Background and objective: Large Language Models (LLMs) show significant potential in healthcare, but their application in Traditional Chinese Medicine (TCM) lacks systematic evaluation. This study aims to comprehensively review LLMs tuning techniques, data construction strategies, evaluation methods, and application scenarios in TCM clinical practice. Methods: A scoping review following PRISMA-ScR guidelines was conducted. Researchers systematically searched seven databases for relevant studies published between database inception to May 2025. The analysis focused on identifying model characteristics, tuning techniques, data sources, evaluation methods, application domains and performance limitations to assess the current state and future directions of TCM-oriented LLMs. Results: We included 27 studies (21 in English, 6 in Chinese). Application domains comprised TCM knowledge consultation (10 studies) and diagnostic assistance (13 studies), with 4 studies establishing TCM LLMs evaluation benchmarks. LoRA fine-tuning was most widely used (65.2%), often combined with prompt engineering (47.8%), continued pre-training (43.5%), and retrieval-augmented generation (39.1%). Most studies (87.0%) employed multiple technique combinations. Training data balanced theoretical knowledge (classics) with clinical experience (case records), though multimodal data remained severely insufficient. Evaluation methods were multidimensional, with accuracy (63.0%) and human assessment (77.8%) most frequently used. Specialized TCM evaluation benchmarks were gradually established. Current models excel at integrating heterogeneous knowledge, basic syndrome differentiation reasoning, and cross-language knowledge conversion, but show limitations in simulating complex TCM reasoning processes and individualized diagnosis. Conclusion: Although TCM-oriented LLMs demonstrate effectiveness in knowledge consultation and diagnostic tasks, they face significant challenges in capturing TCM's holistic paradigm, data quality, and clinical evaluation. Future research should develop TCM-compatible model architectures, build standardized multimodal data ecosystems, strengthen clinical translation, and create evaluation frameworks that reflect TCM's diagnostic process.

Osteoporosis, a prevalent metabolic bone disease, is characterized by reduced bone density and an increased risk of fractures. The primary challenge in tre

Background: Seated lumbar rotation manipulation is widely used for low back pain, but lacks detailed biomechanical analysis. Understanding its biomechanical characteristics is crucial for therapists to improve comprehension and support education and research. The purpose of this study was to analyze the kinematic and mechanical parameters of Seated lumbar rotation manipulation.Methods: Sixty healthy volunteers underwent manipulation by experienced therapists. Three-dimensional movements, thrust velocity, and acceleration were measured using motion capture technology. Force parameters were recorded using pressure sensing gloves mounted on the therapist’s hands. Subgroup comparisons were conducted based on body mass index, and linear regression was used to analyse the relationship between force parameters and BMI (Body Mass Index). Finally, Pearson’s correlation test was employed to examine the correlation between the forces exerted by both hands during each procedure.Results: Kinematic analysis indicated that the angles in three directions were greatest for rotation, followed by lateral bending and flexion. Similarly, rotation was the dominant angular velocities, greater than lateral flexion and anteflexion. Furthermore, Preload duration (2.72 ± 0.10 s) and thrust duration (0.48 ± 0.04 s) were recorded. In terms of force, four key force metrics were calculated: preload force (58.99 ± 9.76 N), valley force (23.25 ± 6.24 N), thrust force (50.54 ± 9.63 N), and peak force (73.77 ± 11.06 N). While the preload rate (21.73 ± 4.66 N/s), thrust rate (106.30 ± 11.72 N/s), and the maximum torque (51.86 ± 7.52 N m) were determined. Subgroup analysis showed significant differences in force parameters by body types (P < 0.01). Linear regression revealed a positive correlation between BMI and force parameters (P < 0.05), and Pearson analysis indicated a significant correlation between forces exerted by both hands (P < 0.05).Conclusion: Seated lumbar rotation manipulation is characterized by long-lever, three-dimensional coupled movements with high-velocity, low-amplitude thrusts. Additionally, the force parameters are positively influenced by somatotype, and bilateral hand force exerts a synergistic effect. This valuable biomechanical quantification help comprehending the technique and supporting its educational and experimental settings.

Background: Neck pain (NP) ranks among the leading causes of years lived with disability worldwide. While spinal manipulation is a common physical therapy intervention for NP, its variable patient responses and inherent risks necessitate careful patient selection. This study aims to develop and validate a machine learning-based prediction model to identify NP patients most likely to benefit from spinal manipulation. Methods: This multicenter study analyzed 623 NP patients in a retrospective cohort and 319 patients from a separate hospital for external validation, with data collected between May 2020 and November 2024. Treatment success was defined as achieving ≥ 50% reduction in Numerical Rating Scale (NRS) and ≥ 30% reduction in Neck Disability Index (NDI) after two weeks of spinal manipulation. We compared data imputation methods through density plots, and conducted δ-adjusted sensitivity analysis. Then employed both Boruta algorithm and LASSO regression to select relevant predictors from 40 initial features, and four feature subsets (Boruta-selected, LASSO-selected, intersection, and union) were evaluated to determine the optimal combination. Nine machine learning algorithms were tested using internal validation (70% training, 30% testing) and external validation. Performance metrics included Area Under the Receiver Operating Characteristic Curve (AUC), accuracy, F1-score, sensitivity, specificity, and predictive values. The SHAP framework enhanced model interpretability. Youden’s Index was applied to determine the optimal predictive probability threshold for clinical decision support, and a web-based application was developed for clinical implementation. Results: The combined LASSO and Boruta algorithms identified nine optimal predictors, with the union feature set achieving superior performance. Among the algorithms tested, the Multilayer Perceptron (MLP) model demonstrated optimal performance with an AUC of 0.823 (95% CI 0.750, 0.874) in the test set, showing consistency between training (AUC = 0.829) and test performance. External validation confirmed robust performance (AUC: 0.824, accuracy: 0.765, F1 score: 0.76) with satisfactory calibration (Brier score = 0.170). SHAP analysis highlighted the significant predictive value of clinical measurements and patient characteristics. Based on Youden’s Index, the optimal predictive probability threshold was 0.603, yielding a sensitivity of 0.762 and specificity of 0.802. The model was implemented as a web-based application providing real-time probability calculations and interactive SHAP force plots. Conclusion: Our machine learning model demonstrates robust performance in identifying suitable candidates for spinal manipulation among neck pain patients, offering clinicians an evidence-based practical tool to optimize patient selection and potentially improve treatment outcomes.

To investigate the roles of the mechanically-sensitive channel Piezo1 and the inflammatory factor IL-6 in cervical spondylotic radiculopathy (CSR) pain and

Intervertebral disc degeneration is a major structural correlate of low back pain and is increasingly viewed as a disorder of mechanobiological dysregulation rather than simple mechanical overload. Over the past decade, disc cells have been shown to engage a diverse mechanosensory repertoire, including Piezo1, transient receptor potential channels, integrin–focal adhesion complexes, acid-sensing ion channels, primary cilia, and cytoskeletal–nuclear signaling axes. These upstream sensors converge on a more limited set of downstream hubs, most notably Yes-associated protein and transcriptional co-activator with PDZ-binding motif and mitogen-activated protein kinase/nuclear factor kappa B, which then shape cell-fate programs such as senescence, autophagy, pyroptosis, and ferroptosis. The extracellular matrix functions both as the substrate and as the output of mechanotransduction, creating a self-reinforcing loop in which degenerative matrix changes amplify pathological mechanosensing. Despite substantial molecular progress, mechanistic understanding remains concentrated in nucleus pulposus models, whereas matched cross-compartment analyses involving the annulus fibrosus, cartilage endplate, and vertebral interface remain limited. As a result, the relative importance of mechanosensors across compartments and loading contexts is still unresolved. Multiscale platforms, including finite element modeling, tunable hydrogels, organ culture, and quantitative magnetic resonance imaging, have strengthened causal interrogation, but translational progress remains largely preclinical. The clearest advance to date has come from biomaterial-based strategies, supported by early human feasibility data for injectable hydrogel implants. In this Review, we synthesize mechanotransduction in intervertebral disc degeneration, summarize the relative maturity of current mechanistic understanding across compartments and signaling branches, and discuss the importance of systematic cross-compartment comparison alongside single-sensor, single-compartment studies.