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This article presents a facial hair removal treatment protocol from a clinical laser nurse perspective, discussing best practices but reporting no new clinical findings.

This study found that a combined machine learning model outperformed individual networks in diagnosing scalp conditions using visual data, enhancing prediction accuracy and early detection, particularly in settings with limited resources.

This study conducted a comprehensive analysis of methods for assessing surface roughness, highlighting that contact methods using profilometers provide accurate results for machine parts, while non-contact optical and laser methods offer high precision and efficiency without damaging surfaces.

This study reported that AutoML effectively modeled itching and pain development, as well as app use, in patients with chronic eczema or psoriasis using a smartphone monitoring app, revealing that factors like BMI, age, and disease activity significantly influenced app engagement.

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.

Smart microneedles using advanced tech could improve psoriasis treatment.

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 study fabricated a polyamide fiber containing amber particles and characterized its potential for textile applications, noting that the chemical structure of amber remained unchanged after grinding and integration.

This study evaluated machine-learning models to predict PCOS among reproductive-aged women in Bangladesh, finding that the XGBoost model achieved high accuracy (99.63%) and effectiveness, particularly when prioritizing clinical features over psychological ones in the predictive process.

This study suggests that estimating autism likelihood as early as one month after birth may enable more precise early intervention for children with developmental support needs, potentially improving diagnosis, workflows, and reducing service wait times.

This review discusses 'vibe coding', a new approach that allows clinicians with minimal coding skills to utilize machine learning tools for medical research by using natural language directives to generate and refine code through AI-driven platforms.

This study analyzed gene expression data to identify key genes involved in severe forms of alopecia areata, discovering four immune monitoring genes (LGR5, SHISA2, HOXC13, S100A3) with potential for early diagnosis and better understanding of the disease's biological mechanisms.

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.

This study developed a support vector machine model using trichoscopic patterns to accurately classify androgenic alopecia severity, with an accuracy of 94.3% in training and 90.0% in test datasets.

This study found that a new FAW-FS algorithm improved recognition of depression in patients with androgenic alopecia, and comprehensive psychological interventions positively impacted their rehabilitation outcomes.

This study found that machine learning techniques, such as Random Forest, SVMs, and KNN, can significantly improve the early detection and determination of hair loss, potentially transforming treatment with more accurate and personalized approaches compared to traditional methods.

In this study, researchers evaluated various machine learning models for predicting type 2 diabetes risk, finding that Random Forest and K-NN models performed best in terms of precision, recall, accuracy, and other metrics using common symptoms as features.

This review consolidates various rational design strategies for nanozymes, emphasizing the mechanisms needed for precise design and exploring their applications in treating inflammatory diseases, diagnosing diseases, and environmental uses, while also discussing the challenges and future prospects in this emerging field.

In this study, researchers identified genes IRF2BP2 and EGFR as key to understanding double-coated fleece formation in Hetian sheep, offering insights that may advance machine learning-driven multi-omics selection models in sheep breeding.

Innovative methods are reducing animal testing and improving biomedical research.

This study developed a hat-shaped device with wearable sensors to estimate scalp moisture content using machine learning, demonstrating that it can provide accurate measurements comparable to professional scalp analyzers without the need for high-cost equipment.

This study found that a pre-trained image processing technique accurately classified scalp conditions with 85% accuracy, suggesting potential for automated diagnosis and treatment selection.

This study used machine learning to identify molecular predictors of drug response in alopecia areata, suggesting a tool for predicting treatment efficacy based on gene signatures.

This study found that integrating machine learning enhances the predictive accuracy of forensic DNA phenotyping from low template DNA, achieving high accuracy for traits like eye color, although challenges remain for admixed populations and complex traits.

This review examines the use of computer vision techniques, specifically deep learning architectures and image processing algorithms, for detecting and assessing skin conditions like vitiligo and dermatitis, and highlights the need for disease-specific datasets to improve automated diagnostic tools in dermatology.

This review discusses advancements in skin microphysiological systems, such as 3D bioprinting, skin organoids, and skin-on-a-chip, and their effectiveness in emulating human skin functions for research and preclinical applications, highlighting the potential for replacing animal testing with these innovative technologies.

This review highlights new perspectives in genomics and epigenomics for skin rejuvenation, comparing innovative strategies like senolytics and DNA repair modulators with classical treatments, and emphasizing the importance of tailoring therapies using individual genomic profiles for personalized anti-ageing approaches.

In this retrospective cohort study, researchers found that exposure to 5-alpha reductase inhibitors and spironolactone in female alopecia patients was not linked to an increased risk of developing malignant or benign gynecologic tumors when compared to minoxidil use.

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.

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.