Citations

Abstract Senescent cells accumulate with age and contribute to tissue dysfunction, yet their identification in tissues is challenging due to low abundance, heterogeneous phenotypes, and the lack of specific markers. Senescence-associated features span multiple subcellular compartments, including nuclear DNA damage foci, cytosolic protein changes, and perinuclear alterations, each requiring tailored detection strategies. To overcome these challenges, we developed SenoQuant ( https://github.com/HaamsRee/senoquant ), a versatile software designed for comprehensive, accurate, and unbiased spatial quantification and prediction of senescence markers across diverse tissue contexts. Utilizing AI models, SenoQuant enables precise nuclear and cytoplasmic segmentation and detection of senescence markers across low- and high-plex imaging modalities, applicable to cultured cells and tissue sections from mice and humans. The platform also supports custom AI models; for example, we built SenCeption, a proof-of-concept predictor of single-cell p21 status from DAPI-stained nuclei in human skin. Available as a free napari plugin, SenoQuant is widely accessible to researchers. By providing a unified approach to senescence analysis and prediction, SenoQuant opens new opportunities for exploring the complex biology of senescence and its impacts on aging and disease.

BACKGROUND Glucagon-like peptide-1 receptor agonists (GLP-1RAs) have expanded from metabolic therapeutics to agents with broad effects on inflammation, tissue repair, cutaneous and adipose biology, and aging-related pathways. Clinicians increasingly encounter cutaneous manifestations and aesthetic sequelae with important implications for dermatologic counseling and multidisciplinary care. OBJECTIVE To review current evidence regarding GLP-1RA effects on skin biology, inflammatory dermatoses, wound healing, cutaneous adverse events, and aesthetic aging. MATERIALS AND METHODS A narrative review of clinical, translational, and mechanistic literature was performed, emphasizing psoriasis, hidradenitis suppurativa, wound repair, skin hypersensitivity reactions, autoimmune blistering disease, and facial volume loss associated with pharmacologic weight reduction. RESULTS GLP-1RAs are hypothesized to improve inflammatory skin conditions and wound repair through anti-inflammatory, immunometabolic, and proreparative pathways. Yet, their use may also be associated with injection-site reactions, hypersensitivity eruptions, rare autoimmune blistering disorders, and clinically significant facial deflation and skin laxity. Current evidence is strongest for mechanistic plausibility and observational clinical signals, whereas dermatology-specific randomized trials remain limited. CONCLUSION GLP-1RAs exert bidirectional effects on cutaneous biology. Recognition of both therapeutic opportunities and potential aesthetic or adverse sequelae is important, with patient counseling ideally occurring within a multidisciplinary framework that integrates metabolic benefits with skin health and appearance.

BACKGROUND Glucagon-like peptide-1 receptor agonists (GLP-1RAs) exert systemic metabolic and anti-inflammatory effects that may be relevant to cellular senescence, a key driver of skin aging. However, human cutaneous data are lacking. OBJECTIVE To explore whether systemic GLP-1RA therapy translates to a detectable reduction in cutaneous senescence, quantified by epidermal p21 expression and supported by AI-driven facial imaging. MATERIALS AND METHODS In this controlled observational pilot study, 3 male adults (GLP-1RA n = 2; GLP-1RA–naïve control n = 1) underwent 3-mm punch biopsies from a standardized, sun-protected gluteal site to isolate intrinsic aging. Senescence was quantified through blinded whole-epidermis digital annotation of p21-positive nuclei. Clinical phenotypes were assessed using noninvasive cheek measurements and AI-based facial age estimation. RESULTS The cohort demonstrated divergence in senescence-associated biomarkers correlating with GLP-1RA exposure. The GLP-1RA–naïve control exhibited 8.0% ( n = 2,062 nuclei) epidermal senescence. By contrast, participants with 163 and 62 days of GLP-1RA exposure showed reduced senescence burdens of 2.4% ( n = 2,045) and 0.6% ( n = 1,636), respectively. Automated facial age estimation remained concordant with chronological age across subjects. CONCLUSION Systemic GLP-1RA exposure is associated with a substantial reduction in epidermal p21 expression in some subjects, suggesting a senomorphic effect and supporting further mechanistic studies at the interface of metabolic regulation and skin aging.

Abstract Purpose The purpose of this systematic review was to compare the inter‐ and intra‐observer reliability of Kellgren–Lawrence (KL) grading versus minimum joint space width (mJSW) measurement on knee radiographs, and to examine the influence of rater type, radiographic view and atlas‐based training. Methods A systematic search of PubMed/MEDLINE, Embase, Scopus and Web of Science was conducted through July 2025 following Preferred Reporting Items for Systematic reviews and Meta‐Analyses (PRISMA) guidelines. Studies reporting inter‐ and/or intra‐observer reliability for KL or mJSW on standard knee radiographs were included. Extracted data included kappa ( κ ) and intraclass correlation coefficient (ICC) values, rater expertise, radiographic view and atlas usage. Raters were categorized as expert, trainee/non‐expert or artificial intelligence (AI)‐assisted/automated. Results Twenty‐four studies (10,394 radiographs) met the inclusion criteria. Inter‐observer reliability was generally higher for mJSW (mean ICC = 0.82 ± 0.17) compared with KL (mean k = 0.72 ± 0.17), with similar trends observed for intra‐observer reliability. Experts and AI‐assisted systems outperformed trainees/non‐experts for both metrics. Reliability improved with semiflexed posteroanterior/Rosenberg views and atlas‐guided training. Conclusion This systematic review demonstrates that mJSW measurement generally shows higher reproducibility than KL grading. Reliability improved with expertise, standardized imaging and structured training, and AI‐assisted approaches performed comparably to expert raters. Quantitative mJSW should complement KL grading, particularly when combined with alignment measures, to enhance consistency and clinical relevance of radiographic assessment. Level of Evidence Level III.

Background: We aimed to develop automated data extraction pipelines with large language models (LLMs) to extract registry data from total hip arthroplasty (THA) operative notes and compare the performance with that of existing natural language processing (NLP) algorithms. Methods: We randomly sampled 1,000 primary THA cases from our institutional registry. Two human annotators manually reviewed each operative note for 3 data points: surgical approach, bearing surface, and fixation technique. All labeled THA notes were split into the development set (n = 239) and the testing set (n = 719). We developed a custom data extraction pipeline for each data point by combining an iteratively customized prompt with an LLM. The performance was compared with that of existing rules-based NLP algorithms. Results: The accuracy of LLMs was superior to that of NLP algorithms for all data points: surgical approach (96% compared with 94%), bearing surface (89% compared with 74%), and fixation technique (96% compared with 95%). Furthermore, the LLM accurately inferred the bearing surface for 80% of the notes that were ambiguous about the bearing surface. Conclusions: We developed LLM pipelines for extracting 3 registry-relevant data points from THA operative notes, demonstrating superior performance to existing NLP algorithms. Clinical Relevance: LLMs have the potential to impact clinical care, including the evaluation of electronic medical record free-text data. As registries serve as a cornerstone of orthopaedic evidence, this work demonstrates promise for LLMs to simplify, improve, and democratize the construction of registry databases from operative notes.

Introduction: Manual measurement of leg length (LL) and offset can be tedious. This study developed an automated algorithm for measuring LL and offset from pre- and postoperative AP pelvis radiographs in a large cohort of THA patients. Methods: Using a deep learning model trained on 1100 total AP pelvis radiographs, an algorithm was developed to calculate LL and offset. Algorithm measurements were compared with manual measurements by 4 raters on a sample of 100 pre- and postoperative image pairs. Inter- and intra-rater consistency was calculated using the intraclass correlation coefficient (ICC). The algorithm was applied to calculate the pre- and postoperative LL and offset discrepancies and the change in LL and offset bilaterally in a cohort of 15,951 image pairs. Results: ICC values between the algorithm and human raters ranged from 0.83 to 0.88 for offset measurements and 0.92 to 0.97 for LL measurements. Human raters demonstrated good-to-excellent inter-rater ICC and uniformly excellent intra-rater ICC. Entire database measurements demonstrated shorter LLs for arthritic joints versus the contralateral leg preoperatively and reduced LL discrepancy post-arthroplasty. Conclusions: We present a deep learning algorithm for calculating LL and offset using AP pelvis radiographs. This tool can support population-level studies and may assist operative management.

Objective: Diabetes mellitus affects over 25% of adults aged 65+ in the United States and is associated with a heightened risk of developing diabetic foot ulcers. Accumulation of senescent cells within the wound microenvironment can impair cutaneous repair, and senolytic therapies offer a promising approach to accelerate wound healing. Approach: In this study, topical fisetin, a flavonoid that promotes apoptosis of senescent cells, was evaluated for its impact on diabetic wound repair. Full-thickness excisional wounds were created on the dorsal skin of diabetic (db/db) mice. Animals were randomized to receive either topical vehicle ( n = 12) or fisetin ( n = 12) for three consecutive days per week, and wound healing outcomes were longitudinally assessed. Results: Topical fisetin significantly increased the ratio of healthy dermis to granulation by day 7 ( p = 0.04) and reduced dermal fibrosis by day 21 ( p = 0.03). Fisetin decreased p16+ cells ( p = 0.01) but increased p21+ cells ( p = 0.02) at day 7 in the dermis, indicating varied effects on senescent cells. M1 macrophages (CD80+) were decreased in the fisetin-treated group ( p = 0.05) at day 21, accompanied by a decrease in pro-inflammatory senescence-associated secretory phenotype cytokines, tumor necrosis factor alpha ( p = 0.03), and interleukin-1beta ( p = 0.01) at day 7. Innovation: Fisetin is the first topical seno-modulatory agent shown to enhance wound repair when administered after injury onset. Its favorable safety and efficacy in both modulating senescence and inflammation highlight its potential as a novel therapy for diabetic wounds. Conclusion: Topical fisetin enhances diabetic wound healing by reducing fibrosis and promoting healthy dermal regeneration through senescence and inflammation modulation. Saranya Wyles, MD, PhD