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This study found that formulations containing pequi and buriti oils improved texture properties and significantly increased gloss in curly hair, with pequi oil also significantly enhancing tensile strength.

This study demonstrated that a computational screening process can identify existing drugs and natural compounds with potential anti-COVID-19 activity, highlighting some candidates for further experimental validation.

This reference list, part of a book by Alex Gough, Alison Thomas, and Dan O'Neill, compiles citations from veterinary journals on various canine and feline health issues, but reports no new research findings.

This study developed a deep learning-based tool to quantify individual hair fibers in mice, revealing distinct hair phenotypes linked to hormonal, genetic, and age-related factors, and suggesting its potential for new diagnostic methods through hair analysis.

In this study, researchers evaluated various machine learning models for predicting type 2 diabetes risk, finding that Random Forest and K-NN models performed best in terms of precision, recall, accuracy, and other metrics using common symptoms as features.

This study analyzed deep learning's application to automate hair density measurement in images and found that YOLOv4 had the best performance among tested algorithms, with a mean average precision of 58.67.

This study found that a combined machine learning model outperformed individual networks in diagnosing scalp conditions using visual data, enhancing prediction accuracy and early detection, particularly in settings with limited resources.

This paper reviews AI integration in robotic surgical systems, highlighting enhanced precision and reduced fatigue but noting challenges like high costs and ethical concerns, suggesting responsible adoption to realize potential benefits for surgical care.

This study introduces AlopeciaDet, a novel feature fusion technique, using camera images to detect Alopecia Areata with 99.45% accuracy, outperforming existing methods by leveraging CRSHOG and ResNet-50 features.

This study found that applying transfer learning with CNN architectures like AlexNet, VGG16, and ResNet50 achieved 99% accuracy in classifying multiclass hair disorders, suggesting a potential technological aid for dermatologists in diagnosing and treating hair conditions.

This study found that low image resolutions allow expert clinicians to detect alopecia, but higher resolutions are necessary for identifying scarring and vellus hair, which may inform future image processing algorithms in dermatology.

This study identified multiple genetic variants in Pakistani families with oculocutaneous albinism, including two novel variants, enhancing understanding of its genetic basis and aiding better management and counseling.

This study evaluates a framework using neural networks and machine learning techniques to classify and detect Alopecia Areata from hair images, aiming for accurate differentiation between healthy hair and the condition.

The document concludes that significant investment in agricultural innovation is necessary to achieve global food security and nutrition.

This review discusses various topical medication instillation techniques for managing glaucoma but reports a lack of evidence on their effectiveness compared to usual care.

This study reports that a robotic and AI-based system successfully analyzes FUE hair transplant procedures, aiding surgeons in planning and assessing operation success through detailed pre-op and post-op evaluations.

This study explored the role of AI technologies in plastic and reconstructive surgery, highlighting their potential across preoperative, intraoperative, and postoperative stages, while also identifying challenges like data privacy and regulatory issues that must be addressed for successful implementation.

This study examined attractive white celebrities and found no significant difference in central forehead height between genders, but reported narrower and downward slanting foreheads in women, which may influence facial rejuvenation and gender-affirming surgery approaches.

This review found that AI in dermatology shows high diagnostic accuracy comparable to experienced clinicians, but integration into clinical practice faces challenges requiring further research.

In this study, researchers developed a deep learning model that efficiently classifies five degrees of harm with high accuracy, achieving up to 98% precision, recall, and F1-score across various harm levels, indicating strong potential for practical application in automated harm evaluation.

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.

In this study, a machine-learning model was evaluated for its ability to categorize various hair conditions, achieving high accuracy and balance between precision and recall, with an overall accuracy of 97% in detecting hair problems.

This review discusses the use of helmet designs in integrative medicine and transcranial photobiomodulation, highlighting new developments and potential future advancements in sensor-controlled therapy helmets, but reports no new clinical results.

This review observed limited evidence of clinically significant benefits from treatments for acute central serous chorioretinopathy, which often resolves spontaneously, but identified PDT and micropulse laser as the most promising for further trials.

This review found significant progress in 3D bioprinting for surgery, noting advances in creating complex tissue constructs, while highlighting ongoing challenges like vascularization and integration with host tissue, emphasizing the need for further research and regulatory development.

This review explores the complexity of human–robot interactions in robotic surgery, finding that complexity increases with conditional autonomy and is predicted to decrease with full autonomy, without reporting new clinical results.

In this study, researchers characterized the retinal structure and function of the Oat^rhg mouse model of gyrate atrophy, finding localized atrophy without significant retina-wide functional impact, suggesting the model may be useful for testing new treatments using multimodal retinal imaging.

This study reports the development of a prototype robotic hair transplant system capable of efficiently harvesting and implanting hair grafts, showing promising accuracy and performance in tests using a spherical scalp phantom.

This study reports two cases of myasthenia gravis patients with thymoma who exhibited various autoimmune symptoms including alopecia, suggesting a link between thymus-related immune dysregulation and comorbid conditions.

This case report suggests that long-acting injectable risperidone may induce hirsutism in some patients, and clinicians should monitor for this potential side effect when prescribing this antipsychotic.