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This study describes an AI-powered system for diagnosing dermatological conditions, achieving a weighted score of 0.87 in both contextual understanding and diagnostic accuracy, suggesting it could enhance tele-dermatology applications by supporting remote consultations and care access in underserved regions.

In this study, treatment with insulin sensitizers significantly increased adipose tissue GLUT4 mRNA expression and improved insulin resistance, menstrual patterns, and androgen profiles in PCOS patients, with rosiglitazone being more effective than metformin.

This study observed that nevus melanocytes do not exhibit signs of senescence, suggesting that their growth arrest is due to cell interactions and not directly caused by BRAF activation.

This study suggests that the growth arrest of nevus melanocytes is not due to oncogene-induced senescence but rather to collective cell interactions, similar to those in normal tissue size regulation.

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.

In this study, researchers used a reporter mouse model to identify and characterize distinct subtypes of dopaminergic neurons in the gut's enteric nervous system, revealing novel populations with potential implications for understanding their roles and vulnerabilities in disease.

This study evaluated a novel, user-friendly approach for detecting hairfall trends over time using machine learning models. The Temporal Fusion Transformer model demonstrated high accuracy in identifying anomalies in hair shedding patterns, potentially aiding in the early detection of health risks related to hormonal fluctuations.

This study introduces ScalpViT, a new deep learning model that accurately diagnoses visually similar scalp diseases with 94.3% accuracy, outperforming other methods like ResNet-50 and EfficientNet-B3, and providing dual visual explainability through GradCAM and Attention Rollout, potentially benefiting diagnosis in resource-limited settings in India.

In this study, researchers developed a hybrid deep learning model called ScalpViT that accurately diagnosed scalp diseases with 94.3% accuracy, surpassing existing methods like ResNet-50 and EfficientNet-B3, and providing visual explainability for clinicians using GradCAM and Attention Rollout techniques.

In this study, researchers developed ScalpViT, a novel deep learning model, to improve the automated diagnosis of visually similar scalp diseases, achieving 94.3% accuracy and outperforming existing models like ResNet-50 and EfficientNet-B3 when tested on a diverse dataset of 7,000 images.

In this study, researchers identified that keloid fibroblasts respond to neurotransmitters from catecholaminergic nerves by producing bone matrix proteins, mediated by β1-adrenergic receptor activation, leading to fibro-osseous reprogramming; blocking this signaling in a rodent model prevented the development of keloid-like pathology.

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.

The ZIP13 variant is linked to abnormal hair quality.

This study, using a reporter mouse line, characterized diverse subtypes of dopaminergic neurons in the enteric nervous system, identifying unique subtypes with potential roles in gut function and disease.

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 highlights that while advances in dermatology, such as biologics and JAK inhibitors, have improved treatments for conditions like atopic dermatitis and psoriasis, challenges remain, including issues with lasting efficacy and the need for more personalized therapies.

In this study, the authors found that syntax-based neural networks performed comparably to pre-trained Transformers on tasks involving definitely unseen sentences, suggesting they are a more transparent and parameter-efficient alternative for certain Natural Language Processing applications.

This study found that dutasteride may offer neuroprotection in early-stage Parkinson's disease by preventing loss of striatal dopamine and altering steroid levels in MPTP-lesioned mice.

This study found no evidence that HDGF expression transforms melanocytes into tumors in a mouse model, although it may play a role in cell differentiation and tumor progression.

In this study, researchers developed a method called the Stiffness-Angle Law and found that hair treated with caffeine-infused shampoo showed a 13.2% increase in stiffness, as measured by their novel evaluation technique.

This study found that activating CXCR2 on ras-transformed keratinocytes promotes migration and tumor development in a mouse skin model.

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 research review highlights the potential of exosome therapy to transform stroke treatment, reporting that in animal models, exosomes can reduce neuroinflammation, oxidative stress, and cell death, while promoting brain repair and regeneration. However, more evidence is needed before clinical applications in humans are established.

This review summarizes the state of organoid technology, highlighting its potential to transform biomedical research and regenerative medicine by providing accurate models for disease study, drug discovery, and potentially developing functional organs for transplantation.

This study developed a deep learning model using the VGG architecture to predict hair disorders and provide tailored therapeutic suggestions, showing reliable recognition of conditions like dandruff, fungal infections, and alopecia by analyzing images of hair and scalp.

This study developed a hair-on-a-chip model that supports the maturation of hair-follicle-like tissue under long-term culture conditions, suggesting its promise for studying hair regeneration and testing scalp-targeted therapies.

In this study, researchers used a mouse model to identify an adipocyte-to-fibroblast conversion pathway that promotes mast-cell recruitment and dermal fibrosis in atopic dermatitis, suggesting a potential therapeutic approach to block this process and reduce inflammation and fibrosis.

This study reports that fly blood progenitors in a long-term organ culture model undergo symmetric cell divisions influenced by cell size and orientation, with infection triggering changes in cell differentiation kinetics.

This study found that epidermal-specific deletion of integrin α3β1 significantly reduces papilloma formation in a skin carcinogenesis model by modulating HB stem cell behavior and CCN2 expression.

In this study, researchers presented a protocol for generating planar skin organoids from human induced pluripotent stem cells, which include hair follicles and other skin structures, offering a relevant in vitro model for investigating skin physiology, pathogen interactions, and drug responses.