This research focuses on the development and application of a novel enzyme‐responsive hydrogel, isoquercitrin‐loaded MMP‐responsive PEG–peptide hydrogel (PEG‐pp‐Iso), for osteochondral defect repair. The hydrogel is synthesized by conjugating isoquercitrin to poly (ethylene glycol) (PEG) and is further enhanced with hydroxyapatite (HAp) to facilitate cartilage and subchondral bone regeneration. To understand the molecular mechanisms behind its regenerative potential, transcriptomic analysis, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, least absolute shrinkage and selection operator (LASSO) regression, and support vector machine‐recursive feature elimination (SVM‐RFE) algorithms, reveals that the CXCL12/CXCR4‐CDC42 signaling axis plays a crucial role in regulating bone marrow mesenchymal stem cells (BMSCs) migration, chondrogenic differentiation, and immune modulation. In vitro, PEG‐pp‐Iso effectively enhances the antioxidant activity, proliferation, migration, and chondrogenic differentiation of BMSCs, as evidenced by increased SOX9 and COL II expression. The inhibition of the CXCL12/CXCR4‐CDC42 axis significantly reduces these effects, confirming its importance in promoting cartilage repair. In vivo, PEG‐pp‐Iso hydrogels promote the migration of BMSCs to osteochondral defect sites, enhances cartilage matrix formation, and improve immune microenvironment regulation by increasing anti‐inflammatory macrophages and reducing pro‐inflammatory ones. These results highlight the potential of PEG‐pp‐Iso in promoting osteochondral regeneration and offer a novel approach to osteochondral defect repair through modulation of immune responses and stem cell activity.
BackgroundAchieving high-precision, low-latency, and continuously adaptive human activity recognition on resource-constrained edge devices represents a core challenge. Existing research primarily focuses on improvements in single directions, such as “online learning,” “model sparsification,” or “feature extraction,” lacking a framework that synergistically optimizes all three. This leads to difficulties in dynamically balancing accuracy, latency, and power consumption when processing non-stationary sensor data streams.MethodsTo address this, this paper designs an end-to-end closed-loop adaptive learning framework. The core innovation of this framework lies in its system-level synergistic design: (1) Employing fast principal component analysis for adaptive feature dimensionality reduction; (2) Introducing an information theory-based dynamic sparse subnetwork activation mechanism to tackle the NP-hard problem of model selection; and (3) Integrating a low-complexity online incremental learning module for real-time tracking of concept drift. Through the closed-loop feedback and control of the aforementioned components, this framework achieves joint dynamic optimization of feature extraction, model complexity, and adaptation speed under edge computing constraints.ResultsExperimental results across five datasets demonstrate that this framework achieves accuracies ranging from 85.6% to 97.4%, with inference latency of approximately 1.0 ms.ConclusionThe framework comfortably meets the real-time requirement.
Acne sequelae primarily include post-acne erythema, post-acne hyperpigmentation, and post-acne scarring. These residual cutaneous lesions persist in a substantial proportion of patients following the resolution of active acne and arise from factors such as inflammatory responses and inappropriate manipulation of lesions. Acne sequelae are highly prevalent, impose a considerable disease burden, and exert a profound negative impact on patients’ physical appearance, psychological well-being, and quality of life. Although a wide range of therapeutic modalities is currently available and new treatment technologies continue to emerge, substantial variability exists in treatment efficacy, and clinical decision-making remains highly heterogeneous. To promote standardized and evidence-based approaches to the diagnosis, prevention, and management of acne sequelae, an expert panel developed this consensus through a comprehensive review of recently published domestic and international literature, integrated with current clinical practice in China. This consensus is intended to provide practical and authoritative guidance for clinicians in the diagnosis, prevention, and treatment of acne sequelae.
To compare perioperative outcomes and assess associations of surgical approach with overall survival (OS) and composite local recurrence after separation surgery (SS) versus expanded anterior column resection (EACR) for spinal metastases. This single-center retrospective cohort included 167 patients treated from August 2018 to August 2021 (SS, n = 50; EACR, n = 117). Follow-up ended at death, last contact, or December 31, 2024. OS was modeled using multivariable Cox regression and propensity-score overlap weighting. Composite local recurrence (radiographic regrowth or recurrent level-specific neurological symptoms) was modeled using Fine-Gray regression with death as a competing event. Compared with SS, EACR was associated with greater blood loss (1388.9 vs 1055.0 mL; P = 0.006), longer operative time (296.5 vs 255.3 min; P < 0.001), and greater 48-hour drainage (297.9 vs 228.0 mL; EACR, n = 116; P = 0.008). Median OS was 17 months after EACR and 13 months after SS (log-rank P = 0.326). For SS versus EACR, the adjusted mortality hazard ratio was 1.16 (95
Facial assessment is central to treatment planning and outcome evaluation. Traditional approaches, including visual inspection, manual anthropometry, and classical esthetic proportions, are limited by inter-observer variability and challenges in accounting for cultural and demographic diversity. Recent advances in digital imaging and computational analysis now enable more objective, reproducible, and clinically standardized evaluation. This structured review employed a transparent literature search across Web of Science, PubMed, and Google Scholar to identify peer-reviewed publications published between 2020 and 2025. The aim was to provide a comprehensive overview of contemporary facial assessment technologies. The retrieved evidence was synthesized narratively and qualitatively with emphasis on clinical accuracy, reproducibility, workflow integration, and patient-reported outcome measures (PROMs). A large body of contemporary literature was reviewed and synthesized. The synthesized literature demonstrates a clear progression from qualitative visual assessment toward quantitative and technology-supported evaluation. Modern three-dimensional (3D) imaging systems achieve submillimeter precision, enabling reliable volumetric measurement for surgical planning and longitudinal monitoring. Automated approaches assist in grading wrinkles, pigmentation, and other aging-related features. However, important limitations remain, including inconsistent acquisition protocols, restricted demographic representation in reference datasets, and the need for clearer standards for prospective validation and workflow integration. Facial assessment in esthetic medicine is evolving toward multimodal integration of 3D volumetry, AI-driven diagnostics, and PROMs. Future clinical practice will benefit from the integration of objective morphological data with patient-reported outcomes, although developing unified platforms requires further prospective validation and real-world clinical studies. Level of Evidence: Not applicable (Review Article). This journal requires that authors assign a level of evidence to each submission to which Evidence-Based Medicine rankings are applicable. This excludes Review Articles, Book Reviews, and manuscripts that concern Basic Science, Animal Studies, Cadaver Studies, and Experimental Studies. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .
