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This study presents an algorithm that may help physicians diagnose female hair loss by synthesizing patient history, clinical examination, and various diagnostic tests.

This study found that polycystic ovary syndrome was the most common cause of hirsutism among premenopausal Algerian women.

This study found that polycystic ovary syndrome was the most common cause of hirsutism among premenopausal women in Algeria.

This study found that polycystic ovary syndrome was the most common cause of hirsutism among premenopausal Algerian women, affecting nearly 60% of participants.

This consensus statement outlines a treatment algorithm for alopecia areata, discussing when to consider systemic treatment and how to assess outcomes, without providing new clinical results.

This review discusses the development of an algorithmic guideline for treating androgenetic alopecia in Asian patients, using the BASP classification to address limitations of previous guidelines, and reports no clinical results.

This review presents an evidence-based algorithm for managing hidradenitis suppurativa in primary care, highlighting the need for more research on treatment effectiveness and the disease's pathogenesis.

This article presents a computer imaging algorithm that may automate and enhance the Severity of Alopecia Tool scoring for alopecia areata through texture analysis of pediatric images.

This study demonstrated that an automated algorithm can effectively track epithelial cell movement within clusters, revealing that partial knockdown of E-cadherin reduces electrotactic potential in breast epithelial cells, particularly in free-moving clusters, and suggesting an adhesion-independent role of E-cadherin in regulating cell electrotaxis.

This study found that the green alga Botryococcus terribilis and its novel compounds significantly promoted hair growth marker gene expression in hair follicle dermal papilla cells without cytotoxic effects.

This consensus statement outlines a treatment algorithm for alopecia areata, detailing systemic treatment indications and options, including EMA-approved medications baricitinib and ritlecitinib for severe cases, as well as other off-label treatments and adjuvant therapies like oral minoxidil.

This study developed and tested a new algorithm for analyzing coat and skin coloration in laboratory animals, using digital images and hierarchical color clustering, which effectively quantified color proportions and tracked changes over time without specialized software.

This review outlines a basic diagnostic algorithm to help pediatricians identify causes of hair loss in children, emphasizing early diagnosis and treatment due to the significant psychosocial impact of conditions like alopecia areata and tinea capitis.

This study introduces diagnostic and therapeutic algorithms tailored for tailored management of alopecia areata based on patient-specific factors like age, disease severity, and quality of life.

This study found that faster algorithms for inferring ancestry in genomic data can better capture historical and functional insights into genome variation than traditional methods in large datasets like the UK Biobank.

In this study, researchers found that differences in skin condition distribution are the main source of errors when AI algorithms classify dermatological conditions from new, previously unseen sources, and proposed steps to improve their generalizability based on available information.

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.

In this study, the researchers developed a quantum algorithm, QuantAnts machines, which identified complexes of CD9, CD34, and CD74 as potential targets for certain cancers involving the RAS pathway.

This article highlights the importance of an algorithmic approach to manage multiple opportunistic infections in HIV-infected patients due to the risk of drug interactions and complications, but reports no new clinical results.

This research reported that using J48 algorithms with bagging improves prediction accuracy of hair health through machine learning by analyzing factors like spatial-temporal images, gender, and age, achieving a real-time performance of 89.5%.

This study presents a machine learning algorithm that achieved 89.5% accuracy in predicting hair health using factors like spatial-temporal images, age, and gender.

This review provides graded evidence and a therapeutic algorithm for managing hidradenitis suppurativa, but does not present new clinical results.

This research reported that a newly developed dually crosslinked gelatin-alginate-based hydrogel scaffold enhances mechanical strength, promotes corneal tissue regeneration, and reduces scarring in rabbit models, suggesting potential applications in corneal tissue engineering.

This study reported that a new deep learning algorithm using Mask R-CNN improved hair follicle classification accuracy by 4 to 15%, suggesting potential clinical application for enhanced hair loss diagnosis.

This study found that using at least one ICD-10 code for alopecia in Medicaid claims data accurately identified alopecia in women of childbearing age, with positive predictive values between 95.3% and 100% across various algorithms tested.

This article discusses beard reconstruction techniques focusing on surgical algorithms and the use of tissue expansion, reporting no new clinical results.

This review discusses the reclassification of pregnancy-related skin diseases and provides a management algorithm but reports no new clinical findings.

This review discusses characteristics of androgenetic alopecia in Asian patients and includes algorithmic management guidelines, but reports no new clinical findings.

This article reviews trichoscopic techniques for diagnosing common hair loss conditions and presents a revised algorithm for identifying specific features of cicatricial and noncicatricial alopecia, but reports no new clinical results.

In this study, DPHC from the brown alga Ishige okamurae demonstrated potential to prevent androgenic alopecia by inhibiting hair loss and promoting hair growth through multiple cellular mechanisms.