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This review highlights recent research using single-cell RNA sequencing and machine learning in wound healing, revealing significant insights into fibroblast diversity, immune cell dynamics, and the spatial organization of cells, which may transform therapeutic strategies for chronic wounds, fibrosis, and tissue regeneration.

This study presents a machine learning algorithm that achieved 89.5% accuracy in predicting hair health using factors like spatial-temporal images, age, and gender.

This study found that progesterone enhances the survival of newborn neurons in the hippocampus of adult male mice, which is associated with improved spatial learning and memory.

This study found that progesterone increased the survival of newborn neurons and enhanced spatial learning and memory in adult male mice, independent of neuron production changes.

This research reported that using J48 algorithms with bagging improves prediction accuracy of hair health through machine learning by analyzing factors like spatial-temporal images, gender, and age, achieving a real-time performance of 89.5%.

RY-023, a specific drug, can improve early stage memory learning without affecting general activity in rats, but it's less effective for later learning stages and doesn't impact memory recall.

This study observed that short-term administration of finasteride in male Wistar rats induced anxiety-like and depression-like behaviors, impaired spatial learning and memory, and reduced synaptic plasticity in the hippocampus, with a trend of increased corticosterone levels noted.

This study introduced Neurospectrum, a framework that effectively identifies meaningful neural dynamics by encoding neural activity into latent trajectories, and reported that it outperformed traditional methods in tracking synchronization, reconstructing stimuli, and identifying fMRI biomarkers in various datasets.

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 review explores the convergence of functional biomaterials, biosensing, and AI technologies in bioengineering, highlighting applications in cancer modeling and regenerative medicine, while addressing challenges like biofouling and dataset integration.

This research by Yuan et al. focused on developing a comprehensive human skin cell atlas, analyzing various cell types and diseases, and introduced a deep learning method, scSEA, for unbiased reference mapping, potentially discovering new cell types.

The researchers developed a comprehensive human skin cell atlas using data from various studies and established a consensus nomenclature for normal human skin in this project, which also includes a deep learning-based method for more effective reference mapping of new cells.

In this study, deep learning models accurately predicted gene expression in whole slide images of colorectal cancer, with convolutional neural networks outperforming transformer and graph-based approaches in spatial RNA pattern prediction.

This study suggests that apoE4 may contribute to cognitive decline by reducing androgen receptor levels in the brain, but androgen treatment improved memory deficits in female mice expressing human apoE4.

In this study, the proposed MSF-VMDNet, combining dual encoder networks with a multi-frequency domain mechanism, significantly outperformed existing methods in segmenting skin cancer tissues from histological slide images, achieving high accuracy with an MIoU of 95.37% and a Dice coefficient of 95.11%.

In this pilot study, no significant differences were observed between memantine and placebo groups in young adults with Down syndrome on the primary memory outcomes, but some improvement was noted in a secondary measure.

This article reviews the steps and methods involved in preparing skin tissues for three-dimensional volumetric imaging, highlighting its potential to provide detailed insights into skin structure not possible with traditional 2D histology, but reports no new findings.

This review discusses the use of single-cell RNA sequencing and spatial transcriptomics to enhance understanding of the pathogenesis and targeted therapies for chronic inflammatory skin conditions, highlighting technology advancements and the need for cost reduction and standardization.

In this study, researchers developed a predictive model for the onset of alopecia areata by analyzing six datasets to identify key feature genes and employing various machine learning algorithms, ultimately finding the XGBoost model most effective for clinical application.

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.

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.

In this study, the BaldGraphFormer framework, integrating visual and clinical data, outperformed unimodal baselines in early-stage androgenetic alopecia detection, achieving an F1-score of 97.62% and macro-average AUC of 0.992, suggesting its potential to support dermatological decision-making and early intervention.

This review explores the role of Foxn1 in mammalian skin biology, discussing its influence on hair follicle function and the potential for further research to enhance understanding of epithelial differentiation.

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 suggests that estimating autism likelihood as early as one month after birth may enable more precise early intervention for children with developmental support needs, potentially improving diagnosis, workflows, and reducing service wait times.

This review examines the current state of automated vitiligo detection systems, noting a lack of large, diverse datasets and consistent imaging conditions, while comparing traditional and modern machine learning approaches to improve reliability and applicability.

This study examined the molecular communication in psoriasis cells, highlighting unique immune cell interactions and identifying new features of the hair follicle cell-psoriasis axis. It suggests the potential for targeted therapies at the single-cell level to improve psoriasis treatment.

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 perspective highlights how advancements in single-cell sequencing and digital pathology can help understand the complex mechanisms of immune responses, fibrosis, and tissue remodeling related to medical implants, aiming to address the challenges of implant-related tissue reactions reported in this study.