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This study developed a new neural network model (AA-GAN-AB-MTEDeep) to enhance Alopecia Areata classification using synthetic scalp images, achieving an accuracy of 96.94%.

In this paper, Chemistry42—a software integrating AI with computational and medicinal chemistry—demonstrated efficiency in designing novel molecular structures targeting DDR1 and CDK20, with properties validated in both in vitro and in vivo studies.

This study systematically examines how smart biomaterials used with DLP technology can enhance bioprinting in tissue engineering and regenerative medicine, while also identifying current challenges and future research needs.

This study concluded that the GIANT brain atlas, which integrates genetic and neuroanatomical variations, provides a more accurate representation of brain structure than traditional neuroanatomical atlases, allowing for better exploration of genetic influences on the brain.

In this study, the proposed MSF-VMDNet, combining dual encoder networks with a multi-frequency domain mechanism, significantly outperformed existing methods in segmenting skin cancer tissues from histological slide images, achieving high accuracy with an MIoU of 95.37% and a Dice coefficient of 95.11%.