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This research review highlights the potential of exosome therapy to transform stroke treatment, reporting that in animal models, exosomes can reduce neuroinflammation, oxidative stress, and cell death, while promoting brain repair and regeneration. However, more evidence is needed before clinical applications in humans are established.

This review summarizes how single-cell omics technologies have advanced understanding of cellular regulatory programs in sheep and goats, but highlights limitations in genomic annotation and integration with population genetics for molecular breeding.

This review highlights that drug repositioning offers a cost-effective alternative to traditional drug discovery, especially for treating orphan and resistant diseases like COVID-19, by repurposing existing drugs such as Hydroxychloroquine and Remdesivir.

This review examines hydrogel microneedles for transdermal drug delivery, discussing their applications and challenges but reports no new clinical results.

In this narrative review, the authors discuss regenerative substances like platelet-rich fibrin and mesenchymal stem cells in biological aesthetics, noting that while these methods show promise for regeneration, their safety, effectiveness, and durability remain uncertain due to limited and heterogeneous evidence.

This study describes the PandaOmics platform, which uses AI and bioinformatics to identify new therapeutic targets and biomarkers for various diseases, demonstrating validation in laboratory and animal studies.

This study reviews the transformative role of artificial intelligence in biomaterial design, highlighting its ability to reduce costs through virtual screening, enhance material performance, and predict biological interactions to advance personalized and precision medicine.

This review highlights that microRNAs (miRNAs) play crucial roles in hair follicle development and cycling in cashmere goats, detailing recent advances in understanding their regulatory functions and potential applications in improving cashmere fiber quality and diagnosing hair disorders.

In this study, machine learning-based computer-aided diagnosis significantly improved accuracy in diagnosing alopecia areata compared to traditional visual methods, achieving up to 91.9% accuracy using different classifiers like CNN, SVM, and random forest models.

In this paper, Chemistry42—a software integrating AI with computational and medicinal chemistry—demonstrated efficiency in designing novel molecular structures targeting DDR1 and CDK20, with properties validated in both in vitro and in vivo studies.

This study developed BroadAMP-GPT, a computational-experimental framework, to discover new antimicrobial peptides, identifying candidates effective against multidrug-resistant pathogens. Notably, AMP_S13 showed strong stability, low toxicity, and efficacy in infection models, highlighting the platform's potential in combating antimicrobial resistance.

In this study, researchers found that p75 neurotrophin receptor signaling affects apoptosis during hair follicle regression in mice, and altering its activity might help manage disorders involving early catagen entry.

This study introduces a novel computational model using fuzzy logic and multi-criteria decision-making techniques to create a triage system for androgenetic alopecia management, stratifying patients into seven severity levels and aiding in resource allocation and treatment planning.

This review discusses recent advancements in bone tissue engineering, emphasizing a shift from passive to smart responsive antibacterial strategies to prevent implant-associated infections, and highlights AI's role in optimizing bone scaffold design for personalized, infection-free implants.

Patients found AI helpful for alopecia diagnosis but want it to support, not replace, doctors.

This research proposes a comprehensive "four-dimensional technology toolbox" for reprogramming wound-repair cells, aiming to treat chronic non-healing wounds like diabetic foot ulcers by altering fibroblasts, keratinocytes, and macrophages to regenerative phenotypes, and addressing clinical translation challenges.

This review found that 3D bioprinting significantly advances skin tissue engineering by enabling the creation of complex, patient-specific skin structures, though technological and regulatory challenges persist, particularly in areas like scalability and physiological mimicry.

This review highlights recent advancements in molecular understanding and treatment approaches for PCOS, including innovative drug delivery systems and AI-driven precision medicine, but provides no new clinical results.

This review assesses the potential of smart dressings, engineered to respond to various stimuli, as advanced treatments for chronic inflammatory skin diseases by examining their activation mechanisms and proposing future developments like integrating AI and closed-loop monitoring for adaptive therapy.

This review identified emerging therapies for androgenetic alopecia, such as low-dose oral minoxidil and stem cell-based treatments, which show promise for improved hair loss management but require standardized protocols and large-scale trials for further validation.

This study explored deep learning's potential in diagnosing scalp conditions like alopecia, psoriasis, and folliculitis, using a two-dimensional Convolutional Neural Network, achieving high accuracy and precision despite challenges of a small and uneven dataset.

This research explores the potential of the metaverse for transforming dermatology, particularly in treating hair and nail disorders through virtual consultations and support communities, but highlights significant challenges such as technological limitations, accessibility for underserved populations, and the need for empirical validation.

This review discusses how emerging technologies in dermatology are enhancing the ability to diagnose and treat hair and nail disorders more accurately and effectively, highlighting advancements in both diagnostic techniques and therapeutic methods such as AI-driven technologies that personalize patient care and improve outcomes.

This source provides an overview of diagnostic and treatment innovations for gastrointestinal disorders, such as wireless capsule technology for motility assessment and new methods for treating constipation and nausea, highlighting both current and emerging techniques.

This study developed an AI-driven method for classifying cells in Follicular Lymphoma cases, achieving a 63% F1-score, precision, and recall in distinguishing centroblasts from other cell types using whole slide images at x20 resolution.

This study explored the use of an AI-driven device, HairMetrix, to quantitatively assess the effects of PRP treatments on scalp health in two Caucasian women with androgenetic alopecia, reporting increases in average hair width and follicle count after one treatment.

In this study, the authors used AI-driven methods to identify and prioritize promising therapeutic targets that may address both aging and Glioblastoma Multiforme, proposing CNGA3, GLUD1, and SIRT1 as novel candidates.

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.

This study found that acne inversa is primarily an epithelial-driven disease, with cyst-forming tendrils causing inflammation due to rupture and the release of debris, highlighting limitations of punch biopsies for analysis.

In this study, artificial intelligence's integration into Indian aesthetic medicine has expanded from consumer apps to crucial roles in diagnostics, treatment planning, and clinic management, driven by demands for precise data over subjective opinions and enhancing competitive advantage.