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This article presents a comprehensive guide for classifying human hair follicle cycle stages in vivo using scalp xenografts on immunocompromised mice, offering valuable resources for researchers in the field.

Mesenchymal cells can significantly boost human hair growth and longevity.

This study developed a deep-learning model that accurately diagnosed alopecia areata with an accuracy of 88.92% and distinguished its activity levels with an accuracy of 83.33%, highlighting the potential for artificial intelligence in improving the diagnosis and treatment of this autoimmune hair loss condition.

This study evaluated an AI-driven system for personalizing non-medicated hair loss treatments in women, showing significant improvements in hair growth, shedding, texture, and scalp health over 24 weeks, with no adverse effects reported.

This study found that Urticadioica L ethanol extracts enhanced hair growth and reduced catagen transition in human hair follicles in an ex vivo organ culture model.

This source reviews the latest developments in hair transplantation for androgenetic alopecia, suggesting that while medical therapy alone remains unsatisfactory, surgical hair restoration is shown to be highly gratifying for male pattern hair loss.

This study demonstrated how cellular flows and tissue mechanics guide the topological transformations in avian skin, essential for feather follicle development from scales.

This study introduces ALUDWIG, an automated tool for assessing female androgenic alopecia severity from smartphone images, which may offer a more consistent alternative to current scoring methods like the Ludwig scale.

This study used numerical models to explore the cooling power required by different scalp cooling devices to prevent chemotherapy-induced hair loss, finding that coolant temperature significantly influences scalp tissue temperatures.

This study found that a genetic test using 5 to 20 SNPs can predict male pattern baldness with variable accuracy in European men, especially those aged 50 and older.

This study demonstrated that injecting reconstructed dermal papilla-like tissues in mice led to new hair growth, suggesting potential future applications in hair transplantation and baldness treatment.

This study observed that most women with PCOS in the United States use adaptive coping strategies, while those with higher psychological severity may resort to maladaptive coping methods.

This study observed that 82% of patients with alopecia areata reported experiencing at least one stressful life event prior to the onset of their condition, highlighting the significance of considering life event strain in managing these patients.

This study reports that deep learning models, particularly CNN and FCN, achieved high accuracy in diagnosing scalp and skin disorders, suggesting potential for improved diagnostic systems with further advancements.

This paper discusses the introduction and advantages of an Insurance-Based Billing Model for scalp cooling devices in the USA, which can enhance access to this treatment for reducing chemotherapy-induced alopecia, particularly benefiting underserved and disadvantaged populations.

This study found that a laboratory model using nude mice can produce human hair follicles with amino acid compositions resembling both normal and trichothiodystrophy-affected human scalp hair over extended periods.

This study outlined modern approaches for using cosmeceuticals in trichology, emphasizing evidence-based medicine, personalized care, and patient safety. It proposed the H.A.I.R. model as a universal algorithm for trichological programs and suggested new educational and practical tools for specialists in trichology and aesthetic medicine.

In this study, activated PRP injections appeared to promote hair growth in AGA-affected scalp skin transplanted onto mice, compared to non-activated PRP.

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.

In a mouse model, this study found that exposure to PM2.5 leads to significant postpartum hair loss, apparent via morphological changes and elevated markers of inflammation, apoptosis, and fibrosis, potentially exacerbated by changes in hormone receptors and stem cell populations.

This study aims to develop an automatic machine learning-based method using the VGG-19 model to accurately classify various hair and scalp diseases.

This perspective highlights the potential of reptile integument as an experimental model to understand the evolution of amniote skin structures, but reports no new research findings.

This study developed a deep learning model using the VGG architecture to predict hair disorders and provide tailored therapeutic suggestions, showing reliable recognition of conditions like dandruff, fungal infections, and alopecia by analyzing images of hair and scalp.

This article argues that human scalp hair follicles could serve as a surrogate model for studying immune privilege in the human eye, offering a novel approach for ocular IP research.

This study found that exosomes from human umbilical cord mesenchymal stem cells reduced hair follicle aging and promoted hair regeneration in models by upregulating COL17A1 through the miR-21-5p/DKK2/Wnt/β-catenin axis, outperforming existing treatments like minoxidil.

A deep learning model accurately detects early hair loss signs using scalp images.

In this study, researchers found that a ring block technique was the most accepted anesthesia modality for platelet-rich plasma injections in hair loss treatment, with the least post-procedural pain compared to other methods like vibration anesthesia and nerve blocks.

In this study, researchers found that WNT activation influenced FGF expression in human scalp-derived fibroblasts, with mouse model results showing FGF9 increased hair follicle number and diameter, while FGF7 reduced diameter.

In this study, researchers developed a novel image processing method using a multi-class support vector machine that achieved an 89.3% accuracy in classifying alopecia areata and related conditions, outperforming existing models in classification accuracy.

In this study, researchers proposed a deep learning approach combining genetic, hormonal, scalp health, and lifestyle data to predict hair loss, employing CNNs for image analysis and RNNs for modeling data over time, although specific results are not reported.