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This review explores the potential of neuro-driven hydrogels for skin and nerve regeneration, integrating biochemical cues and advanced technologies like AI for personalized wound healing, but notes the need for further validation and trials before clinical use.

In this study, researchers differentiated human induced pluripotent stem cells into dermal papilla-like cells and observed gene expression dynamics during five stages of dermal differentiation, demonstrating the potential of these cells to interact with epidermal cells in functional assays.

In this scoping review, researchers observed that while AI-assisted trichoscopy holds promise for standardized assessments of hair and scalp disorders, its clinical translation is limited by small proprietary datasets, inconsistent validation protocols, and a scarcity of real-world clinical studies.

This review explores the interactions between the skin, peripheral nervous system, and immune system and reports no new results; the authors highlight the importance of understanding these connections for various skin conditions.

The study characterized the transcriptional changes in porcine SKP cells transitioning to fibroblast-like cells, indicating potential roles for specific signaling pathways in this cell fate transition.

This review highlights recent advancements in the engineering of skin organoids using hydrogels, which improve their structural and functional fidelity by supporting the architecture and integration of vascular and neural components, promising to enhance regenerative medicine, drug discovery, and complex skin disorder studies.

This review summarizes the latest progress and challenges in skin organoid research, emphasizing advancements in organoid construction, applications in disease modeling, and factors affecting tissue maturation, while noting barriers such as vascularization and neural integration that hinder clinical translation.

This review discusses recent breakthroughs in 3D bioprinting for hair regeneration, highlighting developments like biomimetic dermal papilla spheroids and follicle organoids, but notes that clinical application is hindered by challenges in integrating vascular and nerve systems and managing hair-cycle dynamics.

This paper presents a new method using Convolutional Neural Networks for detecting hair and scalp diseases, aiming to enhance diagnostics accessibility through a web-based platform integration.

This scoping review describes the current use of artificial intelligence in alopecia research, highlighting AI's evolution from diagnostic to prognostic applications in dermatology and identifying gaps in multimodal integration and fairness across demographics.

This review discusses neural mechanisms involved in hair growth control, focusing on piloneural interactions, but reports no clinical findings; the authors suggest further exploration for managing hair growth disorders.

This study developed a convolutional neural network model for non-invasive diagnosis of androgenetic alopecia using dermoscopic images, demonstrating potential accuracy and scalability while highlighting the importance of model interpretability for clinical use.

In this study, a deep neural network model called regX was developed to prioritize driver regulators for cell state transitions by incorporating gene-level regulation and interactions, showing potential therapeutic targets in type 2 diabetes and hair follicle development when applied to single-cell multi-omics data.

This study suggests that prenatal infusion of epidermal neural crest stem cells may improve certain neural markers and reduce cortical injury in a mouse model of methylazoxymethanol-induced malformations.

This study found extensive co-evolution of polyglutamine repeat lengths in neural protein clusters, highlighting their potential role in neurocognitive variation and neuropsychiatric disease development.

In this study, the authors review how epigenetic regulation influences the role of iPSC-derived neural stem/progenitor cells in spinal cord injury therapy, highlighting challenges in the cells' generation, differentiation, and transplantation, and the effect of therapeutic tools on these processes.

In this study, researchers proposed a deep learning approach combining genetic, hormonal, scalp health, and lifestyle data to predict hair loss, employing CNNs for image analysis and RNNs for modeling data over time, although specific results are not reported.

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 discusses the potential of hair follicle stem cells as a novel source for cell therapy in neurodegenerative disorders, but it reports no new clinical results.

This review reports that combining adipose-derived stem cells with decellularized extracellular matrix enhances tissue repair by improving scaffold biological activity and promoting angiogenesis, integration, and functional regeneration across various tissues, while addressing challenges in traditional transplantation methods.

This review discusses recent evidence on the Notch signaling pathway's role in regulating hair follicle development and stem cell differentiation, but provides no new research findings.

This study proposed and tested an Ensemble Pre-Learned Deep Learning and Optimized Long Short-Term Memory (EPL-OLSTM) model for classifying Alopecia Areata, achieving a 93.1% accuracy in differentiating healthy from varying severity levels of AA scalp hair using specific datasets.

This review discusses the role of jumonji family proteins in chromatin regulation and development, highlighting their involvement in transcriptional repression and histone demethylation, but reports no new experimental findings.

This article explores the use of monoclonal antibodies to study the presence and distribution patterns of melanocytes in developing human skin, but reports no new clinical results.

This study reports that an Explainable AI-based hair health prediction system using a novel hybrid model outperformed traditional machine learning methods, achieving high accuracy in predicting key factors and providing personalized recommendations.

This paper introduces an algorithm for using smart device-mounted microscopes to analyze scalp images and diagnose hair loss by extracting specific hair loss features.

In this review, researchers examined advanced sensing technologies and bioelectronic interfaces that could improve understanding of the skin–brain axis, highlighting challenges and strategies for integrating these platforms into models for studying skin and neural interactions.

This study found that somatosensory axons innervating Merkel cells in adult mouse skin exhibit significant plasticity, influenced by epithelial and neural factors, indicating a complex interaction in maintaining homeostasis.

This presentation will review neural networks and artificial intelligence basics, then explore their recent developments and applications in medical care, particularly in decision-making processes to enhance diagnostic accuracy and treatment strategies, ultimately improving patient outcomes in critical situations, as reported in this source.

This study found that secretory proteins in DFCM play crucial roles in regulating nerve restoration by forming significant protein interaction networks during the wound repair process.