: Osteoporosis treatments reduce fracture risk but cannot fully eliminate it, and the concept of treatment failure (TF) or inadequate clinical response (ICR) remains debated.

Aims: The TAILOR study aims to assess the prevalence of ICR and TF in osteoporotic women undergoing active drug treatment for postmenopausal osteoporosis.

Methods: TAILOR is a retrospective study conducted in an Italian outpatient service.

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The increase in life expectancy in recent years has resulted in a higher incidence of age-related diseases. Among these, osteoporosis and sarcopenia, collectively known as osteosarcopenia, have the most significant impact on the quality of life, general health and frailty in the elderly. As for other age-related diseases, pre-clinical studies on these conditions are primarily limited by the availability of experimental model systems.

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