Lin Lu

Objective: Non-steroidal anti-inflammatory drugs (NSAIDs) for acute gout often carry risks of gastrointestinal adverse effects and hepatic, renal, and cardiovascular toxicity. To explore safer treatment options and reduce the use of these drugs and associated risks, this study aimed to evaluate the clinical efficacy and safety of external application of Jinhuang Ointment in the treatment of acute gouty arthritis, providing evidence for optimizing clinical management strategies. Methods: A total of 104 patients with acute gouty arthritis (July 2023–June 2024) were retrospectively assigned to three groups: the control group (n = 35) received conventional Western therapy (celecoxib, low-purine diet, >2000 mL daily water intake, and sodium bicarbonate); T1 group (n = 35) received conventional therapy plus topical diclofenac diethylamine emulsion; T2 group (n = 34) received conventional therapy plus Jinhuang Ointment. All patients completed 7-day treatment. Baseline data including demographic characteristics, gout history, serum uric acid levels, onset time, and target joint distribution were collected. Primary outcome indicators, including pain improvement, joint tenderness, swelling, and joint mobility in the target joint. Secondary outcomes covered changes in serum uric acid levels and inflammatory markers (CRP, ESR). Adverse events occurring during the treatment period were also documented. Results: Baseline characteristics were comparable across groups. After treatment, all groups showed significant improvements in VAS scores, joint swelling, tenderness, and mobility (P < 0.05). The T2 group exhibited a significantly shorter time to pain improvement compared to both the T1 and control groups (P < 0.05). Both T1 and T2 groups showed better outcomes in joint tenderness, swelling, and joint mobility than the control group (P < 0.05), though only T2 demonstrated significant superiority in swelling reduction (P < 0.05). Laboratory results indicated that CRP and ESR decreased more markedly after treatment in T1 and T2 than in controls (P < 0.05), with no intergroup difference in uric acid reduction. Adverse event rates were similar among groups, with no significant differences observed. Conclusion: Jinhuang Ointment combined with conventional therapy significantly improves joint pain and swelling, accelerates symptom relief, and exhibits particular efficacy in swelling reduction, without increasing safety risks. Its multi-component, multi-target mechanism provides a potential explanation for the clinical efficacy observed, thereby supporting the need for higher-quality studies to further verify its application value.

Low back pain (LBP) is a common musculoskeletal disorder, and its pathological basis is closely related to intervertebral disc degeneration (IVDD). Although commonly used conservative treatments and surgical interventions can alleviate symptoms, they are difficult to fundamentally delay or reverse the process of IVDD. In recent years, biological treatment strategies centered on cell therapy, targeting the initiating mechanisms of IVDD, have provided new directions for the fundamental treatment of this disease. Among them, mesenchymal stem cells (MSCs) are regarded as ideal candidate cell types for achieving intervertebral disc regeneration due to their immunomodulatory properties and multidifferentiation potential. The discovery of endogenous stem cells within the intervertebral disc further reveals the tissue’s own repair potential. As the “soil” upon which “seed” cells depend for survival, the intervertebral disc microenvironment, composed of cell niches and their surrounding biochemical and physical factors, plays a key regulatory role in the proliferation, differentiation, and functional expression of stem cells and endogenous cells. However, the intervertebral disc is naturally in extreme conditions such as low oxygen, low nutrition, acidic pH, and high mechanical load. This microenvironment further deteriorates during the degeneration process, not only severely affecting the survival and function of resident cells but also posing severe challenges for stem cell transplantation therapy. This article systematically reviews the characteristics of the intervertebral disc microenvironment under physiological and degenerative states, focusing on the impact of the degenerative microenvironment on the biological behavior of stem cells. It summarizes key strategies for enhancing MSC adaptability and therapeutic efficacy and proposes standardized parameters for optimizing clinical applications, aiming to provide a theoretical basis and path support for advancing the clinical translation of stem cell therapy in intervertebral disc regeneration.

Objective: Geriatric hip fractures, clinically designated as “the last fracture in life” due to significant disability and mortality, pose critical educational barriers for orthopedic residents in mastering complex hip anatomy and surgical decision-making competencies. This study evaluates the implementation value of integrating digital 3D-printing technology with CBL pedagogy in standardized residency training for geriatric hip fracture management.Methods: Fifty-six orthopedic residents undergoing standardized training at Renmin Hospital of Wuhan University were enrolled and randomly assigned to either the control group (CBL, n = 28) or the experimental group (CBL-3DP, n = 28). Both groups received training in surgical planning for geriatric hip fractures. The CBL group underwent traditional CBL teaching, while the CBL-3DP group combined CBL with 3D-printed fracture models. Post-training assessments evaluated theoretical knowledge, practical skills, satisfaction, and engagement with the teaching methodology.Results: Following the instructional intervention, the CBL-3DP group demonstrated significantly superior performance compared to the conventional CBL group in both theoretical and practical assessments. Theoretically, the CBL-3DP cohort achieved higher scores in regional anatomy comprehension, fracture classification accuracy, and mastery of treatment principles (p < 0.05). Practically, this group exhibited enhanced competencies in geriatric hip fracture management domains including patient consultation, physical examination, diagnostic precision, basic emergency management, and preoperative surgical planning (p < 0.05). Questionnaire analyses further indicated that the 3D + CBL approach yielded significantly better outcomes than traditional CBL in: depth of understanding of geriatric hip fractures, learning enthusiasm, diagnostic capabilities, surgical planning proficiency, confidence in managing clinical cases, and satisfaction with the instructional methodology.Conclusion: The integration of 3D printing with CBL methodology enhances training effectiveness and learner satisfaction in geriatric hip fracture education, supporting its adoption in standardized orthopedic residency programs.

Objective: The diagnosis and treatment of arthropathic orthopaedic conditions are inherently linked to anatomical structures, necessitating strong spatial visualization abilities in students. Providing intuitively accessible methods for students to master specialized knowledge presents a formidable challenge for educators. This study aims to evaluate the pedagogical value of integrating 3D-printed model with mixed reality (MR) technology in clinical orthopaedic surgery education.Methods: Thirty-six senior clinical medical undergraduates were randomized into two groups. The experimental group underwent training using the combined 3D- printed model and mixed reality (MR) technology, while the control group received traditional instruction. Learning outcomes were evaluated through standardized Objective Structured Clinical Examination (OSCE) assessments and questionnaires. Correlation analysis was conducted between total OSCE scores and questionnaire scores.Results: The experimental group achieved significantly higher OSCE scores compared to the control group (p < 0.05). Questionnaire analysis indicated that a majority of students in the experimental group perceived the integrated 3D printing and mixed reality (MR) teaching approach as innovative. They also reported significant improvements in anatomical comprehension, interest stimulation, teacher-student interaction, knowledge retention, learning efficiency, and practical skills (p < 0.05). Furthermore, OSCE total scores demonstrated strong positive correlations with all questionnaire subdomains (r > 0.8, p < 0.001). The strongest correlation was observed between learning efficiency and OSCE total scores (r = 0.918).Conclusion: Integrating 3D-printed model and MR technology into orthopedic joint surgery clinical teaching significantly boosts student performance, learning efficiency, and overall teaching quality, demonstrating strong potential for wider application.

Cuproptosis, a recently identified form of copper-dependent cell death, arises from intracellular copper dyshomeostasis. As an essential trace element, copper plays a critical role in bioenergetic metabolism, redox regulation, and synaptic transmission. However, excessive copper exerts cytotoxic effects through multiple pathways, including increased reactive oxygen species (ROS) production, apoptotic cascade activation, necrotic membrane rupture, inflammatory responses, and mitochondrial dysfunction. Distinct from other cell death mechanisms, cuproptosis is characterized by copper ion binding to acetylated mitochondrial respiratory chain proteins, leading to pathogenic protein aggregation, iron-sulfur cluster depletion, and cellular collapse. Emerging evidence underscores aberrant copper accumulation and resultant proteotoxic stress as pivotal contributors to the pathogenesis of multiple musculoskeletal pathologies, including osteoporosis, osteoarthritis, sarcopenia, osteosarcoma, intervertebral disc degeneration, spinal cord injury, and biofilm-associated orthopedic infections. Understanding the spatiotemporal regulation of cuproptosis may provide novel opportunities for advancing diagnostic and therapeutic approaches in orthopedic medicine. This review synthesizes current insights into the molecular mechanisms of cuproptosis, its pathogenic role in musculoskeletal diseases, and the potential for biomarker-driven therapeutic interventions.

Objective To evaluate the accuracy and parsing ability of GPT 4.0 for Japanese medical practitioner qualification examinations in a multidimensional way to investigate its response accuracy and comprehensiveness to medical knowledge. Methods We evaluated the performance of the GPT 4.0 on Japanese Medical Licensing Examination (JMLE) questions (2021–2023). Questions are categorized by difficulty and type, with distinctions between general and clinical parts, as well as between single-choice (MCQ1) and multiple-choice (MCQ2) questions. Difficulty levels were determined on the basis of correct rates provided by the JMLE Preparatory School. The accuracy and quality of the GPT 4.0 responses were analyzed via an improved Global Qualily Scale (GQS) scores, considering both the chosen options and the accompanying analysis. Descriptive statistics and Pearson Chi-square tests were used to examine performance across exam years, question difficulty, type, and choice. GPT 4.0 ability was evaluated via the GQS, with comparisons made via the Mann-Whitney U or Kruskal-Wallis test. Results The correct response rate and parsing ability of the GPT4.0 to the JMLE questions reached the qualification level (80.4%). In terms of the accuracy of the GPT4.0 response to the JMLE, we found significant differences in accuracy across both difficulty levels and option types. According to the GQS scores for the GPT 4.0 responses to all the JMLE questions, the performance of the questionnaire varied according to year and choice type. Conclusion GTP4.0 performs well in providing basic support in medical education and medical research, but it also needs to input a large amount of medical-related data to train its model and improve the accuracy of its medical knowledge output. Further integration of ChatGPT with the medical field could open new opportunities for medicine.

Objective This study aimed to evaluate and compare the effectiveness of knowledge base-optimized and unoptimized large language models (LLMs) in the field of orthopedics to explore optimization strategies for the application of LLMs in specific fields. Methods This research constructed a specialized knowledge base using clinical guidelines from the American Academy of Orthopaedic Surgeons (AAOS) and authoritative orthopedic publications. A total of 30 orthopedic-related questions covering aspects such as anatomical knowledge, disease diagnosis, fracture classification, treatment options, and surgical techniques were input into both the knowledge base-optimized and unoptimized versions of the GPT-4, ChatGLM, and Spark LLM, with their generated responses recorded. The overall quality, accuracy, and comprehensiveness of these responses were evaluated by 3 experienced orthopedic surgeons. Results Compared with their unoptimized LLMs, the optimized version of GPT-4 showed improvements of 15.3% in overall quality, 12.5% in accuracy, and 12.8% in comprehensiveness; ChatGLM showed improvements of 24.8%, 16.1%, and 19.6%, respectively; and Spark LLM showed improvements of 6.5%, 14.5%, and 24.7%, respectively. Conclusion The optimization of knowledge bases significantly enhances the quality, accuracy, and comprehensiveness of the responses provided by the 3 models in the orthopedic field. Therefore, knowledge base optimization is an effective method for improving the performance of LLMs in specific fields.

Objectives: To explore an intelligent detection technology based on deep learning algorithms to assist the clinical diagnosis of distal radius fractures (DRFs), and further compare it with human performance to verify the feasibility of this method.Methods: A total of 3,240 patients (fracture: n = 1,620, normal: n = 1,620) were included in this study, with a total of 3,276 wrist joint anteroposterior (AP) X-ray films (1,639 fractured, 1,637 normal) and 3,260 wrist joint lateral X-ray films (1,623 fractured, 1,637 normal). We divided the patients into training set, validation set and test set in a ratio of 7:1.5:1.5. The deep learning models were developed using the data from the training and validation sets, and then their effectiveness were evaluated using the data from the test set. Evaluate the diagnostic performance of deep learning models using receiver operating characteristic (ROC) curves and area under the curve (AUC), accuracy, sensitivity, and specificity, and compare them with medical professionals.Results: The deep learning ensemble model had excellent accuracy (97.03%), sensitivity (95.70%), and specificity (98.37%) in detecting DRFs. Among them, the accuracy of the AP view was 97.75%, the sensitivity 97.13%, and the specificity 98.37%; the accuracy of the lateral view was 96.32%, the sensitivity 94.26%, and the specificity 98.37%. When the wrist joint is counted, the accuracy was 97.55%, the sensitivity 98.36%, and the specificity 96.73%. In terms of these variables, the performance of the ensemble model is superior to that of both the orthopedic attending physician group and the radiology attending physician group.Conclusion: This deep learning ensemble model has excellent performance in detecting DRFs on plain X-ray films. Using this artificial intelligence model as a second expert to assist clinical diagnosis is expected to improve the accuracy of diagnosing DRFs and enhance clinical work efficiency.

Objective: Explore a new deep learning (DL) object detection algorithm for clinical auxiliary diagnosis of lumbar spondylolisthesis and compare it with doctors’ evaluation to verify the effectiveness and feasibility of the DL algorithm in the diagnosis of lumbar spondylolisthesis.Methods: Lumbar lateral radiographs of 1,596 patients with lumbar spondylolisthesis from three medical institutions were collected, and senior orthopedic surgeons and radiologists jointly diagnosed and marked them to establish a database. These radiographs were randomly divided into a training set (n = 1,117), a validation set (n = 240), and a test set (n = 239) in a ratio of 0.7 : 0.15: 0.15. We trained two DL models for automatic detection of spondylolisthesis and evaluated their diagnostic performance by PR curves, areas under the curve, precision, recall, F1-score. Then we chose the model with better performance and compared its results with professionals’ evaluation.Results: A total of 1,780 annotations were marked for training (1,242), validation (263), and test (275). The Faster Region-based Convolutional Neural Network (R-CNN) showed better precision (0.935), recall (0.935), and F1-score (0.935) in the detection of spondylolisthesis, which outperformed the doctor group with precision (0.927), recall (0.892), f1-score (0.910). In addition, with the assistance of the DL model, the precision of the doctor group increased by 4.8%, the recall by 8.2%, the F1-score by 6.4%, and the average diagnosis time per plain X-ray was shortened by 7.139 s.Conclusion: The DL detection algorithm is an effective method for clinical diagnosis of lumbar spondylolisthesis. It can be used as an assistant expert to improve the accuracy of lumbar spondylolisthesis diagnosis and reduce the clinical workloads.

Chronic diseases are a growing concern worldwide, with nearly 25% of adults suffering from one or more chronic health conditions, thus placing a heavy burden on individuals, families, and healthcare systems. With the advent of the “Smart Healthcare” era, a series of cutting-edge technologies has brought new experiences to the management of chronic diseases. Among them, smart wearable technology not only helps people pursue a healthier lifestyle but also provides a continuous flow of healthcare data for disease diagnosis and treatment by actively recording physiological parameters and tracking the metabolic state. However, how to organize and analyze the data to achieve the ultimate goal of improving chronic disease management, in terms of quality of life, patient outcomes, and privacy protection, is an urgent issue that needs to be addressed. Artificial intelligence (AI) can provide intelligent suggestions by analyzing a patient’s physiological data from wearable devices for the diagnosis and treatment of diseases. In addition, blockchain can improve healthcare services by authorizing decentralized data sharing, protecting the privacy of users, providing data empowerment, and ensuring the reliability of data management. Integrating AI, blockchain, and wearable technology could optimize the existing chronic disease management models, with a shift from a hospital-centered model to a patient-centered one. In this paper, we conceptually demonstrate a patient-centric technical framework based on AI, blockchain, and wearable technology and further explore the application of these integrated technologies in chronic disease management. Finally, the shortcomings of this new paradigm and future research directions are also discussed.