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In this study, human scalp hair grafted onto nude mice accurately reflected methyl mercury exposure, demonstrating its potential as a research model for monitoring mercury incorporation into human hair.

This study observed that developing mammary organs in Monodelphis domestica show mammary hairs in 12-week-old females, which disappear by 18 weeks, with findings suggesting shared characteristics between their nipples and eutherian nipples, supporting the evolutionary link between mammary glands and hair organs.

This study describes a new method using ImageJ to quantitatively assess hair loss in mice with alopecia areata, offering a more precise and reproducible alternative to traditional visual scoring systems by employing image-based analysis techniques.

Using synchrotron X-ray imaging, this study mapped the complex innervation of male rat vibrissa follicles, revealing distinct types of myelinated afferent neurons and patterns of axonal organization that may inform sensory processing in the brainstem.

This review highlights that genetically engineered mouse models are valuable for studying melanoma development and progression by mimicking human skin biology, revealing key signaling pathways and molecular mechanisms that contribute to melanoma heterogeneity and offering insights into potential therapeutic targets.

This study observed that darker hair is typical in wetter regions for the Indriidae family, while within Propithecus, dark black hair is common in colder forests, suggesting evolutionary adaptations to environmental pressures.

This study investigated the Mandarin duck as a model for understanding lifelong developmental changes, finding that male sail feather morphogenesis involves a combination of local morphogenetic programs, epigenetic regulation, and hormonal cues, with increased female estrogen levels observed before the mating season.

This study demonstrated that human sebaceous glands can be successfully transplanted onto nude mice, where androgen stimulation increases gland volume, cell size, and labeling index.

This study used in vivo confocal microscopy and a ResNet34 deep learning model to classify meibomian gland images with an AUROC greater than 0.95, indicating its potential for automatic diagnosis and screening of meibomian gland dysfunction.

This study observed that injecting mice with α-MSH resulted in black hair regrowth in shaved and plucked areas, suggesting a potential link to the agent darkening hair in animals with a specific pituitary tumor.

This study found that a non-scarring alopecia in C3H/HeJ mice resembles human alopecia areata and may serve as a valuable model for studying specific subtypes of the human condition.

This study found that gray mongooses have more numerous and well-developed sebaceous glands compared to domestic cats, suggesting they may benefit from enhanced lipid-derived skin protection.

This study found that cupric chloride treatment may partially correct delayed maturation and abnormal arborization of Purkinje cells in the cerebellum of hemizygous brindled mice, a model for Kinky hair disease.

A new image-based method improves accuracy in measuring hair loss in mice.

This review discusses the commonly observed pathological findings in progeroid mouse models and highlights the importance of understanding mouse strain backgrounds and progeria-related phenotypes for accurate pathology data interpretation.

This study presents a follicular automaton model that simulates human hair cycle dynamics and can reproduce hair patterns similar to diffuse or androgenetic alopecia by incorporating spatially heterogeneous parameters and follicle miniaturization.

This study demonstrates that a proposed multi-stage framework improves the accuracy and faithfulness of drug-related responses generated by language models, compared to traditional methods.

This review discusses recent research on melanocyte stem cells and explores the differences between mouse and human skin, but it reports no new clinical results.

This review highlights a disconnect between mechanistic insights and physiological relevance in preclinical models of hair loss disorders and suggests a shift toward more human-relevant systems could enhance translational research, despite limitations in existing models like transgenic mice and xenografts.

This study generated a Dct-H2BGFP mouse model that allows for effective identification and isolation of melanocytic cells in vivo, facilitating research into their molecular and biological properties.

In this study, researchers developed a non-invasive, partially automated protocol for extracting DNA from marmoset hair follicles, resulting in high-quality whole genome sequencing with low chimerism levels, making it a reliable method compared to blood DNA sequencing in these primates.

In this study, mixing mouse epithelial and dermal cells was the most successful method for reconstituting hair follicles in mice, with the hair patch assay providing the quickest results.

This study found that grafting split-thickness human skin onto nude mice produces scars that mimic hypertrophic scar characteristics in humans, offering a useful model for studying scar formation and treatments.

This study discovered a conserved "fiber-like-motif" of melanosomes aligned in hair across various mammalian species, shedding light on the internal structure that influences hair's thermal and mechanical properties.

This study developed a deep-learning model using unannotated dermatology images from online forums, achieving 49.64% accuracy in classifying 22 skin diseases and 61.76% accuracy in detecting monkeypox, highlighting the potential of these images for skin disease diagnostics in China.

This study observed that amelanotic melanocytes from human hair follicles appeared morphologically immature, suggesting they might not transfer melanosomes into keratinocytes.

This study describes a new mouse model for vitiligo that mimics key clinical features such as skin depigmentation and CD8 + T cell infiltration, providing a useful tool for in-depth research and drug discovery.

This review discussed recent research on using machine learning to predict mental disorders, reporting that Adaboost could predict depression with 92.5% accuracy and 93.6% specificity, while other models like XGBoost and RNN were applied for post-stroke depression and EEG-based depression detection, respectively.

In this study, researchers developed an in vitro airway-organoid model using human lung-derived cell lines on RADA16-I hydrogels to serve as a potential tool for studying adenovirus isolation, pathogenesis, and antiviral drug screening.