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Human hair structure varies by ethnicity, and certain treatments can improve hair condition and appearance.

This study introduced a single-stage network using large kernel attention that effectively restores high-resolution images by capturing both global and local details, reducing parameters and improving processing speed.

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 suggests that passage number significantly affects in silico scratch assay results, highlighting the importance of reporting passage number for reproducibility in cell culture experiments.

This study found that taxol induces apoptosis in dermal papilla cells, affecting protein expression related to biological processes and potentially contributing to taxol-induced alopecia.

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.

This study conducted a meta-analysis of gene expression data from alopecia areata patients, identifying 5109 differentially expressed genes and highlighting enriched pathways like JAK-STAT signaling, providing insights into the disease's pathogenesis and potential treatment targets.

This study identified 63 genetic loci associated with male-pattern baldness, uncovering genes and pathways that may help develop treatments and suggesting its connection to other human conditions.

This study identified four new genetic loci associated with acne risk and highlighted key pathways involved in its genetic predisposition, potentially explaining 9.4% of acne's phenotypic variance.

This study outlines the Human Gene Correlation Analysis tool, which classifies human genes by coexpression levels and identifies overrepresented annotation terms in correlated gene groups, with no new clinical results reported.

This study found that both genetic and epigenetic factors significantly influence age-related facial skin aging, with lifestyle and environmental factors also playing a substantial role.

This study found that their proposed method for facial image restoration using Denoising Diffusion Probabilistic Models produced higher-quality results compared to traditional methods, particularly improving face recognition accuracy with different types of masks.

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.

This pilot study found that hair metabolome analysis may help differentiate pregnancy complications from healthy pregnancies by revealing significant metabolite differences between trimesters.

This study identified differentially expressed miRNAs between the telogen and anagen phases of hair growth in Wan Strain Angora rabbits, contributing to understanding miRNA-mediated regulation of rabbit hair follicle cycling.

This study finds that the images of soft-haired Kerr black holes show unique rotational, dilational, and drifting effects compared to bald black holes, linked to Noether charges and generalized symmetries.

In this study, researchers using molecular docking techniques found that 12-Methoxy carnosic acid from rosemary shows promising potential as a natural inhibitor of the 5 alpha reductase enzyme, which is involved in hair loss, suggesting its efficacy compared to finasteride.

In this study, improved hidden Markov algorithms based on Bayesian methods enhanced the resolution and segmentation accuracy of low-dose CT images significantly more than naive Bayesian methods.

This study investigated protein expression changes during mouse hair follicle cycles and suggested their involvement in biological networks, potentially identifying targets for hair disease therapy.

In this research, experienced clinicians noted inconsistency in the reproducibility of the commonly used Hamilton-Ludwig scales for assessing pattern hair loss severity in photographic assessments, leading to the proposal of a new 5-point scale for staging female hair loss.

In this study on testosterone-induced benign prostatic hyperplasia in rats, hydroxychloroquine significantly reduced prostate-related markers and enhanced therapeutic effects when combined with finasteride, suggesting potential as a new treatment approach.

This study identified distinct plasma miRNA profiles in alopecia areata that may aid in diagnosis and therapy, but further validation is needed to confirm these exploratory findings.

This article proposes a machine learning framework for classifying healthy hair and alopecia areata using image processing and classification techniques, but does not report new clinical findings.

This review highlights the potential role of mathematical and computational approaches in enhancing and accelerating the clinical translation of regenerative medicine products but reports no new clinical findings.

In this study, a computer-aided imaging system accurately measured the width of balding areas in female pattern hair loss, correlating strongly with clinical staging severity.

This paper presents a new method using Convolutional Neural Networks for detecting hair and scalp diseases, aiming to enhance diagnostics accessibility through a web-based platform integration.

In this study, researchers found that synchronization between expanding epithelial cells and background mesenchymal cells in the mouse hair cycle may be maintained by inhibitory regulation, with potential mediators of this regulation identified.

This review examines advancements in deep learning methods for detecting dermatological conditions from dermoscopic images, summarizing available datasets and suggesting future research directions, but reports no new results.

This study introduced a novel deep clustering approach for melanoma detection from dermoscopic images, demonstrating improved performance over existing methods by mitigating class imbalance issues using a center-oriented margin-free triplet loss.

This study established a new in vitro culture system that supports the growth and dynamic self-organization of hair follicle stem cells and their progeny, promoting a stable population equilibrium.