BioMedical Engineering OnLine最新文献

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.

In the twenty-first century, increasing studies suggest that magnetic devices are employed for technical innovation and solving clinical bottleneck problems. Magnetic surgery technology uses specially designed magnetic medical instruments or equipment to transform the "non-contact" magnetic force into a particular force. This could play a specific function in clinical diagnosis and treatment, such as vascular anastomosis, tissue compression, instrument anchoring, surgical navigation, space expansion, and controlled tracing. According to different application methods and principles, magnetic surgery technology can be divided into the following five core categories: magnetic compression technique (MCT), magnetic anchor technology (MAT), magnetic navigation technology (MNT), magnetic levitation technology (MLT), and magnetic tracer technology (MTT). Some of these technologies present great superiority and tend to replace traditional approaches, which are widely used in multiple diseases, including digestive, gynecological, breast, and urinary. From this perspective of multidisciplinary, magnetism could be utilized in clinical practice well, and magnetic surgery has tremendous potential in clinical treatment. The application of magnetic surgery makes operation greatly simplified and the postoperative complications reduced. Meanwhile, the biological security of magnetic surgery is assessed, and a uniform standard needs to be established. Despite some challenges that still exist in the development of magnetic surgery, it is necessary to investigate and explore more novel technologies, which bring clinical benefits to patients.

Titanium alloy anastomotic staples are widely used in gastrointestinal surgery due to their ability to reduce operative time and standardize anastomotic construction. The developmental focus of these devices has evolved from ensuring basic mechanical reliability toward improving biocompatibility and, more recently, toward functionally engineering the staple-tissue interface to actively regulate healing outcomes. Current research efforts primarily target three interrelated challenges: enhancing tissue integration, preventing bacteria-associated anastomotic complications, and enabling controlled biodegradability, particularly in the context of malignant gastrointestinal disease. To address these challenges, a range of material modification strategies-including surface coatings, biochemical functionalization, physical micro/nano-topographical engineering, and biodegradable metal design-have been explored. In this review, we systematically survey studies published since 2010, identified through searches of major biomedical and materials science databases, with emphasis on experimental models directly relevant to gastrointestinal anastomosis. Rather than providing a descriptive catalog, this review critically analyzes how distinct modification strategies influence cellular responses, inflammatory regulation, and translational feasibility within the unique anastomotic microenvironment. Despite increasing research activity, an integrative framework linking mechanistic insights to clinical performance remains insufficiently developed. By synthesizing current evidence and highlighting unresolved biological and translational challenges, this review aims to clarify the functional design principles of next-generation anastomotic staples and to inform future development of clinically viable, bioactive surgical implants.

Background: The socket is an essential component of prostheses and critical to the mobility, quality of life, and independence of individuals with lower-limb loss. A poor-fitting socket can lead to residual limb health issues, gait abnormalities, and prosthesis abandonment. To mitigate these risks, prosthetists routinely evaluate socket fit, but current assessments rely largely on subjective measures, which may not consistently ensure optimal outcomes. The use of pressure sensors at the limb-socket interface could enhance these evaluations, yet challenges with practicality and wearability have limited their clinical adoption. To address these gaps, we developed and evaluated a prosthetic sock with integrated textile-based pressure-sensing cells.

Results: Individual cells were first loaded to evaluate sensitivity, repeatability, and drift. Then, a surrogate residual limb was fabricated from silicone and 3D-printed components to assess performance at the limb-socket interface. The sensitivity test demonstrated the ability to detect load changes equivalent to 5% of the maximum load throughout the full range, while the repeatability evaluation resulted in an intraclass correlation coefficient of 0.998 (0.996-1.000) and a percentage coefficient of variation (CV) below 2%. The average drift over 10 min was 3.03 ± 0.44%. When loading the residual limb in the neutral standing position, three out of four sock cells tested at the limb-socket interface showed percentage CVs below 10%. Finally, all cells tested detected changes in socket pressure distribution under simulated gait events when applying a load of 600 N.

Conclusions: These results were similar to commonly used off-the-shelf pressure sensors, and the sock performance is, therefore, promising. However, further development is required, including form factor improvements, implementation of shear-sensing capabilities, calibration, and validation with prosthesis users.

Background: Bone defects remain a major clinical challenge in orthopedic and reconstructive surgery. However, conventional inorganic ceramic biomaterials are limited by many problems, such as poor plasticity, limited osteogenic potential, and uncontrolled degradation kinetics, resulting in failing to keep pace with new bones' healing. Harmonizing biomaterial degradation with osteogenesis thus remains a crucial problem to be addressed in bone tissue engineering.

Methods: In this study, Mg²⁺-doped calcium sulfate whiskers (MCSW) are synthesized by adopting a high-temperature autoclave method and are fabricated into bone grafting via die-casting molding. Their physicochemical properties, including surface morphology, solution diffusion behavior, mechanical performance, elemental distribution, and Mg²⁺ release kinetics, are systematically characterized. Biocompatibility and in vitro osteoinductive potential are evaluated through assaying cell viability and analyzing osteogenic differentiation. Furthermore, the bone grafting is implanted into critical-sized calvarial defect models in Sprague-Dawley rats, and their bones' regenerative efficacy is assessed by micro-computed tomography (micro-CT) and histological staining.

Results: MCSW exhibits many characteristics, including a rough surface, solution-dependent degradation behavior, appropriate mechanical strength, as well as sustained and controlled Mg²⁺ release, presenting favorable physicochemical properties with tunable degradation, which shows it is suitable for applying to bone tissue engineering. In vitro studies demonstrate excellent biocompatibility, supporting the survival, proliferation, and osteogenic differentiation of BMSCs, as proved by enhanced alkaline phosphatase (ALP) activity and calcium mineral deposition. More importantly, the in vivo degradation rate of MCSW is well synchronized with osteogenesis, resulting in effective bone regeneration and satisfactory repair of critical-sized defects.

Conclusion: This Mg²⁺-doping strategy for calcium sulfate whiskers provides a novel and effective approach to enhance osteogenic activity and achieve tunable degradation in bone tissue engineering biomaterials, thereby showing strong potential for future clinical application.

Background: Recognizing knee hyperextension during gait in stroke patients is clinically challenging and involves cumbersome, costly procedures. This study proposes a simplified strategy based on a transformer-long-short-term memory (transformer-LSTM) approach for knee hyperextension recognition via surface electromyography (sEMG) data.

Aim: This study proposes an algorithmic model to streamline knee hyperextension screening in stroke patients and reduce associated screening costs.

Methods: Using an open-source gait database of 50 stroke patients, co-contraction index (CCI) values-derived from Biceps Femoris (BF)/Rectus Femoris (RF), Tibialis Anterior (TA)/Gastrocnemius (GAS), and their combinations-were input into the model. Performance was evaluated using accuracy, F1 scores, Receiver Operating Characteristic (ROC) curves, and loss function convergence.

Results: The transformer-LSTM achieved a recognition accuracy of 83.38% (F1: 0.8335), outperforming linear regression (36.78%, F1: 0.3405), support vector machines (38.65%, F1: 0.3185), convolutional neural networks (72.56%, F1: 0.7259), gated recurrent units (70.94%, F1: 0.7103), long-short-term memory (76.86%, F1: 0.7687), and transformer (75.76%, F1: 0.7678). The transformer-LSTM demonstrated the fastest loss function convergence and an ROC Area Under Curve of 0.99.

Conclusions: The transformer-LSTM provides superior recognition rates (83.38%) and offers a robust solution for screening knee hyperextension in stroke patients, highlighting its potential for clinical application.

Objectives: This study aims to evaluate the predictive value of CT target scan-based radiomics and clinical features in patients with chronic obstructive pulmonary disease (COPD) who also present with malignant pulmonary nodules.

Methods: A retrospective analysis was conducted on 104 patients diagnosed with COPD and pulmonary nodules, treated at our hospital between September 2022 and September 2024. The cohort was stratified into a benign group (n = 48) and a malignant group (n = 66) based on the definitive pathological examination results. All participants underwent spiral CT target scanning. Univariate and multivariate logistic regression analyses were employed to identify influencing factors. In addition, a receiver operating characteristic (ROC) curve was constructed to assess the predictive value.

Results: In comparison with the benign group, the malignant group exhibited a greater aspect ratio, a higher proportion of patients over 45 years of age, and increased occurrences of mixed ground-glass opacities, calcification, spiculation sign, vascular convergence sign, and lobulation sign, along with a significant elevation in the CAT score (P < 0.05). The CAT score, density, spiculation sign, and lobulation sign emerged as independent risk factors for the presence of malignant pulmonary nodules in patients with COPD (P < 0.05). The aforementioned imaging factors were incorporated into a multivariate logistic regression model: Logit (P) = 10.490 + 0.917 × CAT score + 1.547 × density + 0.823 × spiculation sign + 0.736 × lobulation sign. The area under the curve (AUC) values for the CAT score, radiomics model, and combined model were 0.768, 0.826, and 0.909, respectively.

Conclusions: Radiomics features derived from CT target scanning, when combined with clinical features, demonstrate promising potential for differentiating between benign and malignant pulmonary nodules in patients with COPD. This approach may indirectly reflect patients' pulmonary function and serve as a reference for the early diagnosis of COPD combined with lung cancer.