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This study proposes a new mathematical model, the Harmonic Mean equation, for precisely quantifying nuclear pleomorphism in breast cancer grading, showing high performance with accuracy, recall, specificity, precision, and F1-score metrics.

This study identified distinct plasma miRNA profiles in alopecia areata that may aid in diagnosis and therapy, but further validation is needed to confirm these exploratory findings.

This study highlights the growing role of artificial intelligence in vascular surgery, where AI improves diagnostic accuracy, surgical planning, and patient monitoring, ultimately enhancing clinical outcomes, shortening recovery times, and reducing healthcare costs.

This study introduced a novel framework called SL-HyDE that significantly improved zero-shot dense retrieval accuracy in medical information retrieval without relying on labeled data.

This review discusses the developments over the past five years in nanozyme-based theranostics for tumor therapy, including their classification, design, and synergistic strategies. It also outlines the challenges and prospects of using nanozymes to enhance selectivity, biosafety, repeatability, and stability in therapeutic applications.

This study highlights that while advances in dermatology, such as biologics and JAK inhibitors, have improved treatments for conditions like atopic dermatitis and psoriasis, challenges remain, including issues with lasting efficacy and the need for more personalized therapies.

This study introduced LabeledIn, a semi-automatic, human-reviewed catalog of drug-disease treatment relationships, highlighting the important role of human input in creating accurate and detailed drug indication resources.

This review examines the current applications and potential of artificial intelligence in healthcare, highlighting its role in improving prevention, diagnosis, and treatment across multiple major disease areas, but reports no new experimental findings.

This study found that hair proteomics may serve as a promising, non-invasive biomarker source for stress-associated psychopathology, showing high accuracy in distinguishing patients with non-suicidal self-injury disorder from healthy controls.

This study reviews AI's role in aesthetic medicine, noting it enhances diagnostic accuracy and personalized treatment planning, but faces challenges like ethical concerns, algorithmic biases, and regulatory issues that need addressing for successful integration.

This study explored the role of AI technologies in plastic and reconstructive surgery, highlighting their potential across preoperative, intraoperative, and postoperative stages, while also identifying challenges like data privacy and regulatory issues that must be addressed for successful implementation.

This study integrated single-cell RNA-seq data from four previous studies to create a comprehensive human corneal cell state meta-atlas, revealing novel marker genes, rare cell states, and distinct transcription factors, and offering a tool to enhance future cornea research.

This review discusses the growing role of colorimetric sensors in food safety, highlighting their potential to enhance detection accuracy and manageability, while also noting challenges such as environmental interference and the need for further industrial validation.

This review highlights that gut dysbiosis and bacterial extracellular vesicles are key factors in PCOS pathophysiology, and suggests AI-driven analysis of these profiles could enhance diagnostic accuracy and treatment personalization, though ethical concerns like data privacy and bias must be considered.

This article discusses drug repurposing as a strategy to find new uses for existing medications, highlighting its potential benefits like faster development and lower costs, while also addressing challenges such as drug specificity, ethical concerns, and regulatory issues.

This review aims to systematically evaluate the various types of antimicrobial wound dressings and current research on antimicrobial agents, providing insights for innovative treatments of infected wounds, due to the lack of comprehensive studies on their efficacy in promoting wound healing.

In this study, researchers found that coordinated intercellular Ca2+ signaling among basal stem cells in mice is crucial for cell cycle progression and tissue-wide communication during epidermal regeneration.

This review highlights the evolution of forensic genetics from basic DNA analysis to complex genome-wide studies, enabling insights into personal traits, ancestry, and habits, and suggests future advancements through technologies like CRISPR and AI.

This review highlights how utilizing unconventional and non-traditional cell sources for cultivated meat production could overcome manufacturing challenges, with distinct biological advantages potentially improving bioprocess efficiency and enabling the creation of novel meat products, according to the authors.

This systematic review provides an in-depth evaluation of non-invasive imaging and biophysical methods for diagnosing and monitoring vitiligo, emphasizing the need for studies with larger sample sizes to verify their use in clinical practice and research.

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 study found that using curated biomedical databases as training examples for information extraction tasks in Genome-Wide Association Studies can outperform cost-insensitive methods, demonstrating their potential use without expert annotation.

This review examines advancements in deep learning methods for detecting dermatological conditions from dermoscopic images, summarizing available datasets and suggesting future research directions, but reports no new results.

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 review presents a comprehensive evaluation of medical reasoning using large language models, highlighting a significant gap between exam-level performance and true clinical decision-making accuracy.

This article discusses ovarian steroid cell tumors, which can produce testosterone and manifest symptoms like hirsutism, emphasizing surgery as the primary treatment, but reports no new clinical findings.

In this study, researchers developed a new photoactivatable Cre recombinase mouse model, DiLiCre, which allows precise light-induced genetic modifications and cell tracing, demonstrating its effectiveness for advancing biological and biomedical research.

This study demonstrates that a proposed multi-stage framework improves the accuracy and faithfulness of drug-related responses generated by language models, compared to traditional methods.

This study found that all four evaluated large language models generated a significant proportion of inappropriate responses when prompted with or without LGBTQIA+ identity terms, with more severe bias observed in prompts mentioning LGBTQIA+ identities.

This study found that their proposed method for facial image restoration using Denoising Diffusion Probabilistic Models produced higher-quality results compared to traditional methods, particularly improving face recognition accuracy with different types of masks.