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

This study developed a model for detecting sparse hair clusters using enhanced object detection neural networks and medical images, which accurately identifies and counts sparse hair clusters with greater accuracy and efficiency than existing methods.

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, 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.

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 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.

This study introduced the KRDQN predictive framework, which outperformed existing methods in predicting adverse drug reactions and provided interpretable insights into drug mechanisms, aiding pharmacovigilance and clinical decision-making.

In this study, researchers developed a predictive model for the onset of alopecia areata by analyzing six datasets to identify key feature genes and employing various machine learning algorithms, ultimately finding the XGBoost model most effective for clinical application.

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%.

This study used machine learning models to analyze PCOS symptoms for early diagnosis, finding Support Vector Machine and VGG16 algorithms achieved high accuracy rates of 94.44% and 98.29% respectively.

Lichen planopilaris is a rare, chronic condition causing hair loss, mainly in middle-aged women, and early treatment is important to prevent permanent baldness.

This study found that hippocampus sparing whole brain radiation therapy (HS-WBRT) using multi-field intensity modulated radiation therapy prevents alopecia without compromising cognitive function compared to traditional whole brain radiation therapy.

This study found that horizontal sections of scalp biopsies in patients with Central Centrifugal Cicatricial Alopecia often reveal follicular miniaturization, inflammation, and scarring, which can guide personalized treatment.

This review highlights cellulose-based materials' potential in skin disease treatments, emphasizing their smart, responsive design for various conditions, with insights into their roles in wound healing, infection prevention, and wearable monitoring.

This research developed a pig graph pangenome assembly of 27 genomes, revealing the importance of structural variations in adaptation and breed-specific traits, with BTF3 identified as a key gene influencing intramuscular fat and meat quality.

This article discusses the complexities of treating hair and scalp disorders in African American patients, emphasizing the importance of understanding their specific hair care practices and the unique impact on quality of life, but reports no new empirical findings.

This study identified non-structural proteins in preschool children's scalp hair that suggest potential biomarkers for brain development, immune function, and stress response with heritability and age-related differences.

This review discusses the integration of Artificial Intelligence in non-surgical cosmetic procedures, highlighting its potential to improve personalized aesthetic care and operational efficiency while addressing ethical and regulatory challenges; it presents no new clinical findings.

This study discovered that the gene Tfap2b identifies a melanocyte stem cell population in zebrafish, essential for regenerating melanocytes with multi-fate potential into adult pigment cells.

This review explores the role of the TMPRSS2 gene in facilitating SARS-CoV-2 infection and its potential as a therapeutic target in COVID-19 and other respiratory infections, highlighting challenges in developing selective inhibitors.

This review synthesizes current research on how the interaction between the skin microbiome and exosome-mediated communication influences hair disorders, and discusses how microbiome restoration and exosome-based therapies could advance treatments for conditions like alopecia by promoting hair regrowth and follicular health.

This article discusses the minimal research on hair growth physiology and highlights Dr. Masumi Inaba's contributions to understanding androgenetic alopecia, but reports no new experimental results.

This review discusses potential causes of alopecia and hair overgrowth in pediatric patients, detailing diagnostic techniques and treatments, but presents no new research findings.

This study reported that pathways related to apoptosis, cell proliferation, and WNT signaling might be key drivers of hair loss in androgenetic alopecia, guiding potential targets for therapy development.

This review summarizes discussions from a 2010 stem cell biology meeting, covering the origin, behavior, and therapeutic potential of pluripotent and multipotent stem cells, without reporting new experimental findings.

In this study, researchers observed that individuals with alopecia areata exhibited significant changes in scalp microbiota diversity and composition, which were linked to disease severity and inflammation markers, though it remains unclear if these microbial shifts are a cause or a result of hair loss.

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 study, researchers examined a patient with ZMYM2::FGFR1 fusion-positive leukemia, finding that Pemigatinib showed efficacy, while Ponatinib resistance was linked to a specific FGFR1 mutation. Other FGFR inhibitors demonstrated high effectiveness in ex vivo assays.

This review discusses the use of zebrafish larvae as an alternative model for studying ototoxicity, facilitating large-scale screening for otoprotective compounds, but reports no new clinical results.

In this article, the authors describe how drug repurposing, supported by large-scale data and artificial intelligence, can make drug discovery more cost-effective and expedient compared to traditional methods, despite certain regulatory challenges.