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This study examined the latest advances in synthetic biology for increasing microbial production of sesquiterpenol compounds, highlighting innovative strategies to overcome challenges such as low yields and terpenoid-related toxicity, which hinder industrial-scale applications.

This review discusses recent advances in protein immobilization on bacterial cellulose for various biomedical uses and reports no new clinical results.

This paper reports insights from an Expert Panel convened by the Society for Immunotherapy of Cancer on managing immune-related adverse events (irAEs) following immune checkpoint inhibitor therapy, highlighting surveillance gaps and the need for a comprehensive irAE registry to improve post-treatment care for cancer survivors.

This study reviewed literature on the use of robotic systems in plastic and reconstructive surgery, highlighting their application in various procedures despite the predominance of descriptive case reports and a lack of comparative clinical outcomes and long-term data.

This narrative review highlights that declining testosterone levels are a complex public health issue potentially influenced by modifiable lifestyle factors and environmental stressors, suggesting integrated strategies may be needed to address the trend's impacts on health.

In this study, researchers developed a polyphenol–amino acid nanozyme capable of controlled hydrogen peroxide delivery, which effectively activated hair follicles via oxidative stress in a mouse model, suggesting a potential non-pharmacological treatment for alopecia.

This review article identifies a need for standardized guidelines in using photobiomodulation therapy for spinal cord injury repair, emphasizing that current inconsistencies in therapy parameters may hinder optimal outcomes and limit clinical trials.

This study demonstrated that Periplaneta americana extract significantly promoted hair regeneration in depilated mice by enhancing superoxide dismutase activity, upregulating vascular endothelial growth factor, modulating the FOXO/PI3K/AKT pathway, and restoring skin microbiome balance, suggesting its potential as a novel therapeutic candidate for alopecia.

In this review, it is discussed how mesenchymal stem cell aggregation plays a crucial role in organ development and has potential applications in organ regeneration through tissue engineering.

This review summarizes the current applications and mechanisms of self-assembled peptide hydrogels in promoting skin, bone, and nerve regeneration, highlighting their potential due to their biocompatibility and supportive role in tissue healing.

This study analyzed clinical and histological reports and concluded that blue light therapy is safe for human skin, with potential photoprotective effects against UV irradiation due to its impact on skin pigmentation mediated by Opsin-3.

This study found that a Faster Residual Convolutional Neural Network model achieved an accuracy of 84.3% in recognizing alopecia areata and various scalp conditions from image databases.

This study found that AI using YOLOv11 architecture can reliably differentiate between hyaluronic acid and silicone oil cosmetic fillers on ultrasound, achieving high accuracy, whereas identifying calcium hydroxyapatite and polymethylmethacrylate remains less consistent, requiring further improvements.

In this study, VGG19 slightly outperformed MobileNetV2 in hair disease classification accuracy, achieving 98% compared to MobileNetV2's 97%. However, MobileNetV2 was faster and more computationally efficient, making it suitable for resource-limited settings.

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

This study developed a deep learning model for the automated early detection of androgenetic alopecia using trichoscopic images, and found it demonstrated high accuracy and generalizability in a Korean clinical cohort, achieving a 90% accuracy in external validation.

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 study found that GoogLeNet outperformed other CNN models in accurately identifying the type of folliculitis.

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.

In this study, the researchers used machine learning techniques on an image dataset to diagnose Alopecia Areata, achieving a maximum accuracy of 98.3%.

This study evaluated different convolutional neural network architectures for diagnosing scalp and hair diseases, and found that VGG16 and VGG19 consistently outperformed other models in accuracy, demonstrating their effectiveness and reliability in this medical application.

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.

This study suggests that histological features of primary melanoma can partially predict lymph node metastasis using AI, achieving a best prediction AUROC of 0.65.

A deep learning model was developed to help diagnose trichothiodystrophy by analyzing hair patterns.

This study developed a high-performance deep learning model using the Inception-ResNet v2 architecture to classify 10 hair disease classes, achieving an accuracy of 94.7% and balanced precision, recall, and F1-scores of 0.94, suggesting reliability for automated dermatology diagnostics.

In this study, researchers developed a hybrid deep learning model called ScalpViT that accurately diagnosed scalp diseases with 94.3% accuracy, surpassing existing methods like ResNet-50 and EfficientNet-B3, and providing visual explainability for clinicians using GradCAM and Attention Rollout techniques.

This study introduces ScalpViT, a new deep learning model that accurately diagnoses visually similar scalp diseases with 94.3% accuracy, outperforming other methods like ResNet-50 and EfficientNet-B3, and providing dual visual explainability through GradCAM and Attention Rollout, potentially benefiting diagnosis in resource-limited settings in India.

In this study, researchers developed ScalpViT, a novel deep learning model, to improve the automated diagnosis of visually similar scalp diseases, achieving 94.3% accuracy and outperforming existing models like ResNet-50 and EfficientNet-B3 when tested on a diverse dataset of 7,000 images.

This study found that a lightweight Convolutional Neural Network model can accurately and quickly determine natural hair tone from high-resolution images of hair roots, outperforming other popular methods.

This study used a deep learning approach that successfully predicts alopecia, psoriasis, and folliculitis with a 2D convolutional neural network, achieving a training accuracy of 96.2% and validation accuracy of 91.1%.