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This study researched the dun coat color in Mongolian horses, finding that variations in TBX3 expression in different skin areas are linked to Bider markings, suggesting TBX3's role in this specific pigment pattern, while further investigation is needed on its regulation.

This study developed a deep-learning model that accurately diagnosed alopecia areata with an accuracy of 88.92% and distinguished its activity levels with an accuracy of 83.33%, highlighting the potential for artificial intelligence in improving the diagnosis and treatment of this autoimmune hair loss condition.

This study examines the structure of the "sinuskissen" in cat hair follicles, revealing it as a loose connective tissue susceptible to changes in aqueous flow, and describes its variable three-dimensional shapes.

In this study, the researchers analyzed skin samples from Dun Mongolian horses to uncover molecular pathways linked to the "Bider" marking, identifying differential gene expression and several pigment-related signaling pathways that may play key roles in its formation.

This study found that the dark pigmentation pattern in Dun Mongolian horses' "Bider marking" is closely associated with higher protein levels and specific localization of MITF and WNT3A, suggesting these are key factors in its formation.