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Various nutritional supplements are available over the counter, yet few have been investigated in randomized controlled trials. The rationale for using the specific mix of nutritional substances including collagen type II, hyaluronic acid, n-acetyl-glucosamine, bamboo extract, L-lysine, and vitamin C is the assumption that combining naturally occurring ingredients of the intervertebral disc would maintain spine function. This double-blinded, placebo-controlled randomized trial aimed to evaluate the efficacy of a nutraceutical supplement mix in the management of lumbar osteochondrosis. Fifty patients were randomly assigned to either the supplement or placebo group in a 1:1 ratio. Patient-Reported Outcome Measures (PROMs) included the Oswestry Disability Index (ODI), the visual analogue scale for pain (pVAS), short form-12 (SF-12) physical and mental component summary subscale scores (PCS and MCS, respectively), and global physical activity questionnaire (GPAQ). Magnetic resonance imaging (MRI) was used to evaluate degenerative changes of intervertebral discs (IVD) including Pfirrmann grades as well as three-dimensional (3D) volume measurements. Data were collected at baseline and after the 3-month intervention. None of the PROMs were significantly different between the supplement and placebo groups. Disc degeneration according to Pfirrmann classifications remained stable during the 3-month intervention in both groups. Despite no significance regarding the distribution of Pfirrmann grade changes (improvement, no change, worsening; p = 0.259), in the supplement group, one patient achieved a three-grade improvement, and worsening of Pfirrmann grades were only detected in the placebo group (9.1%). Furthermore, in-depth evaluations of MRIs showed significantly higher 3D-measured volume changes (increase) in the supplement (+740.3 ± 796.1 mm3) compared to lower 3D-measured volume changes (decrease) in the placebo group (−417.2 ± 875.0 mm3; p < 0.001). In conclusion, this multi-nutrient supplement might not only stabilize the progression of lumbar osteochondrosis, but it might also potentially even increase IVD volumes as detected on MRIs. [ABSTRACT FROM AUTHOR]

Background and Objective: Spinal cord injuries can have a severe impact on athletes' or patients' lives. High axial impact scenarios like tackling and scrummaging can cause hyperflexion and buckling of the cervical spine, which is often connected with bilateral facet dislocation. Typically, finite-element (FE) or musculoskeletal models are applied to investigate these scenarios, however, they have the drawbacks of high computational cost and lack of soft tissue information, respectively. Moreover, material properties of the involved tissues are commonly tested in quasi-static conditions, which do not accurately capture the mechanical behavior during impact scenarios. Thus, the aim of this study was to develop, calibrate and validate an approach for the creation of impact-specific hybrid, rigid body - finite-element spine models for high-dynamic axial impact scenarios., Methods: Five porcine cervical spine models were used to replicate in-vitro experiments to calibrate stiffness and damping parameters of the intervertebral joints by matching the kinematics of the in-vitro with the in-silico experiments. Afterwards, a five-fold cross-validation was conducted. Additionally, the von Mises stress of the lumped FE-discs was investigated during impact., Results: The results of the calibration and validation of our hybrid approach agree well with the in-vitro experiments. The stress maps of the lumped FE-discs showed that the highest stress of the most superior lumped disc was located anterior while the remaining lumped discs had their maximum in the posterior portion., Conclusion: Our hybrid method demonstrated the importance of impact-specific modeling. Overall, our hybrid modeling approach enhances the possibilities of identifying spine injury mechanisms by facilitating dynamic, impact-specific computational models., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Clarifying multifactorial musculoskeletal disorder etiologies supports risk analysis, development of targeted prevention, and treatment modalities. Deep learning enables comprehensive risk factor identification through systematic analyses of disease data sets but does not provide sufficient context for mechanistic understanding, limiting clinical applicability for etiological investigations. Conversely, multiscale biomechanical modeling can evaluate mechanistic etiology within the relevant biomechanical and physiological context. We propose a hybrid approach combining 3D explainable deep learning and multiscale biomechanical modeling; we applied this approach to investigate temporomandibular joint (TMJ) disorder etiology by systematically identifying risk factors and elucidating mechanistic relationships between risk factors and TMJ biomechanics and mechanobiology. Our 3D convolutional neural network recognized TMJ disorder patients through participant-specific morphological features in condylar, ramus, and chin. Driven by deep learning model outputs, biomechanical modeling revealed that small mandibular size and flat condylar shape were associated with increased TMJ disorder risk through increased joint force, decreased tissue nutrient availability and cell ATP production, and increased TMJ disc strain energy density. Combining explainable deep learning and multiscale biomechanical modeling addresses the "mechanism unknown" limitation undermining translational confidence in clinical applications of deep learning and increases methodological accessibility for smaller clinical data sets by providing the crucial biomechanical context.