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This study evaluated different convolutional neural network architectures for diagnosing scalp and hair diseases, and found that VGG16 and VGG19 consistently outperformed other models in accuracy, demonstrating their effectiveness and reliability in this medical application.

This review discusses the dermatologic manifestations and management of polycystic ovary syndrome, underscoring the need for mechanism-based, personalized treatments and integrated mental health support, but reports no new clinical results.

Certain immune cells in atopic dermatitis skin could be targeted for treatment.

This study found that a random forest algorithm most effectively detected COVID-19, with high specificity and accuracy, among 10,862 individuals in an Iranian hospital setting.

This study suggests that histological features of primary melanoma can partially predict lymph node metastasis using AI, achieving a best prediction AUROC of 0.65.

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

This article reviews key questions in skin biology, particularly the mechanisms of hair follicle patterning and the role of stem cells in the epidermis, reporting no new results.

This review discusses the effectiveness and potential complications of medium-depth and deep chemical peels for improving skin appearance and reports no clinical results; it highlights the need for careful procedure application.

This study suggests that microvascular damage and platelet deregulation may persist in ulcerative colitis patients even during remission, remaining as disease-associated molecular signatures.

This study developed a deep learning-based method that identified 3,620,516 potential drug-disease associations, suggesting a promising tool for large-scale virtual screening in drug research.

In this study, a deep neural network model called regX was developed to prioritize driver regulators for cell state transitions by incorporating gene-level regulation and interactions, showing potential therapeutic targets in type 2 diabetes and hair follicle development when applied to single-cell multi-omics data.

This study introduced Neurospectrum, a framework that effectively identifies meaningful neural dynamics by encoding neural activity into latent trajectories, and reported that it outperformed traditional methods in tracking synchronization, reconstructing stimuli, and identifying fMRI biomarkers in various datasets.

This study found that ulcerative colitis is associated with diverse molecular changes and chronic inflammation, with some biomarker levels partially recovering during remission.

This editorial emphasizes the importance of recognizing the risk of deep vein thrombosis (DVT) during surgery beyond the commonly focused concern of anesthetic toxicity, but it does not present new findings.

Propofol can easily cause deep sedation, so caution is needed when using it for procedures.

This study introduces PROMETHEUS, a framework that organizes causal claims from scientific texts into navigable and persistent "causal atlases," enhancing research by highlighting localized evidence, agreement, and contradictions within complex data sets.

This study reports that an Explainable AI-based hair health prediction system using a novel hybrid model outperformed traditional machine learning methods, achieving high accuracy in predicting key factors and providing personalized recommendations.

In this study, individuals with alopecia and psoriasis reported higher body shame and depressive symptoms and lower self-compassion compared to the general population, with self-compassion mediating the relationship between external shame and anxiety and depression symptoms.

This study found that their newly developed ADRL framework significantly improved the accuracy of scalp tissue layer segmentation in HR-MR images compared to existing methods.

In this study, the researchers developed a deep learning framework to pre-emptively screen for adverse drug effects, showing strong predictive performance, including for increased bleeding risks with edoxaban compared to other anticoagulants.

This study developed a high-performance deep learning model using the Inception-ResNet v2 architecture to classify 10 hair disease classes, achieving an accuracy of 94.7% and balanced precision, recall, and F1-scores of 0.94, suggesting reliability for automated dermatology diagnostics.

This study proposes a deep learning model using CNN with VGG16, VGG19, and MobileNetV2 architectures, achieving high accuracy in classifying scalp diseases from images, potentially facilitating diagnosis and treatment via mobile devices.

This study found that inadequate thickness of deep dermal tissue in pigs is a key factor leading to fibrosis, with fibrosis likely serving a compensatory function for skin thickness.

This overview details the Dermatologic Surgery journal's focus on dermatologic procedures but provides no new research findings.

This article discusses various methods of facial enhancement, such as botulinum toxin and fillers, and emphasizes their temporary nature but reports no new clinical results.

This case report describes a 10-year-old girl with advanced puberty signs and resistant acne, highlighting potential early-onset puberty with associated growth patterns.

This study found that endogenous EGF may facilitate the healing of second-degree scald wounds in mice through autocrine, paracrine, and endocrine mechanisms.

This study introduces a novel method for digitizing hair color that accurately captures and renders the color appearance of physical hair samples in synthetic images.

This case report describes a female triathlete with a femoral stress fracture complicated by DVT and CRPS, highlighting the need for a multidisciplinary approach in treating the female athlete triad.

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.