Annals of Biomedical Engineering最新文献

Purpose: Low back pain associated with whole-body vibration (WBV) exposure remains a significant health concern, yet the biomechanical mechanisms linking WBV to spinal loads are incompletely understood. Prior computational studies often relied on simplified assumptions, such as static muscle activation patterns and constrained lumbar joint rotations, limiting the fidelity of dynamic spinal load predictions. To address these gaps, this study aims to establish and validate a muscle-driven lumbar spine model that integrates nonlinear mechanical properties of intervertebral joints and an adaptive feedback control strategy.

Methods: A hybrid inverse-forward dynamics framework, integrated with a robust adaptive proportional-integral-derivative (PID)-based control algorithm providing closed-loop feedback tracking, dynamically allocated muscle excitations to stabilize lumbar posture under vertical vibration without artificial rotational constraints. The effects of muscle activations and vibration frequency on spinal biomechanical loads and biodynamic responses were also investigated.

Results: Validations against in vivo intradiscal pressure and erector spinae electromyography showed good agreement (r > 0.9). For biodynamic responses, seat-to-head transmissibility was used to set the pelvis-seat interface properties, and apparent mass was predicted with favorable agreement. A preliminary analysis of frequency effects revealed peak spinal loads near resonance. Active muscle control considerably altered resonance frequencies (4.5 Hz vs. 5 Hz in passive models) and reduced vibration transmissibility while increasing lumbar compressive loads at resonance, highlighting a critical trade-off between vibration mitigation and spinal biomechanical stress.

Conclusion: By addressing limitations in resolving dynamic muscle recruitment and joint-level loads, this work provides a validated framework for evaluating vibration-induced spinal biomechanics, offering insights into injury pathways and informing ergonomic interventions.

Purpose: The growing demand for functional tissues and organs has driven advances in tissue engineering, particularly through 3D bioprinting. However, the mechanical stress associated with extrusion can compromise cell viability, limiting its clinical applicability. This study aimed to evaluate the viability of mature osteoblast-like cells (SaOS-2) in alginate-based bioinks supplemented with different platelet concentrates, platelet-rich plasma (PRP), platelet-poor plasma (PPP), platelet-rich fibrin (PRF), and injectable PRF (iPRF) to identify formulations that enhance cell survival post-printing.

Methods: Bioinks composed of alginate and varying concentrations (10% and 20%) of platelet concentrates were prepared and characterized rheologically. SaOS-2 cells were embedded in the bioinks and printed using extrusion-based 3D bioprinting. Printed scaffolds were analyzed for cell viability using the LIVE/DEAD assay and confocal microscopy at 24, 48, and 72 hours post-printing.

Results: Rheological analysis confirmed the printability of constructs containing 10% PPP, 10% PRF, and 20% PRF. Cell viability exceeded 58% at 24 hours and 80% at 48 hours across all tested bioinks. Notably, PRF-containing constructs demonstrated viability recovery up to 86% at 72 hours, suggesting a protective and regenerative role.

Conclusion: PRF-enriched bioinks significantly improve cell viability after extrusion and enhance the physical integrity of bioprinted scaffolds. These results support the potential of PRF-based bioinks as promising candidates for clinically relevant bone tissue engineering applications.

Hypertension (HTN), despite contemporary endovascular repair, is a common and challenging complication of coarctation of the aorta (CoA), and its mechanisms and optimal management remain uncertain. Using computed tomography angiography (CTA), we present a feasibility workflow that integrates statistical shape analysis (SSA), computational hemodynamics, and machine learning (ML) to investigate predictors of HTN persistence after endovascular treatment. It builds on our randomized controlled trial comparing safety and efficacy of two types of aortic stents, in which all patients underwent a 3-year structural follow-up with blood pressure measurements, transthoracic echocardiography, and CTA. The current analysis includes twenty-nine patients with paired baseline and follow-up CTAs. Deep-learning segmentation was used to reconstruct patient-specific aortic geometries, from which statistical shape modes (SSMs) were derived. In addition, CFD-based hemodynamic indices were computed to characterize simulated flow patterns. These features were then evaluated using a stacking ensemble classifier and complementary nonparametric statistical testing to predict HTN at 3-year post-procedure. In four-fold cross-validation, model performance varied across folds, with accuracies ranging from 71.9 to 93.8% and area under the receiver-operating-characteristic curve (AUC-ROC) ranging from 0.74 to 0.95. Statistical analysis also identified several hemodynamic variables as candidate biomarkers associated with post-treatment HTN persistence. Overall, these results support the feasibility of combining SSA, computational hemodynamics, and ML to explore shape- and flow-related factors associated with post-repair HTN.

Purpose: Traditional hypertonia diagnosis relies on the Modified Ashworth Scale (MAS), which is subjective and dependent on doctors' experience. Although previous studies have explored the use of force sensors and surface electromyography (sEMG), finding a reliable and valid detection method remains a challenge. This study aims to develop a simple yet effective platform that integrates biomechanical and sEMG data for upper-limb muscle tone assessment, providing a more objective and quantitative evaluation approach.

Methods: A detection platform was developed to collect biomechanical and sEMG data from 59 subjects, including 49 patients (MAS Ⅰ = 21, MAS Ⅰ +  = 16, MAS Ⅱ = 12) and 10 healthy individuals, at different movement speeds (15°/s, 20°/s, and 25°/s). The acquired data underwent feature extraction, including signal processing and statistical analysis. Dimensionality reduction was applied to optimize the extracted features, and these features were then integrated into a classification algorithm for further analysis.

Results: The extracted features effectively distinguished patients from healthy individuals, with statistically significant differences (p < 0.01). Furthermore, the strong correlation between the extracted features and MAS scores (p < 0.01) confirmed the reliability of the proposed method. Finally, the classification algorithm demonstrated high consistency with clinical evaluations, validating its potential for clinical application in muscle tone assessment.

Conclusion: This study introduces an objective and quantitative method for assessing muscle tone, shifting away from the traditional subjective MAS evaluation. By enhancing diagnostic accuracy, the proposed approach provides a more reliable basis for hypertonia diagnosis and treatment. The findings hold significant promise for optimizing clinical decision-making, ultimately improving patient management and therapeutic strategies.

Magnesium (Mg) biodegradable implants are emerging as a new generation of implantable materials due to their excellent biocompatibility, mechanical properties similar to bone, and the potential to release bioactive byproducts like magnesium ions (Mg²⁺) and hydrogen gas (H₂). This review article investigates the synergistic effects of these two corrosion products on bone and vascular tissue regeneration, immune modulation, and the reduction of oxidative stress. Under controlled conditions, H₂ demonstrates anti-inflammatory effects by inhibiting the NF-κB pathway and activating Keap1-Nrf2. Concurrently, Mg²⁺ activates the Wnt and TRPM7 pathways to stimulate osteogenesis and angiogenesis. However, excessive release of these compounds can lead to detrimental effects. The article further addresses the challenges in modeling, clinical translation, and real-time monitoring. It also proposes future research directions, including reactive design, implantable sensors, and trials in high-risk populations. This comprehensive review provides a foundation for developing smart and personalized implants for tissue regeneration.

Purpose: Detecting vulnerable coronary plaques through coronary computed tomography angiography (CCTA) is a crucial, yet challenging task. To date, most of the proposed vulnerability markers have been studied in isolation. This study introduces the first integrated analysis combining radiomic, mechanical, and hemodynamic factors to explore their synergistic contribution to plaque vulnerability.

Methods: The study analyzed 161 plaques in 46 coronary arteries from 39 patients, with 7 arteries (28 plaques) from 7 individuals, labeled as vulnerable from intravascular imaging. First, CCTA radiomic features were extracted. Second, mechanical markers were computed through finite element simulations conducted with varying material characteristics, accounting for the arterial wall mechanical properties' uncertainties. Third, hemodynamic markers were derived from transient computational fluid dynamics simulations. Finally, a machine learning pipeline was developed to classify coronary arteries and patients based on radiomic, mechanical, and hemodynamic features, both individually and in combination.

Results: Radiomics achieved the highest sensitivity (1.00), with all vulnerable patients identified, but lower specificity (0.69). Differently, mechanics and hemodynamics achieved higher specificities (0.94 and 0.97, respectively) but lower sensitivities (both 0.86). By integrating at least two out of the three models, the predictive performance improved, up to sensitivity = 1.00 and specificity = 0.97, with only one misclassified case.

Conclusion: Although based on only 39 patients, the results highlight the power of a multi-level integrative approach for coronary plaque assessment. The study revealed that (i) hemodynamics outperformed mechanics and radiomics; (ii) while radiomics maximized sensitivity, mechanics and hemodynamics prioritized specificity, and (iii) integrating at least two variable types added value.

Purpose: Sickle cell disease (SCD) is a hereditary blood disorder that increases stroke risk in children. Previously, we showed that SCD caused accelerated arteriopathy in a sickle cell transgenic mouse model compared to sickle cell trait controls. We sought to determine whether radiomics analysis of label-free carotid artery magnetic resonance angiography (MRA) can differentiate between SCD mice (SS) from heterozygous sickle cell trait control mice (AS). Radiomics analysis of MRI data was used to extract quantitative imaging features, then tested for discrimination between SS and AS mice.

Methods: MRA scans of Townes sickle cell transgenic mice at one and three months of age were completed. 112 radiomic features extracted from segmented carotid artery images using PyRadiomics software were used to develop models predictive of SCD genotypes.

Results: At one month of age, four radiomics features yielded accuracy of 74%. At three months, a single feature achieved 76% accuracy. Analysis of MRA-derived arterial morphology confirmed that incorrectly identified mice carotid arteries resembled the incorrectly predicted genotype: larger luminal areas for AS and smaller luminal areas for SS, reflecting how biological variability of SCD impacted radiomic feature predictions.

Conclusion: This study demonstrates feasibility of radiomics in discriminating arterial features between SCD and control mice, and supports radiomics as a non-invasive imaging analytical approach to characterize arterial remodeling in preclinical SCD models, motivating future translational studies linking imaging features to vascular pathology.

This paper describes the development, fabrication and testing of Playcuff, a wearable device designed to act as a videogame controller for children with motor disabilities, which also provides an orthotic action to improve the control of the upper limb. The aim of this device is to empower children with motor impairment and enable them to access and enjoy gaming despite their disabilities. The videogame controller function was achieved through on-board gesture classification using a two-tiered Fine Tree machine learning algorithm integrated into the device's firmware. Based on features extracted from two inertial sensors present on the device, the classifier was trained to identify in real time 22 classes representing different postures and movements of forearm and wrist, showing an accuracy higher than 94%. A cohort of children (n = 19, aged 9.01 ± 1.95 years old) with neuromotor impairment involving the upper limb were enrolled to test the device. The acceptability and effectiveness of the device were evaluated through a specific questionnaire: the resulting answers were heavily skewed towards appreciation (80.5%) rather than criticism. The methods of classification were found to be simple and effective in controlling the game. In conclusion, Playcuff was shown to be a versatile and well-received orthotic controller, which could be used in future also for videogame-based rehabilitation.

Purpose: Upper-limb motor impairments are common and affect quality of life. Shared-control robotic assistance systems driven by patients' residual effort generation capacities are being developed. These systems require the integration, into their control scheme, of knowledge about the maximum voluntary torque achievable by patients at each joint angle. This study aimed to quantify the performance of simplified mathematical models primarily developed to fit healthy individuals' torque-angle curves, as candidates for integration in the control scheme.

Methods: 15 patients (6F, 9M, 62.7 ± 14.4 years) hospitalized in a French rehabilitation center for stroke (n = 10), multiple sclerosis (n = 4), or traumatic tetraplegia (n = 1) were included. Passive, then maximum concentric and eccentric torques were measured in the seated position, in shoulder external-internal rotation and in elbow flexion-extension, at an imposed speed of 30°/s, using a Con-Trex® isokinetic ergometer.

Results: The normalized RMSE between modeled and experimental curves was 3.1 ± 2.0% of corresponding peak torques. Univariate linear models displayed no difference in nRMSE between mathematical models, but differences across patients (p < 0.001, R² = 0.38). The distance between modeled and experimental curves was continuously lower than 10% of the peak torque over 92 ± 13% of the experimental range of motion.

Conclusion: Regardless of the mathematical model used, torque-angle curve modeling was globally less effective than that for healthy individuals, while still allowing consideration for future use for robotic assistance for the majority of patients. Further investigation of patient-related factors affecting model quality will be necessary to assess results' generalizability.

Trial registration number: ID-RCB: 2024-A01007-40, clinicaltrial.gov ID: NCT06608121.