Search

everythingexperts & clinicslearnfaqsresearchcommunity

for

Search:↓

This article reviews the generation of complex integument patterns through genetic, chemical, and environmental influences, with applications in tissue engineering, but reports no new experimental results.

This study suggests that the rs2075876 variant in the AIRE gene may significantly increase susceptibility to alopecia areata in the examined male population.

This study observed that aberrant DNA methylation in murine and human cutaneous squamous cell carcinoma likely contributes to the silencing of tumor suppressor genes, notably affecting the FILIP1L gene.

This review examines how mathematical modeling of the MAPK pathway, enhanced by single-cell proteomic data, improves understanding of regulatory mechanisms, predicts system behavior, and guides experimental research, emphasizing recent developments in modeling and inference.

This study experimentally validated Lockhart's viscoplastic framework for tip growth in Arabidopsis root hairs by demonstrating alignment with observed growth rates and estimating yield turgor pressure and cell wall viscosity, offering a methodology adaptable to other species and conditions.

Accurate protein modeling can help develop new treatments for prostate cancer and other diseases.

This study concluded that specific SNPs in the TRPS1 gene significantly alter its protein structure, affecting interactions and contributing to the development of congenital hypertrichosis.

This study suggests that keratinocytes derived from hair follicles of patients with atopic dermatitis provide a useful model for investigating AD-related inflammation, as they showed greater expression changes of AD markers when stimulated with type 2 cytokines compared to those from healthy donors.

In this study, researchers found that a PER3 gene SNP may be pathogenic for a new subtype of dyschromatosis universalis hereditaria, especially when combined with a SASH1 mutation.

This review discusses the role of zebrafish as a model for studying human hereditary pigmentary disorders and reports no new experimental results, emphasizing their genetic similarities and the genetic tools available.

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 developed a deep learning model for the automated early detection of androgenetic alopecia using trichoscopic images, and found it demonstrated high accuracy and generalizability in a Korean clinical cohort, achieving a 90% accuracy in external validation.

This study explored the creation of 3D-printed baricitinib pills using polylactic acid and found that the doughnut-shaped tablets increased surface area and drug release, with 59% released from the higher concentration pill over 24 hours.

In this study, researchers used computational modeling and virtual screening to identify two FDA-approved drugs, Tadalafil and Finasteride, that may effectively inhibit key proteins involved in melanoma progression, suggesting potential for new therapeutic strategies against aggressive melanoma.

Innovative methods are reducing animal testing and improving biomedical research.

In this study using genetic mouse models, researchers discovered that senescent melanocytes in nevi secrete osteopontin, which activates hair stem cells, enhancing hair growth; this process is mirrored in human hairy nevi, suggesting a potential therapeutic target for regenerative disorders.

This study utilized a pigmented human epidermal equivalent model to incorporate melanocytes into the epidermis and found enhanced differentiation potential compared to conventional in vitro systems, reflecting in vivo cellular trajectories and highlighting melanocyte-to-keratinocyte communication pathways.

This study constructed a comprehensive atlas of prenatal human skin, revealing that innate immune cells, such as macrophages, play a crucial role in skin morphogenesis by interacting with non-immune cells, influencing hair follicle formation and angiogenesis beyond their traditional immune functions.

In this study involving genetically modified rats, researchers observed that specific mutations in the vitamin D receptor affect calcium levels and bone formation, emphasizing the receptor's role in maintaining healthy bone density and its importance in regulating hair cycle and skin health.

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 found that a data-driven model using XGBoost effectively predicts individualized responses to minoxidil for androgenetic alopecia, outperforming traditional methods in accuracy and reliability.

This study developed a reliable and convenient cell-based model to screen for type II 5α-reductase inhibitors, identifying Curcumae longae and Mori ramulus extracts as potential candidates.

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.

In this study, single-cell sequencing revealed clonal expansions of CD4+ and CD8+ T cells in murine and human alopecia areata, supporting the development of predictive models for human disease.

This study demonstrated that molecular dynamics simulations of keratin could effectively model the mechanical properties of hair, aligning well with experimental data, especially when conducted in vacuum conditions.

This study found that among various predictive models for baldness risk, Random Forest Regression performed best with the lowest mean squared error and highest R², indicating strong predictive accuracy, especially with complex datasets, while Linear Regression was better suited to simpler datasets.

In this study using the C3H/HeJ mouse model of alopecia areata, researchers found that hyperexpanded CD8⁺ T cell clones were sufficient to initiate disease, establishing a causal link between T cell clonality and pathogenicity.

This study reports that using machine learning models, particularly XGBoost and Random Forest, can accurately predict PCOS phenotypes based on non-invasive data, with cycle length as the most significant predictor.

This review discusses the data resources and computational models used in drug repositioning, highlighting their role in discovering unknown drug mechanisms and reports no new empirical results.

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%.