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This study found that a Faster Residual Convolutional Neural Network model achieved an accuracy of 84.3% in recognizing alopecia areata and various scalp conditions from image databases.

This study found that schizophrenia-derived neurons exhibited impaired differentiation and mitochondrial dysfunction, suggesting a potential link between these factors and neurodevelopmental processes in schizophrenia.

This study suggests that pre-trained Transformers only outperform syntactic and lexical neural networks on unseen DarkNet sentences after extreme domain adaptation, indicating unexpected advantages from their massive pre-training corpora.

This study used machine learning models, such as Convolutional Neural Networks (CNN), to accurately differentiate False Daisy from similar plants like Smooth Joyweed.

This study introduces a novel method for capturing and digitally rendering the color appearance of physical hair samples using deep neural networks.

This study found that specific DNA variants in the TCHH, WNT10A, and FRAS1 genes are associated with predicting straight hair in Europeans, showing high sensitivity but low specificity, especially using a neural networks approach.

In this study, deep learning models accurately predicted gene expression in whole slide images of colorectal cancer, with convolutional neural networks outperforming transformer and graph-based approaches in spatial RNA pattern prediction.

This study found that secretory proteins in DFCM play crucial roles in regulating nerve restoration by forming significant protein interaction networks during the wound repair process.

This study introduced a single-stage network using large kernel attention that effectively restores high-resolution images by capturing both global and local details, reducing parameters and improving processing speed.

A deep learning model was developed to help diagnose trichothiodystrophy by analyzing hair patterns.

Skin and hair cells release serotonin and histamine naturally, which could help improve skin health.

In this study, the integration of artificial intelligence in medicine was discussed, highlighting its potential to enhance diagnostic processes, optimize therapies, and provide advanced patient monitoring despite challenges like data inconsistency and limited model transparency.

This review examines the current state of automated vitiligo detection systems, noting a lack of large, diverse datasets and consistent imaging conditions, while comparing traditional and modern machine learning approaches to improve reliability and applicability.

This review discusses the potential of microneedles to enhance transdermal drug delivery by expanding the range of deliverable drugs, highlighting fabrication methods, material choices, and applications, but notes existing challenges with sustained delivery, cost-effectiveness, and large-scale production.

In this study, HDAC inhibitors promoted the differentiation of rat C6 glioma cells through the production of 5α‐reduced neurosteroids, enhancing serotonin-stimulated BDNF gene expression.

This study found that a deep learning approach successfully predicted three types of hair and scalp diseases with high accuracy, despite challenges in dataset availability and image variety.

This study highlights the use of data mining techniques to identify malnutrition and nutritional deficiencies contributing to global health burdens, but it presents no new research findings.

EfficientNet improves accuracy in diagnosing hair loss stages.

This review found that AI in dermatology shows high diagnostic accuracy comparable to experienced clinicians, but integration into clinical practice faces challenges requiring further research.

HairLossMultinet accurately classifies hair damage with 98% accuracy but needs a more diverse dataset for broader use.

In this study, the integration of obesity-related features and machine learning techniques significantly enhanced cardiovascular disease detection, with the XGBoost classifier achieving a 74% accuracy rate and improved metrics compared to other models.

This review explores the convergence of functional biomaterials, biosensing, and AI technologies in bioengineering, highlighting applications in cancer modeling and regenerative medicine, while addressing challenges like biofouling and dataset integration.

This article introduces the concept of organ size control in regeneration, regulated by the Hippo signaling pathway, but reports no new experimental results.

This study validated a protocol for inducing surface ectoderm differentiation from human induced pluripotent stem cells and highlighted the role of TGFβ signaling pathways in this process.

This study found that neurosteroids and benzodiazepines decrease network excitability in neuronal cultures, with specific long-term depressive effects on inhibitory neurons.

This study found that keratin filament networks in SG1 cells of mice undergo dynamic changes during cornification, impacting the barrier function of the stratum corneum.

This review discusses neural mechanisms involved in hair growth control, focusing on piloneural interactions, but reports no clinical findings; the authors suggest further exploration for managing hair growth disorders.

This study identified aging-related epigenetic and transcriptomic biomarkers and suggested that curcumin might target and inhibit the JUN gene, implicating potential therapeutic strategies against aging.

In this study, the BaldGraphFormer framework, integrating visual and clinical data, outperformed unimodal baselines in early-stage androgenetic alopecia detection, achieving an F1-score of 97.62% and macro-average AUC of 0.992, suggesting its potential to support dermatological decision-making and early intervention.

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%.