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This study suggests that a combination of topical minoxidil with oral finasteride and dutasteride may promote new hair growth in androgenetic alopecia, with minor safety concerns reported.

This study found that autologous serum platelet-rich plasma improved skin quality and reduced wrinkles, texture, and pores in healthy females, partly by regulating the expression of MMP-1, tyrosinase, fibrillin, and tropoelastin.

This review discusses recent advancements in mobile technology and artificial intelligence for dermatology, offering an overview of their current applications but reports no new clinical findings.

The digital system for measuring melasma shows promise but needs more development for better accuracy and automation.

The ProScope HR is an effective, user-friendly, and affordable tool for diagnosing hair loss.

This article reviews optimal patient selection and donor site quality for successful hair transplant procedures, but reports no new clinical findings.

This study highlights the potential of novel non-invasive testing techniques to enhance the diagnosis, treatment, and care of scalp hair diseases, urging future research to improve their accuracy and efficiency.

In this study, researchers developed a machine learning model that accurately diagnosed scalp conditions like fine dandruff and perifollicular erythema with 75% and 82% accuracy, respectively, and created a user-friendly web platform for scalp health self-assessment, which achieved high user satisfaction.

A new tool helps better assess and treat hair loss in Chinese men.

Adjusting hair dryer settings improves drying efficiency and keeps hair healthy.

This study found that children with cancer showed more deviation from typical facial morphology compared to healthy controls, although the differences were not enough to distinguish patients from controls based on facial asymmetry alone.

This study introduced a novel, weakly supervised method for segmenting hair in Scanning Electron Microscope images using simple image-level annotations, achieving over 30% improvement in mean Hausdorff Distance compared to Unet and SAM, while enhancing interpretability and refinement.

This study found that the Random Forest machine learning algorithm achieved the highest accuracy, 96%, in diagnosing Polycystic Ovarian Syndrome using patients' clinical data.

This review reports that while deep learning shows promise in diagnosing scalp disorders from images, challenges remain with data quality and model interpretability, suggesting that integrating explainable AI techniques is crucial for building trust and facilitating clinical adoption.

This study developed a novel system that can automatically estimate hair loss parameters from images without expert input, reporting satisfactory results from testing on samples collected in Kolkata.

This study found that obesity and a negative body image negatively impact health-related quality of life and mental health in women with PCOS, indicating the need for healthcare professionals to address these issues with effective treatments.

In this study, researchers developed a three-dimensional ultrastructural analysis tool using serial block-face scanning electron microscopy to reveal detailed nerve networks and muscle structures in human skin tissues, demonstrating the technique's potential to study structural changes in aging or disease.

This study found that in the Thai population, hair density varies by scalp location and decreases with age, and that Asians generally have lower hair density compared to Caucasian and African populations.

In this observational study, hair density was found to vary with age, as measured in subjects from a dermatology outpatient department, with males showing a mean unit density of 104.78 and females 108.36.

This study constructed a single-cell atlas of hair follicle cells from yaks and taurine cattle, revealing that differences in WNT signaling within dermal papilla cells may be key to the yak's adaptation to cold environments on the Qinghai-Tibet Plateau.

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.

This study reported that the PM-DBiGRU model enhances aspect-level sentiment classification in drug reviews, outperforming existing methods on the newly proposed SentiDrugs dataset.

In this study, researchers used Google Search ads to gather an open access dataset of 10,408 dermatological images from over 5,000 U.S. internet users, enhancing the diversity and representativeness of skin condition images available for research and artificial intelligence development.

This study explored deep learning's potential in diagnosing scalp conditions like alopecia, psoriasis, and folliculitis, using a two-dimensional Convolutional Neural Network, achieving high accuracy and precision despite challenges of a small and uneven dataset.

This study found that self-supervised pretraining significantly improves accuracy in medical image classifiers for dermatology and chest X-ray tasks, outperforming supervised baselines and showing robustness to distribution shifts with limited labeled data.

This bibliometric review analyzes trends in published research on ocean pollution biodegradation and bioremediation over time, reporting a notable growth in related publications from 2010 to 2022.

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 found that ensemble machine learning models effectively predict hair fall by combining the strengths of individual algorithms, leading to higher accuracy, precision, and recall in identifying hair and non-hair fall instances compared to single algorithms.

Hair properties change under electromagnetic fields and are influenced by individual characteristics and the environment.

This study found that multiple genes and pathways, particularly several keratin-associated proteins, may be involved in the molecular pathogenesis of male androgenetic alopecia.