Background: The use of lower limb exoskeletons in clinical rehabilitation has expanded in recent years, offering potential benefits for walking recovery. However, current clinical evidence on their effectiveness remains inconclusive. Additionally, the way individuals adapt to these robotic devices and how this adaptation contributes to functional improvements is not yet fully understood. This study was intended to (1) investigate the safety and feasibility of the Fourier X2 exoskeleton for walking rehabilitation and (2) examine its effect on walking function following a rehabilitation program.
Methods: A randomized controlled trial was undertaken with 46 individuals who had suffered a spinal cord injury (SCI) within the last year. Participants were randomly allocated into two groups: an intervention group (IG), which received gait training using the Fourier X2 exoskeleton, and a control group (CG), which underwent conventional gait training. Each participant completed 20 gait training sessions lasting one hour. The neurological impairment ranged from C2 to L4, with participants classified under the American Spinal Injury Association Impairment Scale (AIS) C or D. The treatment program involved 20 gait training sessions, each lasting one hour, utilizing the Fourier X2 exoskeleton. Safety was assessed by tracking adverse events, while pain and fatigue levels were measured using the Visual Analogue Scale (VAS). Functional outcomes were evaluated through the Lower Extremity Motor Score (LEMS), Walking Index for Spinal Cord Injury II (WISCI-II), Spinal Cord Independence Measure III (SCIM-III), and various walking assessments, including the 10-Meter Walk Test (10MWT), 6-Minute Walk Test (6MWT), and Timed Up and Go (TUG).
Results: No major complications were observed during the study. Participants in the intervention group (IG) reported experiencing mild pain (1.7 cm, SD 1.1) and moderate fatigue (3.3 cm, SD 1.6) as measured by the Visual Analogue Scale (VAS, 0-10 cm range). Statistical analysis of WISCI-II scores showed notable progress in both the "group" effect (F = 17.82, p < 0.001) and the "group-time" interaction (F = 7.27, p = 0.03). Further post-hoc evaluations revealed that the IG achieved a significant improvement of 3.20 points (SD 2.16, p < 0.0001), whereas the control group (CG) demonstrated a minor, non-significant increase of 0.5 points (SD 1.31, p = 0.32). No other variables showed significant differences between the two groups.
Conclusions: The Fourier X2 exoskeleton is both safe and well-tolerated in clinical environments. Participants who received training with the device exhibited enhanced walking independence, as reflected in their WISCI-II scores.
Trial registration: The Chinese Clinical Trial Register (ChiCTR) includes this study under registration number ChiCTR2000041186, dated December 21, 2020.
Background and objective: The application of computational fluid dynamics (CFD) in the cardiovascular field has been increasingly observed to analyze hemodynamic conditions within intravascular lumens. This study aims to quantitatively elucidate the development of CFD technology on hemodynamics over the past two decades through literature retrieval and bibliometric analysis.
Methods: The literature retrieval is conducted using the Web of Science database, where all academic articles concerning hemodynamic analysis using CFD technology in the past 20 years are included. The retrieval strategy was primarily based on three aspects: time, cardiovascular anatomical parts, and cardiovascular diseases.
Results: Over the past two decades, the publication of CFD-focused articles in the cardiovascular field has grown steadily at an average annual rate of 10.19%, with a stable distribution across anatomical parts. A similar overall trend is observed for research on cardiovascular diseases (11.89% annual growth). However, in recent years, the growth rates for publications on individual diseases have begun to diverge significantly.
Conclusions: The quantitative evidence from literature retrieval and bibliometric analysis shows the continuous development of CFD technology in the cardiovascular field over the past two decades. The consistent distribution of research across different cardiovascular anatomical parts suggests a balanced development process. However, the development of CFD technology on specific cardiovascular diseases might perform distinctively in the coming years.
Objectives: Diabetic foot ulcer management relies predominantly on reactive treatment adjustments based on current wound status. This study developed an accessible machine learning framework using routinely collected clinical metadata (no imaging required) to predict healing phase transitions at the next clinical appointment, enabling proactive treatment planning with an integrated recommendation system.
Methods: Longitudinal data from 268 patients with 329 distinct ulcers across 890 appointments were analyzed. Features (n 103) including temporal measurements normalized by inter-appointment intervals were engineered. An Extra Trees classifier was optimized via Bayesian hyperparameter tuning with impurity-based feature selection and sequential augmentation to predict three transition categories: favorable, acceptable, or unfavorable. Threefold patient-level cross-validation ensured robust performance estimation.
Results: Feature selection identified 30 essential predictors, achieving 70.9% dimensionality reduction. The optimized classifier demonstrated 78% ± 4% accuracy with balanced category performance (per-class F1 scores: 0.72-0.84) and average AUC of 0.90. Historical phase features dominated predictive importance. The integrated treatment recommendation system achieved 88.7% within-category agreement for offloading prescriptions across all chronicity levels. Dressing recommendations demonstrated chronicity-stratified performance, with match rates declining from 83.7% for acute wounds to 5.6% for very chronic wounds, appropriately reflecting clinical reality that treatment-resistant wounds require individualized therapeutic experimentation.
Conclusions: This framework demonstrates potential for next-appointment trajectory prediction using accessible clinical metadata without specialized imaging, pending prospective validation. The chronicity-dependent recommendation performance appropriately distinguishes wounds amenable to standardized protocols from treatment-resistant cases requiring iterative experimentation.
3D bioprinting is a revolutionary technology that has recently emerged in the area of tissue regeneration owing to its ability to create complex tissue and organs for replacement. The requirement of various tissue types to offer patient-specific treatments is challenging; bioprinting uses a specialized material called 'bioink', which helps to address the issue. MXene, a well-known two-dimensional nanomaterial, has been gaining interest recently. It has been identified as a promising candidate in the field of tissue engineering because of its unique combination of different properties, such as biocompatibility, mechanical strength, and electrical conductivity. These are essential properties for the development of the next-generation bioinks. In this review, we report a comprehensive analysis of the latest advances in MXene-based bioinks in 3D bioprinting over conventional tissue scaffolding, focused on the materials' properties and their role in tissue regeneration. We highlight the ability of MXene in bioink, where MXene has the capacity to enhance cell growth by providing a conducive microenvironment for electrically active tissue, additionally supporting the 3D construct for stability. MXene in bioinks is advancing toward the field of tissue engineering for its application in therapeutic applications.
Background: Cardiogenic oscillations in airflow can cause ventilator autotriggering during pressure support ventilation, potentially leading to inappropriate hyperventilation. A method to attenuate these oscillations in real time may help reduce autotriggering.
Materials and methods: High-resolution airflow and ECG signals were collected from intubated surgical patients receiving pressure support ventilation. Singular spectrum analysis (SSA) was applied in a sliding-window format to generate a smoothed respiratory waveform. We quantified attenuation of cardiogenic oscillations using ECG-aligned timing, frequency-domain analysis, and reduction in cardiac-frequency spectral power. Waveform fidelity was assessed using respiratory-envelope correlation and root-mean-square error (RMSE). Computational feasibility was evaluated by measuring processing time per window.
Results: SSA substantially reduced cardiac-frequency spectral power (82-87% reduction) while preserving respiratory structure (correlation with respiratory envelope 0.92-0.94). Reconstruction error was modest (RMSE 0.08-0.11 normalized units). Computation time per 6-s window was 14-22 ms, supporting potential real-time use. Attenuation performance remained stable during changes in respiratory rate.
Conclusions: Sliding-window SSA attenuated cardiogenic oscillations in patient airflow signals and preserved the dominant respiratory pattern. As a proof-of-concept, this approach shows potential for integration into autotrigger-suppression logic, though further validation in larger and more diverse populations is required.
Background: Generalized joint hypermobility (GJH) is often challenging to assess, but its presence could suggest a syndromic diagnosis of Ehlers-Danlos Syndromes (EDS).
Objective: An automated and objective method for estimating joint hypermobility with Beighton score using short video clips is proposed.
Method: A total of 225 adults (91.8% female, median age 32.0, range 18-64) referred to a specialized EDS clinic were recruited for this study. A video-based method relying on pose-estimation libraries was developed to predict per-joint hypermobility of both elbows, knees, fifth fingers, thumbs, and spine; as well as the overall Beighton score. The system was developed on the first 100 individuals (training set), and validated on the remaining 125 individuals (test set).
Results: The system screened out 31.9% of the training set and 32.0% of the test set as not having GJH, while recalling 89.1% and 91.9% of the true positives on the train and test set, respectively. The consistency of the system between the training and test sets suggests that it generalizes well to unseen individuals. The system was tuned to be with a focus on sensitivity to avoid screening out individuals with GJH. As such, the specificity of the system is 52.1% on the training set and 42.4% on the test set.
Conclusion: The proposed system can objectively screen individuals for possible GJH and also screen out those without GJH during the referral process, reducing the burden on specialized EDS clinics while providing early diagnostic triage. Future research will focus on deploying the tool as a mobile application.
Background: Post-traumatic knee replacement (PTKR) is frequently complicated by the presence of retained metallic hardware around the joint, which limits the use of intramedullary alignment guides. Consequently, extramedullary jigs are often required, although they may increase radiation exposure and reduce alignment precision. Patient-specific guides (PSGs), generated from medical imaging and produced via 3D printing, offer a potential alternative for improving accuracy in complex surgical scenarios. This study aimed to assess the accuracy of PSGs in PTKR using in-vitro knee models with and without retained hardware.
Methods: CT images of arthritic knees were used to generate 3D-printed anatomical models. Metallic plates and screws were subsequently mounted to replicate typical post-traumatic hardware configurations. These phantoms underwent CT scanning for virtual surgical planning, and patient-specific guides (PSGs) were designed based on the reconstructed preoperative models. In-vitro distal femoral and proximal tibial resections were then performed by a surgeon using the corresponding PSGs. After the simulated procedures, all phantoms were re-scanned to quantify PSG positioning accuracy and resection angles.
Results: Knee phantoms with hardware exhibited shape deviations 17-18.5 times greater than those without hardware (p < 0.05). PSG positioning errors averaged 0.68 mm and 2.83° in hardware models, compared to 0.55 mm and 1.32° in non-hardware models. Resection angle errors in hardware phantoms ranged from 2.4° to 3.1°, significantly higher than in the non-hardware group.
Conclusions: Based on the in-vitro experimental findings, PSGs allow PTKR to be performed without the removal of retained hardware while achieving accuracy that exceeds that of traditional extramedullary alignment techniques. Although hardware presence results in a quantifiable reduction in accuracy, PSGs continue to demonstrate improved alignment precision and contribute to enhanced workflow efficiency in the context of complex PTKR.
Background: This study aimed to analyze the risk factors and predicted model for clinical relapse after discontinuation of antiviral therapy in patients with chronic hepatitis B (CHB).
Methods: A retrospective analysis was conducted on the clinical data of 99 CHB patients who met the discontinuation criteria and were treated at Southern Central Hospital of Yunnan Province (The First People's Hospital of Honghe State) from March 2020 to December 2022. All subjects received nucleos(t)ide analogs (NAs) or interferon-based antiviral therapy and discontinued treatment once they met the cessation criteria, followed by a 2-year follow-up. Based on relapse status, patients were divided into a relapse group and a non-relapse group. Clinical characteristics were compared between the two groups. A multivariate logistic regression analysis was performed to analyze the independent risk factors for clinical relapse within 2 years after treatment cessation.
Results: During the 2-year follow-up, 45 patients (45.45%) experienced clinical relapse after discontinuation. Compared with the non-relapse group, the relapse group exhibited significantly higher age, HBsAg levels at treatment cessation, and HBV DNA load at discontinuation (p < 0.05), as well as a shorter total duration of antiviral therapy (p < 0.05). Multivariate analysis revealed that age, total antiviral treatment duration, HBV DNA load at discontinuation, and HBsAg levels at cessation were independent risk factors for clinical relapse of CHB patients (p < 0.05). A combined predictive model was constructed based on multivariate logistic regression coefficients: combined model = -17.497 + 0.181 × age + (-0.123) × total antiviral duration + 1.746 × HBV DNA at discontinuation + 0.032 × HBsAg at discontinuation. ROC analysis demonstrated that the AUC of the combined model was 0.945 (95% CI: 0.902-0.987) for predicting 2-year clinical relapse, with a sensitivity of 91.11% and specificity of 83.33%. Spearman correlation analysis indicated that patient age and HBV DNA load at discontinuation were negatively correlated with time to relapse (p < 0.05), whereas HBsAg levels showed no significant correlation with total antiviral duration (p > 0.05).
Conclusions: Age, HBV DNA load at discontinuation, HBsAg quantification at discontinuation, and the total antiviral duration were identified as key factors influencing clinical relapse after cessation of antiviral therapy in patients with CHB. A predictive model incorporating these factors demonstrated good clinical predictive value.
Background: Artificial intelligence (AI) techniques are increasingly applied to magnetic resonance imaging (MRI) for detecting temporomandibular joint (TMJ) anomalies; however, their overall diagnostic accuracy and generalizability remain uncertain.
Objectives: To systematically review and meta-analyse the diagnostic performance of AI models for TMJ anomaly detection on MRI and to identify factors influencing model performance.
Methods: A comprehensive search of PubMed, Scopus, Embase, and Web of Science was conducted for studies published between January 2015 and September 2025. Two reviewers independently screened and extracted data. Eligible studies developed and tested AI, machine learning, or deep learning models on human TMJ MRI and reported quantitative performance metrics. Risk of bias was assessed using the QUADAS-2 tool. Pooled sensitivity and specificity were estimated using a bivariate random-effects model, while pooled accuracy was derived using logit transformation. Heterogeneity (I2) was explored through subgroup analyses by model architecture and validation strategy.
Results: Fourteen studies were included in the systematic review, of which six met the criteria for meta-analysis. Across these six studies, 18 models were analyzed for accuracy, 29 for sensitivity, and 24 for specificity. The pooled diagnostic accuracy was 0.487 (95% CI 0.403-0.571), with pooled sensitivity and specificity of 0.399 (95% CI 0.348-0.450) and 0.399 (95% CI 0.343-0.456), respectively, all showing substantial heterogeneity (I2 > 90%). Subgroup analyses indicated that advanced architectures such as ResNet-18, Inception v3, and EfficientNet-b4 achieved higher and more consistent diagnostic performance.
Conclusions: Advanced deep learning architectures such as ResNet-18, Inception v3, and EfficientNet-b4 demonstrated superior diagnostic performance for detecting temporomandibular joint anomalies on MRI. These findings highlight the potential of AI-assisted MRI interpretation to improve diagnostic consistency, efficiency, and early detection of TMJ pathology. However, substantial heterogeneity and limited external validation currently limit clinical translation. Standardized multicenter studies and transparent model validation are essential to ensure reliable integration of AI tools into clinical TMJ imaging workflows.
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