Bioengineering最新文献

In recent years, we assisted the exploitation of Artificial Intelligence (AI) that invasively pervades in several instances of everyday life. The potential of this technology promises the automation of human tasks increasing accuracy and efficiency. The integration of AI systems in the orthopaedic field is becoming more and more a concrete reality, so this topic is gaining increasing interest by the scientific community. More and more authors are testing the power of AI in orthopaedics, exploiting the application in routine workflow, and asking if AI could improve clinical and surgical practice. In this brief narrative review, the state-of-art of AI in hip district orthopaedics is presented, particularly focusing on the application of AI tools in the context of radiological images, early diagnosis, clinical datasets, and around operative theatre. Possible future development of AI-hip pathology management is exposed too, and clear doubts about exploits of these tools in clinical practice are also exposed.

Capacitive ECG (cECG) technology offers significant potential for improving comfort and unobtrusiveness in long-term cardiovascular monitoring. Nevertheless, current research predominantly emphasizes basic heart rate monitoring by detecting only the R-wave, thereby restricting its clinical applicability. In this study, we proposed an advanced cECG mattress system and conducted a systematic evaluation. To enhance user comfort and achieve more accurate cECG morphological features, we developed a multi-layered cECG mattress incorporating flexible fabric active electrodes, signal acquisition circuits, and specialized signal processing algorithms. We conducted experimental validation to evaluate the performance of the proposed system. The system exhibited robust performance across various sleeping positions (supine, right lateral, left lateral and prone), achieving a high average true positive rate (TPR) of 0.99, ensuring reliable waveform detection. The mean absolute error (MAE) remains low at 1.12 ms for the R wave, 7.89 ms for the P wave, and 7.88 ms for the T wave, indicating accurate morphological feature extraction. Additionally, the system maintains a low MAE of 0.89 ms for the RR interval, 7.77 ms for the PR interval, and 7.85 ms for the RT interval, further underscoring its reliability in interval measurements. Compared with medical-grade devices, the signal quality obtained by the cECG mattress system is sufficient to accurately identify the crucial waveform morphology and interval durations. Moreover, the user experience evaluation and durability test demonstrated that the mattress system performed reliably and comfortably. This study provides essential information and establishes a foundation for the clinical application of cECG technology in future sleep monitoring research.

Background: Laryngeal and hypopharyngeal cancers (LHCs) exhibit heterogeneous outcomes after definitive radiotherapy (RT). Large language models (LLMs) may enhance prognostic stratification by integrating complex clinical and imaging data. This study validated two pre-trained LLMs-GPT-4o-2024-08-06 and Gemma-2-27b-it-for outcome prediction in LHC. Methods: Ninety-two patients with non-metastatic LHC treated with definitive (chemo)radiotherapy at Linkou Chang Gung Memorial Hospital (2006-2013) were retrospectively analyzed. First-order and 3D radiomic features were extracted from intra- and peritumoral regions on pre- and mid-RT CT scans. LLMs were prompted with clinical variables, radiotherapy notes, and radiomic features to classify patients as high- or low-risk for death, recurrence, and distant metastasis. Model performance was assessed using sensitivity, specificity, AUC, Kaplan-Meier survival analysis, and McNemar tests. Results: Integration of radiomic features significantly improved prognostic discrimination over clinical/RT plan data alone for both LLMs. For death prediction, pre-RT radiomics were the most predictive: GPT-4o achieved a peak AUC of 0.730 using intratumoral features, while Gemma-2-27b reached 0.736 using peritumoral features. For recurrence prediction, mid-RT peritumoral features yielded optimal performance (AUC = 0.703 for GPT-4o; AUC = 0.709 for Gemma-2-27b). Kaplan-Meier analyses confirmed statistically significant separation of risk groups: pre-RT intra- and peritumoral features for overall survival (for both GPT-4o and Gemma-2-27b, p < 0.05), and mid-RT peritumoral features for recurrence-free survival (p = 0.028 for GPT-4o; p = 0.017 for Gemma-2-27b). McNemar tests revealed no significant performance difference between the two LLMs when augmented with radiomics (all p > 0.05), indicating that the open-source model achieved comparable accuracy to its proprietary counterpart. Both models generated clinically coherent, patient-specific rationales explaining risk assignments, enhancing interpretability and clinical trust. Conclusions: This external validation demonstrates that pre-trained LLMs can serve as accurate, interpretable, and multimodal prognostic engines for LHC. Pre-RT radiomic features are critical for predicting mortality and metastasis, while mid-RT peritumoral features uniquely inform recurrence risk. The comparable performance of the open-source Gemma-2-27b-it model suggests a scalable, cost-effective, and privacy-preserving pathway for the integration of LLM-based tools into precision radiation oncology workflows to enhance risk stratification and therapeutic personalization.

Achilles tendon injuries are among the most frequent and debilitating musculoskeletal conditions, often resulting in incomplete healing and functional deficits. Conventional repair techniques primarily restore structural continuity but rarely achieve full biomechanical or histological regeneration. Recent advances in tissue engineering have introduced innovative strategies combining biomimetic scaffolds, cellular therapy, growth factors, and mechanical loading to promote regenerative rather than fibrotic repair. This review summarises the current understanding of Achilles tendon biology and healing mechanisms, with a focus on the integration of stem cell technologies, scaffold design, and mechanobiological conditioning. Various scaffold systems, including natural, synthetic, hybrid, and hydrogel-based constructs, are evaluated for their biocompatibility, mechanical performance, and tenoinductive potential. Preclinical studies demonstrate that mesenchymal stem cell (MSC)-loaded scaffolds exhibit significantly enhanced biomechanical outcomes in tendon defect models, including improved tensile strength, organized collagen I deposition and aligned fibre architecture in repaired constructs. While preclinical results are promising, clinical translation remains limited by regulatory, economic, and methodological challenges. Future research should prioritise standardised protocols, long-term functional outcomes, and interdisciplinary collaboration.

Artificial intelligence (AI) is rapidly transforming cardiovascular medicine, with profound implications for congenital heart disease (CHD). Tetralogy of Fallot (ToF), the most common cyanotic disease, requires lifelong surveillance and complex management because of late complications such as pulmonary regurgitation, arrhythmias, and right ventricular dysfunction. This review synthesizes current evidence on AI applications across the continuum of ToF care-from prenatal diagnosis to adulthood follow-up. We examine advances in imaging, perioperative planning, intraoperative monitoring, intensive care, and long-term surveillance, including wearable and implantable technologies. Machine learning (ML), deep learning (DL), and natural language processing (NLP) are revolutionizing diagnostic accuracy, risk stratification, surgical decision-making, and personalized long-term care. The future lies in the integration of multimodal data, including imaging, electronic health records (EHRs), genomic information, and continuous monitoring, to support precision medicine. Challenges remain regarding dataset limitations, interpretability, regulatory standards, and ethical concerns. Nevertheless, ongoing innovation and collaboration between clinicians, engineers, and regulators promise a new era in congenital cardiology. By embedding AI throughout the patient journey, healthcare systems may improve outcomes and quality of life for individuals with ToF.

Traditional drug delivery methods for gastrointestinal diseases, including oral and systemic administration, often suffer from degradation, inadequate mucosal absorption, and off-target toxicity. Consequently, these methods result in low bioavailability and suboptimal therapeutic outcomes for localized conditions such as inflammation and early-stage cancer. This review examines the innovative integration of advanced bioengineering platforms with therapeutic gastrointestinal endoscopy to address these delivery challenges. We concentrate on three principal bioengineered platforms: (1) nanoparticle systems (e.g., lipid, polymeric, and inorganic nanoparticles) designed for localized chemotherapy and theranostics; (2) in situ-forming hydrogels that serve as intelligent wound management materials and sustained drug depots; and (3) drug-eluting and biodegradable stents that convert passive luminal scaffolds into active, long-term drug-releasing devices. An analysis of these platforms demonstrates that their synergy with endoscopy facilitates precise, minimally invasive, and sustained local therapy, potentially transforming the treatment landscape for gastrointestinal diseases such as cancer and inflammatory bowel disease. Additionally, we investigate advanced strategies, including active targeting and stimulus-responsive release mechanisms, to enhance spatial precision. Despite promising preclinical advancements, clinical translation encounters challenges related to long-term biocompatibility, scalable manufacturing, regulatory pathways for drug-device combinations, and cost-effectiveness. Ultimately, the convergence of bioengineering and endoscopy presents significant potential to usher in a new era of precise, localized, and sustained micro-invasive treatments in gastroenterology.

Osteosarcoma (OS) remains the most common primary malignant bone tumor in adolescents, with conventional treatments yielding only modest improvements in long-term survival. Immunotherapy has emerged as a promising strategy to overcome these limitations. B7-H3 (CD276) stands apart from other potential targets due to its high expression in tumors cells, as well as its strong association with tumor aggressiveness and poor prognosis. This review provides a comprehensive overview of B7-H3, covering its molecular structure, regulatory mechanisms, biological functions, and expression patterns in tumor tissues. We emphasize the dual roles of B7-H3-both immunoregulatory and non-immunoregulatory-in shaping the tumor microenvironment (TME) and facilitating immune evasion. Building on these insights, we summarize current immunotherapeutic strategies targeting B7-H3 in OS, including monoclonal antibodies (mAbs), chimeric antigen receptor T cells (CAR-T), antibody-drug conjugates (ADCs), and bispecific antibodies (bsAbs). These four strategies have their own advantages and deficiencies. Excitingly, rapid advances in nanoparticle-based systems offer promising solutions to overcome the limitations, especially to develop more effective drug delivery systems and to reshape the TME by targeting immune cells. Despite promising progress, significant challenges remain. These include the absence of an identified B7-H3 receptor, the immunosuppressive and heterogeneous nature of the OS TME, and the need for improved targeting specificity and safety. Addressing these challenges through optimization of delivery systems, combination strategies, and the integration of nanotechnology may unlock the full potential of B7-H3-based immunotherapy in the treatment of OS.

Ischemic heart disease (IHD) remains the predominant cause of global mortality, necessitating accurate incidence forecasting for effective prevention strategies. Existing statistical models inadequately capture nonlinear epidemiological patterns, while deep learning approaches lack clinical interpretability. We constructed an interpretable predictive framework combining particle swarm optimization (PSO), bidirectional long short-term memory (BiLSTM) networks, and a novel multi-scale attention mechanism. Age-standardized incidence rates (ASIRs) from the Global Burden of Disease (GBD) 2021 database (1990-2021) were stratified across 24 sex-age subgroups and processed through 10-year sliding windows with advanced feature engineering. SHapley Additive exPlanations (SHAP) provided a three-level interpretability analysis (global, local, and component). The framework achieved superior performance metrics: mean absolute error (MAE) of 0.0164, root mean squared error (RMSE) of 0.0206, and R² of 0.97, demonstrating a 93.96% MAE reduction compared to ARIMA models and a 75.99% improvement over CNN-BiLSTM architectures. SHAP analysis identified females aged 60-64 years and males aged 85-89 years as primary predictive contributors. Architectural analysis revealed the residual connection captured 71.0% of the predictive contribution (main trends), while the BiLSTM-Attention pathway captured 29.0% (complex nonlinear patterns). This interpretable framework transforms opaque algorithms into transparent systems, providing precise epidemiological evidence for public health policy, resource allocation, and targeted intervention strategies for high-risk populations.

Neurological disorders such as Parkinson's and Alzheimer's diseases often involve overlapping motor and cognitive impairments that motivate integrated rehabilitation approaches. This study presents the technical validation of a dual-modality rehabilitation platform that combines haptic-based motor interaction with cognitive engagement through an adaptive Sudoku task in healthy adults under simulated tremor conditions. The system integrates a real-time tremor-filtering pipeline based on discrete wavelet denoising, Kalman smoothing, and wavelet packet decomposition, designed to attenuate high-frequency oscillations while preserving voluntary motion. The preclinical evaluation was carried out in two stages: (i) technical validation with healthy adults performing a standardized cognitive-haptic task under three conditions (no tremor, simulated tremor without filtering, simulated tremor with filtering) and (ii) extended usability testing with older participants without diagnosed neurological disorders. Quantitative evaluation focused on latency, performance degradation under simulated tremor, and partial restoration with filtering, while usability was assessed using the System Usability Scale (SUS). The platform achieved low end-to-end latency (41.4 ± 1.4 ms) and high usability (overall mean SUS = 81.4 ± 6.2), indicating stable performance and positive user feedback. Filtering significantly improved performance compared with unfiltered tremor but did not fully restore baseline performance, highlighting the current algorithm as a first-step compensation strategy rather than a complete solution. This work therefore demonstrates technical feasibility and interaction performance in healthy participants under simulated tremor; it does not assess clinical effectiveness and is intended to inform subsequent patient studies in populations with neurodegenerative diseases.

This study aimed to develop a microencapsulation formulation for efficient encapsulation of β-glucosidase to improve its stability in a rumen-like environment and sustain activity post-rumen in the ruminant gut. Various alginate-based formulations were evaluated to achieve high encapsulation efficiency (EE) and stability. These included control alginate beads (AB), microcapsules with chitosan (MCS), alginate-sucrose beads (AOS), alginate-sucrose-maltodextrin beads (AOMS), and alginate pectin beads (APB). The microcapsules were made using Buchi encapsulator B-390 with calcium chloride as the gelling solution. Alginate proved to be a suitable polymer for β-glucosidase encapsulation and <1 mm diameter microbeads were obtained across all formulations. Alginate alone (AB: 1% alginate, 0.2 U/mL β-glucosidase) showed low EE (3% ± 1.0) due to leakage and syneresis. Modifying the gelling solution with 0.1% chitosan (MCS) increased EE to 49 ± 2.64% by reducing alginate porosity. Further improvements were achieved by adding stabilizers to the alginate solution (AB), in addition to using the modified gelling solution (MCS): Adding sucrose (AOS) at 4% increased EE to 95.5 ± 2.08%, while adding sucrose (4%) and maltodextrin (2%) (AOMS) achieved 100 ± 2.16%. On the other hand, adding pectin (4%) (APB) to the alginate solution resulted in a lower EE of 40.5% ± 2.55, likely due to interference with alginate crosslinking. In vitro rumen fermentation showed a dry matter degradation of 42-54%, underscoring the need for more robust microcapsules. Encapsulation strategies, such as incorporation of additional protective layers, are essential to enhance bead stability, minimize degradation, and improve enzyme retention, to ensure efficient delivery and sustained enzymatic activity in the hindgut.