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This review highlights recent research using single-cell RNA sequencing and machine learning in wound healing, revealing significant insights into fibroblast diversity, immune cell dynamics, and the spatial organization of cells, which may transform therapeutic strategies for chronic wounds, fibrosis, and tissue regeneration.

This study presents a machine learning algorithm that achieved 89.5% accuracy in predicting hair health using factors like spatial-temporal images, age, and gender.

This study found that progesterone enhances the survival of newborn neurons in the hippocampus of adult male mice, which is associated with improved spatial learning and memory.

This study found that progesterone increased the survival of newborn neurons and enhanced spatial learning and memory in adult male mice, independent of neuron production changes.

This research reported that using J48 algorithms with bagging improves prediction accuracy of hair health through machine learning by analyzing factors like spatial-temporal images, gender, and age, achieving a real-time performance of 89.5%.