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In this study, facial feminization surgeries were found to be effective and generally safe, significantly enhancing feminine appearance in patients, with minimal major complications reported.

In this study, researchers introduced the Sanusi FUE Score Scale to better predict and grade the difficulty of follicular unit excision hair transplantation by accounting for diverse hair and skin types, with further validation needed.

This study found that the FOS gene may play a significant role in promoting hair follicle development in Tan sheep, with elevated expression during the Er-mao period.

This study describes a system called FOLLYSIS©, which uses mathematics and image analysis to optimize Follicular Unit Extraction hair transplants, showing high accuracy in measuring donor area density and reducing donor site injury.

In this study, researchers found that the FOS gene may play a significant role in promoting hair follicle development in Tan sheep between birth and the Er-mao period.

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.

In this study, Dodatek A operationalizes the Functional Androgen Axis framework by defining three system-level indices and an efficiency metric to describe androgen function, incorporating key methodological improvements and acknowledging significant limitations for future empirical validation.

This study used machine learning models, such as Convolutional Neural Networks (CNN), to accurately differentiate False Daisy from similar plants like Smooth Joyweed.

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 developed a new method for analyzing multivariate time-series data that successfully predicts website competition dynamics and outperforms conventional methods in identifying and predicting competitive structures.

This study found that MEGA PROTAC improved the prediction of ternary structures with higher maximum DockQ scores compared to the BOTCP method in 16 out of 22 test cases.

In this study, EQ-5D-5L and SF-6DV2 showed suitable measurement properties in assessing health utility among Chinese university staff and students, though they reported differences in sensitivity and cannot be used interchangeably.

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 found a high concordance between subject-reported and clinician-reported Fitzpatrick Skin Phototypes, but 14% of subjects misjudged their skin type, often overestimating it.

This study utilized the Random Forest Algorithm to create a machine learning model aimed at accurately predicting hair loss by considering complex datasets involving genetic, hormonal, lifestyle, and environmental factors, but specific outcomes were not reported.

This study developed a model for detecting sparse hair clusters using enhanced object detection neural networks and medical images, which accurately identifies and counts sparse hair clusters with greater accuracy and efficiency than existing methods.

This study used machine learning models to analyze PCOS symptoms for early diagnosis, finding Support Vector Machine and VGG16 algorithms achieved high accuracy rates of 94.44% and 98.29% respectively.

This article describes a new hair transplant technique combining FUT and FUE to increase graft numbers in patients with high levels of baldness and reduced scalp laxity, but it does not report clinical outcomes.

In this study, the authors found that syntax-based neural networks performed comparably to pre-trained Transformers on tasks involving definitely unseen sentences, suggesting they are a more transparent and parameter-efficient alternative for certain Natural Language Processing applications.

This study found that overexpression of the FA2H gene in cashmere goats' hair follicle cells may enhance hair proliferation and regulate genes affecting cashmere fineness.

In this study, a novel GAN-based image translation method focusing on regions of interest showed improved predictive performance for post-hair transplant images compared to existing methods, using an ensemble approach to enhance robustness and detection accuracy.

This study identified a new FAK isoform, FAKΔe4, which is regulated by ECM stiffness and affects cell migration, invasion, and mechanosensing in human-derived data and engineered models.

This study found that oral administration of Flammulina velutipes polysaccharide reduced tumor volume and improved survival in mice by modulating macrophage polarization and enhancing antitumor immune responses.

This study examined different logistic damping effects in a chemotaxis system related to Alopecia Areata and found that certain conditions guarantee a unique, globally bounded classical solution, while others ensure a weak solution, offering new insights into preventing blow-ups in this context.

This study explored using large language models to automatically identify monkeypox cases from electronic medical records, finding that models based on DeepSeek features performed better than traditional methods, with logistic regression showing high accuracy in detecting key symptoms like fever and rash.

This study introduces a novel image enhancement method that significantly improves the visual quality of low-light sports images by utilizing improved bilateral filtering and the CLAHE algorithm, achieving a 65.24% improvement in color and edge detail preservation compared to state-of-the-art methods on the LOL dataset.

This study found that specific SNPs in the sheep FAT1 gene are significantly associated with wool quality traits, suggesting potential markers for improving wool crimp, fibre length, and fibre diameter in breeding.

This study suggests that pre-trained Transformers only outperform syntactic and lexical neural networks on unseen DarkNet sentences after extreme domain adaptation, indicating unexpected advantages from their massive pre-training corpora.

This study found that an Attention-based Balanced Multi-Task Deep learning system achieved a 95.11% accuracy in classifying Alopecia Areata conditions using hair and scalp images, outperforming classical methods.

This study found that the proposed Bayesian measurement-error-driven hidden Markov regression model effectively calibrated inflated covariate effect sizes in a community-based survey on androgenetic alopecia regardless of misclassification type.