Fangcai Li

Objective: To systematically characterize temporal lipid metabolic remodeling in serum and spinal cord tissue following spinal cord injury (SCI) and to identify candidate serum lipid biomarkers associated with disease progression. Methods: A moderate-to-severe SCI model was established in Sprague-Dawley rats using a modified MASCIS weight-drop contusion protocol. Serum and injured spinal cord tissue were collected at 6 h, 1 d, 3 d, 7 d, and 14 d after injury. Broad-coverage lipidomic profiling was performed using ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). In parallel, transcriptomic profiling of injured spinal cord tissue was conducted, followed by bioinformatic analyses and RT-qPCR validation of selected metabolic genes. Results: Broad-coverage lipidomic analysis showed significant and dynamic lipid remodeling in both serum and spinal cord tissue after SCI. The serum lipid profile exhibited a multiphasic pattern, with early suppression, intermediate activation, and late-stage remodeling. Structural phospholipid subclasses showed coordinated temporal changes after injury. Long-chain acylcarnitines (LCACs) increased rapidly in serum during the acute phase and then gradually accumulated in the injured spinal cord tissue. In contrast, cholesteryl esters (CEs) progressively accumulated in spinal cord tissue, while showing a biphasic pattern in serum, with an early decrease followed by a rebound increase. Receiver operating characteristic curve analysis showed that LCACs and CEs had excellent discriminatory performance across multiple post-injury stages. Conclusions: SCI is associated with extensive lipid metabolic reprogramming in both serum and spinal cord tissue. LCACs and CEs represent promising candidate serum biomarkers closely associated with disease progression. Multi-omics analyses further suggest that serum lipid remodeling parallels lipid metabolic and inflammatory reprogramming in the injured spinal cord, while the contribution of local pathology to circulating LCAC and CE changes remains to be clarified.

Background: Peer teaching is an established pedagogical approach in medical education; yet, traditional methods face challenges including inconsistent knowledge support, variable teaching quality, and limited scalability. Artificial intelligence (AI) large language models offer potential to augment peer teaching by providing on-demand access to medical knowledge and clinical reasoning support. However, AI integration within structured peer teaching has not been systematically evaluated in clinical education. Objective: This study aims to evaluate the effectiveness of AI-assisted peer teaching compared to traditional peer teaching in orthopedic clinical education, with respect to knowledge acquisition, clinical skills development (particularly clinical reasoning), student engagement, and 3-month knowledge retention. Methods: This historical cohort study compared 2 consecutive cohorts of medical students (aged 20-27 years, 108/190, 56.8% male) at the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China. All eligible students from each cohort were enrolled. The control group (2021 cohort, n=96, taught in 2024) received traditional peer teaching; the intervention group (2022 cohort, n=94, taught in 2025) received AI-assisted peer teaching with access to DeepSeek-V3. Primary outcomes were assessed using a validated 50-item multiple-choice examination (0-100 points) and a 4-station Objective Structured Clinical Examination (OSCE; 0-100 points) with standardized rubrics (intraclass correlation coefficient>0.85). Secondary outcomes included student engagement and satisfaction (5-point Likert scales) and AI usage metrics. Assessments were conducted at baseline, postintervention (8 weeks), and 3-month follow-up. Analysis of covariance adjusted for baseline knowledge, prior AI experience, and learning interest to address observed baseline imbalances. Results: Using independent samples t tests (α=.05, 2-tailed), the AI-assisted group demonstrated significantly higher postintervention knowledge scores (mean 79.69, SD 8.41 vs mean 75.33, SD 9.26; mean difference=4.36, 95% CI 1.84-6.87; P<.001; Cohen d=0.49). OSCE total scores were significantly higher (mean 80.95, SD 7.57 vs mean 76.24, SD 9.23; mean difference=4.71, 95% CI 2.31-7.11; P<.001; d=0.56), with clinical reasoning showing the largest effect (mean difference=2.22, 95% CI 1.18-3.25; P<.001; d=0.61). Analysis of covariance adjusted results remained significant for all primary outcomes (adjusted knowledge difference=3.52, P=.002; adjusted OSCE difference=4.52, P<.001). At 3-month follow-up (174/190, 91.6%), the AI-assisted group maintained higher knowledge scores (mean 77.36, SD 8.60 vs mean 72.84, SD 10.42; mean difference=4.52, 95% CI 1.68-7.36; P=.002; d=0.47), with similar knowledge decay rates between groups. Conclusions: This study provides the first systematic evidence that integrating AI tools within structured peer teaching enhances orthopedic clinical education across multiple domains, including knowledge acquisition, OSCE performance, and student engagement. Unlike prior studies examining AI as a stand-alone learning tool, this work demonstrates the synergistic potential of combining AI knowledge support with peer teaching’s social learning benefits, with particularly strong effects on clinical reasoning. These findings support scalable, cost-effective implementation of AI-augmented peer teaching, though randomized controlled trials are needed to confirm causality and determine optimal implementation strategies.

Objective: To describe the incidence, types, severity, and risk factors of short-term complications following unilateral biportal endoscopic (UBE) cervical spine surgery in patients with cervical radiculopathy (CR) and cervical myelopathy (CM). Methods: We retrospectively analyzed 107 consecutive patients who underwent UBE cervical spine surgery at a single center between June 2021 and June 2024 (CR: n = 68; CM: n = 39). Demographic, clinical, and surgical data were collected. Complications were systematically documented and classified using the Clavien-Dindo system. Firth’s penalized logistic regression was used to identify risk factors for complications. Results: The overall complication rate was 12.1% (13/107). Complications occurred in 7.4% (5/68) of patients with CR and 20.5% (8/39) of patients with CM. In the CR group, all complications were minor (Clavien-Dindo Grade I) and resolved with conservative management. In the CM group, complications were more severe, including symptomatic epidural hematoma requiring surgical evacuation (10.3%). Independent risk factors identified by Firth’s penalized logistic regression included age ≥65 years (OR = 3.38, 95% CI 1.39–8.22, p = 0.007), comorbidities (OR = 2.70, 95% CI 1.19–6.13, p = 0.017), and laminectomy (versus foraminotomy) (OR = 3.15, 95% CI 1.36–7.30, p = 0.008). The learning curve analysis showed a significantly higher complication rate in the first 20 cases (8/20, 40.0%) compared with subsequent cases (5/87, 5.7%, p = 0.0002). Both groups achieved significant functional improvements at final follow-up. Conclusion: UBE cervical spine surgery demonstrates an acceptable safety profile in patients with cervical radiculopathy. However, patients with cervical myelopathy, particularly those undergoing multi-segment laminectomy and flavectomy, carry higher complication risks. Careful patient selection, surgical planning, and attention to the learning curve are essential to optimize outcomes.

This numerical simulation study aimed to investigate how the geometric matching between vertebral bony endplates and bone graft affected the segmental biom

Retrospective study. To assess the revision rate of lateral lumbar interbody fusion (LLIF) with lateral plate fixation (LLIF+LP) and to evaluate the preoperative radiological parameters associated with the need for revision. LLIF is a minimally invasive option that provides indirect decompression and favorable fusion rates; however, stand-alone LLIF has been associated with substantial early revision rates. The addition of lateral plate fixation (LLIF+LP) enhances segmental stability, yet the predictors of revision following LLIF+LP remain poorly defined. Patients who underwent LLIF+LP were categorized into two groups: non-revision and revision. Central canal stenosis, lateral recess stenosis, foraminal stenosis, facet joint degeneration, endplate Modic changes, ligamentum flavum hypertrophy (>4 mm), and fat infiltration (FI) grade of the lumbar multifidus muscle were evaluated. Clinical efficacy was determined using the Visual Analog Scale (VAS), Oswestry Disability Index (ODI), and Japanese Orthopaedic Association (JOA) scores, assessed both preoperatively and at the final follow-up. A total of 163 patients were included in the study, consisting of 144 in the non-revision group and 19 in the revision group, yielding an overall revision rate of 11.7% (19/163). Univariate and multivariate logistic regression analyses demonstrated that ligamentum flavum hypertrophy and the FI grade of the lumbar multifidus muscle were significantly associated with the revision rate (p0.05). LLIF+LP surgery yields favorable outcomes for lumbar degenerative diseases with a low reoperation rate. Preoperative evaluation of ligamentum flavum hypertrophy and the FI grade of the lumbar multifidus muscle may assist in guiding surgical planning, preoperative discussions, and management of patient expectations.

BACKGROUND Peer teaching is an established pedagogical approach in medical education; yet, traditional methods face challenges including inconsistent knowledge support, variable teaching quality, and limited scalability. Artificial intelligence (AI) large language models offer potential to augment peer teaching by providing on-demand access to medical knowledge and clinical reasoning support. However, AI integration within structured peer teaching has not been systematically evaluated in clinical education. OBJECTIVE This study aims to evaluate the effectiveness of AI-assisted peer teaching compared to traditional peer teaching in orthopedic clinical education, with respect to knowledge acquisition, clinical skills development (particularly clinical reasoning), student engagement, and 3-month knowledge retention. METHODS This historical cohort study compared 2 consecutive cohorts of medical students (aged 20-27 years, 108/190, 56.8% male) at the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China. All eligible students from each cohort were enrolled. The control group (2021 cohort, n=96, taught in 2024) received traditional peer teaching; the intervention group (2022 cohort, n=94, taught in 2025) received AI-assisted peer teaching with access to DeepSeek-V3. Primary outcomes were assessed using a validated 50-item multiple-choice examination (0-100 points) and a 4-station Objective Structured Clinical Examination (OSCE; 0-100 points) with standardized rubrics (intraclass correlation coefficient>0.85). Secondary outcomes included student engagement and satisfaction (5-point Likert scales) and AI usage metrics. Assessments were conducted at baseline, postintervention (8 weeks), and 3-month follow-up. Analysis of covariance adjusted for baseline knowledge, prior AI experience, and learning interest to address observed baseline imbalances. RESULTS Using independent samples t tests (α=.05, 2-tailed), the AI-assisted group demonstrated significantly higher postintervention knowledge scores (mean 79.69, SD 8.41 vs mean 75.33, SD 9.26; mean difference=4.36, 95% CI 1.84-6.87; P<.001; Cohen d=0.49). OSCE total scores were significantly higher (mean 80.95, SD 7.57 vs mean 76.24, SD 9.23; mean difference=4.71, 95% CI 2.31-7.11; P<.001; d=0.56), with clinical reasoning showing the largest effect (mean difference=2.22, 95% CI 1.18-3.25; P<.001; d=0.61). Analysis of covariance adjusted results remained significant for all primary outcomes (adjusted knowledge difference=3.52, P=.002; adjusted OSCE difference=4.52, P<.001). At 3-month follow-up (174/190, 91.6%), the AI-assisted group maintained higher knowledge scores (mean 77.36, SD 8.60 vs mean 72.84, SD 10.42; mean difference=4.52, 95% CI 1.68-7.36; P=.002; d=0.47), with similar knowledge decay rates between groups. CONCLUSIONS This study provides the first systematic evidence that integrating AI tools within structured peer teaching enhances orthopedic clinical education across multiple domains, including knowledge acquisition, OSCE performance, and student engagement. Unlike prior studies examining AI as a stand-alone learning tool, this work demonstrates the synergistic potential of combining AI knowledge support with peer teaching’s social learning benefits, with particularly strong effects on clinical reasoning. These findings support scalable, cost-effective implementation of AI-augmented peer teaching, though randomized controlled trials are needed to confirm causality and determine optimal implementation strategies.

There was a mistake in the Funding statement, an incorrect number was used. The correct number is "2015C03Bd051.". The publisher apologizes for this mistake. The original version of this article has been updated. In the published article, there was a mistake in the Funding statement. The funding statement for the Key Development Project of the Department of Science and Technology was displayed as "2015CBd051". The correct statement is "Key Development Project of Department of Science and Technology (2015C03Bd051).'' Correction on: Yu C, Wu Y, Huang H, Li X, Wang J, Liu C, Shen Y, Huang D, Tang R, Wang Z, Jiang L and Li F (2025) Molecular mechanisms of deer antler in promoting osteogenic differentiation of human mesenchymal stem cells via JUN modulation. Front. Immunol. 16:1550249. doi:10.3389/fimmu.2025.1550249

Background: Traditional Chinese medicine and food deer antler has been extensively used in bone regeneration, but its molecular mechanisms remain poorly understood. Preliminary investigations suggest deer antler contains bioactive compounds that influence osteogenic differentiation and immune microenvironments.Purpose: To elucidate the molecular mechanisms of deer antler in promoting human mesenchymal stem cell (hMSC) osteogenic differentiation, focusing on JUN downregulation and immune microenvironment modulation using bioinformatics and molecular docking approaches.Methods: Chemical components and targets were identified using the BATMAN-TCM database. Differentially expressed genes (DEGs) related to osteogenic differentiation were analyzed using Gene Expression Omnibus datasets. Gene Ontology (GO), KEGG enrichment, LASSO regression, and SVM-RFE were applied to identify key genes. A Protein-Protein Interaction (PPI) network was constructed to determine core genes. JUN expression was validated using independent datasets and ROC analysis. Immune cell infiltration was analyzed using CIBERSORT, examining JUN’s correlation with immune cells. Molecular docking explored JUN’s interaction with two active deer antler compounds.Results: The study identified 62 bioactive compounds and 1051 potential targets. DEGs analysis revealed 282 genes associated with osteogenic differentiation. Cross-analysis identified 43 overlapping genes, enriched in “response to mechanical stimulus” and “rheumatoid arthritis” pathways. Machine learning approaches highlighted 7 critical genes, with JUN emerging as the core gene. JUN levels were significantly decreased during osteogenic differentiation, showing high diagnostic accuracy (AUCs: 0.977-1.000). Immune cell analysis revealed JUN correlations with neutrophils, monocytes, eosinophils, M2 macrophages, and resting CD4+ T cells. Molecular docking confirmed strong binding affinities of JUN with Retinol (-8.1 kcal/mol) and Progesterone (-6.0 kcal/mol).Conclusions: The study provides a comprehensive molecular framework demonstrating JUN as a key molecule in hMSC osteogenic differentiation. Deer antler’s bioactive compounds, particularly Retinol and Progesterone, potentially exert therapeutic effects through targeted JUN modulation, offering novel insights into natural compound-mediated bone regenerative mechanisms.

Purpose: Understanding the risk factors associated with unscheduled readmission following lateral lumbar interbody fusion (LLIF) is crucial for mitigating the occurrence of these costly events. This study aims to ascertain the incidence and factors of unscheduled hospital readmission subsequent to LLIF.Methods: A retrospective analysis was conducted on patients who underwent LLIF at our institution from March 2016 to February 2023. Instances of unscheduled hospital readmission after LLIF were meticulously recorded, including baseline demographics, characteristics of spine pathology, surgical interventions, duration between two hospitalizations, and hospitalization costs and duration. Reasons for readmission were categorized based on their etiology. A case–control methodology was employed to compare unscheduled hospital readmission patients against planned readmission patients due to staged surgery. Parametric data were analyzed with a two‐tailed T‐test, nonparametric data with the Wilcoxon rank‐sum test, and categorical data with the χ ² test.Results: A total of 1521 patients who received LLIF at our institution were included in the study. A total of 59 patients (3.88%) were unscheduled readmitted due to adjacent segment disease (ASD), cage subsidence, the original surgical segments remaining narrow, spondylodiscitis, and pain. 51 patients (3.35%) experienced reoperation, predominantly attributable to ASD. Compared to planned readmission patients, unscheduled readmission patients tended to be younger, had a lower likelihood of having scoliosis, and were more likely to have short‐segment surgery and higher initial hospitalization costs. Among unscheduled readmission patients, patients receiving short‐segment surgery, as well as those who paid less during the initial hospitalization, demonstrated a higher likelihood of a 90‐day readmission rate.Conclusion: Our findings indicated the heightened risks of unscheduled hospital readmission after LLIF. Taking targeted measures against these risk factors is expected to reduce the healthcare burden caused by unplanned readmissions in the future.