Bioengineering最新文献

{"title":"Multimodal Feedback-Enhanced Soft Hand Rehabilitation Robot: System Development and Preliminary Clinical Evaluation.","authors":"Jianwei Lai, Aiguo Song, Xuemei Fan, Guangyu Sun, Yaxuan Di, Ting Wu","doi":"10.3390/bioengineering13101172","DOIUrl":"https://doi.org/10.3390/bioengineering13101172","url":null,"abstract":"<p><p>Soft robotic systems have attracted increasing attention for hand rehabilitation because of their compliance, adaptability, and potential for comfortable human-robot interaction. However, the clinical translation of soft hand rehabilitation robots remains limited by insufficient integration of interactive training, user feedback, and practical wearability. This study presents a multimodal feedback-enhanced soft hand rehabilitation robot based on lattice-structured soft pneumatic actuators (LSPAs), together with a preliminary clinical evaluation. The system integrates a fabric-based soft glove, pneumatic actuation, fingertip force sensing, hand-motion tracking, and interactive virtual rehabilitation tasks to provide coordinated visual, auditory, force-related, and mirror-visual feedback. Material characterization was first conducted to compare three TPU materials with different mechanical properties, followed by system-level evaluation of usability, wearability, and actuator durability. A four-week robot-assisted rehabilitation intervention was then conducted in nine stroke survivors. Participants completed five one-hour sessions per week, including passive training and multimodal interactive training. Clinical outcomes were evaluated using the Brunnstrom motor recovery stage, Fugl-Meyer Assessment of the upper extremity (FMA-UE), National Institutes of Health Stroke Scale (NIHSS), and Activities of Daily Living (ADL) scale, while usability was assessed using the System Usability Scale (SUS). After four weeks, the mean Brunnstrom stage increased from 2.44±2.07 to 3.67±2.12, FMA-UE increased from 18.33±25.63 to 32.67±23.11, NIHSS decreased from 5.67±4.36 to 3.22±2.99, and ADL increased from 49.78±39.53 to 66.67±22.50. The mean SUS score was 74.4. TPU420D provided the best durability among the tested configurations, with more than 30 h of recorded use without failure during the experimental period. These findings support the feasibility and usability of the proposed system and provide preliminary evidence for its application in stroke hand rehabilitation. Because of the small sample size, heterogeneous post-stroke status, short intervention period, and absence of a control group, the clinical findings should be interpreted as preliminary rather than confirmatory evidence of therapeutic efficacy.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"13 10","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149261738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using Long Short-Term Memory and Causal Forest to Identify Preeclampsia Subtypes with Differential Aspirin Associations for Preterm Birth Prevention.","authors":"Cheng Liu, Yang Su, Yao Wei, Yongxin Li, Chengxi Bao, Rui Qiao","doi":"10.3390/bioengineering13101171","DOIUrl":"https://doi.org/10.3390/bioengineering13101171","url":null,"abstract":"<p><p>Aspirin prevents preterm birth in some women with preeclampsia but not others, yet no longitudinal multi-indicator study has examined subtype differences in aspirin response. We aimed to identify subtypes most likely to benefit. In this retrospective cohort of 53,362 deliveries (4505 preeclamptic women), we applied a two-stage machine learning framework using 15 indicators (14 laboratory tests plus systolic blood pressure) from two-time windows: before 16 weeks and within 2 weeks before delivery. A long short-term memory autoencoder with K-means clustering identified subtypes, and causal forest estimated the average treatment effect of aspirin on preterm birth (<37 weeks) for each subtype, adjusted for confounders. Five stable subtypes were identified (silhouette coefficient 0.542; mean adjusted Rand index 0.903). One subtype (<i>n</i> = 345), characterised by mild liver enzyme elevation and coagulation abnormalities in late pregnancy, had the highest preterm birth rate (57.7%) and ICU admission rate (9.6%), and showed the largest aspirin-associated reduction in preterm birth (ATE -0.026, 95% CI: -0.032 to -0.021), consistent across 70/30 splits. Sensitivity analyses using 34 indicators confirmed similar effects (ATE -0.027, 95% CI: -0.031 to -0.023), while no benefit was observed in non-preeclamptic women. These findings suggest that aspirin prophylaxis may be associated with a greater reduction in preterm birth in this preeclampsia subtype, but further validation is required.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"13 10","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149261846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence in Healthcare: From Predictive Models to Generative, Multimodal, and Agentic AI.","authors":"İsmail Baydili, Burak Tasci, Gülay Tasci, Sengul Dogan, Turker Tuncer","doi":"10.3390/bioengineering13101175","DOIUrl":"https://doi.org/10.3390/bioengineering13101175","url":null,"abstract":"<p><p>Artificial intelligence (AI) is increasingly used across the diagnostic pathway, from screening and disease detection to differential diagnosis, prognostic stratification, and treatment-response assessment. This narrative review examines predictive, generative, multi-modal, and agentic AI through a diagnostic-science lens and evaluates the evidence required for translation into clinical practice. Predictive models can identify complex patterns in images, physiological signals, laboratory data, and electronic health records, but high retrospective accuracy does not establish diagnostic utility. Clinical validity and utility depend on the intended use, target population, disease spectrum and prevalence, reference standard, operating threshold, calibration, and the consequences of false-positive and false-negative results. Generative AI may support problem representation, differential diagnosis, information synthesis, and documentation, yet fluent outputs can omit critical alternatives, amplify false premises, or convey unjustified certainty. Multimodal systems may better reflect clinical reasoning by integrating imaging, text, signals, pathology, and molecular data, but they must demonstrate incremental value over the best single-modality test and remain robust to missing data. Agentic systems can coordinate evidence retrieval, test selection, and sequential workflows, thereby increasing the need for bounded permissions, auditability, error recovery, and human escalation. Across all paradigms, a credible pathway to diagnostic implementation requires independent external and prospective validation, representative consecutive patients, subgroup analysis, workflow and human-AI evaluation, clinical impact studies, and post-deployment surveillance. The field should therefore be judged not by model capability alone, but by whether AI measurably improves diagnostic yield, timeliness, safety, equity, and patient-relevant outcomes in real care settings.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"13 10","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149261609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Blockchain and Internet of Medical Things for Resilient Healthcare.","authors":"Graziella Di Grezia, Alfredo Clemente, Stefano Dugheri, Vincenzo Mattei, Alessio Gili, Emanuela Mari, Fabrizio Urraro, Nicola Mucci, Antonio Baldassarre","doi":"10.3390/bioengineering13101173","DOIUrl":"https://doi.org/10.3390/bioengineering13101173","url":null,"abstract":"<p><p>The COVID-19 pandemic exposed persistent weaknesses in health-data interoperability, cybersecurity, provenance, cross-organizational governance, and continuity of digital care. This short communication presents an updated focused narrative synthesis and a five-layer governance framework for integrating blockchain and the Internet of Medical Things (IoMT) in patient-centered digital health. The literature was updated through 10 August 2026, with recent peer-reviewed evidence complemented by authoritative standards and European regulatory sources. The synthesis positions blockchain primarily as a distributed governance, provenance, and audit layer rather than a clinical database or stand-alone security solution, while IoMT provides heterogeneous sensing infrastructure for continuous and distributed care. The framework separates: (1) device and data acquisition; (2) network and edge orchestration, including 5G/SDN where clinically justified; (3) blockchain-based governance; (4) privacy-enhancing data, analytics, and AI; and (5) application and service delivery. Homomorphic encryption, zero-knowledge proofs, secure aggregation, differential privacy, and federated learning are treated as complementary controls with distinct threat models and computational trade-offs. The revised framework also incorporates the European Health Data Space, the EU AI Act and its medical-device interplay, and NIS2 as a regulatory overlay. Across six use-case domains, evidence is strongest for technical feasibility, provenance, and programmable access control; evidence for clinical effectiveness, cost-effectiveness, equity, sustainability, and large-scale deployment remains limited. The proposed model therefore emphasizes standards-based interoperability, minimization of on-chain personal data, endpoint trust, auditable human oversight, and prospective real-world validation.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"13 10","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149261646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Fast and Generalizable Deep Neural Network for the Detection of Atrial Fibrillation.","authors":"Harshit Mishra, Farhan Adam, Nachiket Makwana, Pradyot Tiwari, Subramani Kandasamy, K V S Hari","doi":"10.3390/bioengineering13101174","DOIUrl":"https://doi.org/10.3390/bioengineering13101174","url":null,"abstract":"<p><p>Atrial fibrillation (AFib) is the most prevalent sustained cardiac arrhythmia, yet most deep learning detectors are validated on a single cohort and few report uncertainty. We developed a compact 14M-parameter convolutional neural network for binary AFib classification from 10 s, 12-lead ECG at 500 Hz, trained exclusively on the HDXML dataset (67,432 ECGs; 9628 AFib) with on-the-fly augmentation for noise, drift and missing channels. Using one unchanged checkpoint, we evaluated six independently sourced public cohorts totaling over 1.25 million recordings, including MIMIC-IV critical care and two ambulatory Holter cohorts. All metrics are reported with 95% confidence intervals from a cluster bootstrap at the patient, recording or subject levels. ROC-AUC exceeded 0.95 in every cohort, from 0.960 (95% CI 0.956-0.963) on CODE-15 to 0.999 (0.998-0.999) on SPH, including 0.966 (0.965-0.967) on MIMIC-IV with 75.3% sensitivity and 97.7% specificity, 0.963 (0.929-0.987) on CPSC 2021 and 0.995 (0.988-0.999) on MIT-BIH. Performance was stable under progressive lead masking without retraining, and median inference took 110 ms per segment on a server-class CPU without a GPU. A compact network trained on one curated source can therefore generalize across hospitals, countries and recording hardware. This study is retrospective; prospective validation is required before clinical use.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"13 10","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149261588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BMA-Net: A Bilateral Attention Network with Retinal Domain Transfer-Learning for CIMT-Based Cardiovascular Risk Classification from Fundus Images.","authors":"Mingze Lyu, Abhirup Banerjee","doi":"10.3390/bioengineering13101168","DOIUrl":"https://doi.org/10.3390/bioengineering13101168","url":null,"abstract":"<p><p>Carotid intima-media thickness (CIMT) is an established marker of subclinical atherosclerosis, but its reliance on ultrasound equipment and trained operators limits its suitability for large-scale screening. Retinal fundus photography may offer a more scalable approach to vascular assessment, as it contains retinal vascular features associated with systemic cardiovascular health and can be acquired quickly and non-invasively. This study proposes a two-stage transfer learning framework for CIMT-based cardiovascular risk classification. An attention-enhanced EfficientNet-B4 is first pretrained on the Asia Pacific Tele-Ophthalmology Society 2019 Blindness Detection dataset and subsequently transferred to a weight-sharing Siamese architecture to classify the China Fundus Carotid Intima-Media Thickness dataset. Across ten independent initializations, the proposed model achieved an average macro-F1 score of 77.81% and an area under the receiver operating characteristic curve of 84.08% on the validation set and 78.29% and 85.22%, respectively, on the test set. It achieved overall higher point estimates for performance metrics than those previously reported for the Siamese ResNeXt baseline model with squeeze-and-excitation attention, and demonstrated a reduced disparity in class-wise performance. Grad-CAM analysis further revealed differences in the spatial activation patterns between models trained under different experimental configurations. Overall, these findings support the feasibility of fundus imaging as a scalable and non-invasive approach to CIMT-based cardiovascular risk assessment.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"13 10","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149261634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Engineering Polymeric Biomaterials for Radiation-Induced Vaginal Injury After Cervical Cancer Therapy: Pathobiological Basis, Material Strategies, and Future Perspectives.","authors":"Hui Chang, Xiaolong Wang, Yutong Wu, Qian Li, Qingsen Gao, Xianhu Liu, Chenchen Ren","doi":"10.3390/bioengineering13101169","DOIUrl":"https://doi.org/10.3390/bioengineering13101169","url":null,"abstract":"<p><p>Radiation-induced vaginal injury is an underrecognized complication of cervical cancer radiotherapy and evolves through a pathological continuum involving epithelial depletion, oxidative stress, chronic inflammation, microvascular dysfunction, extracellular-matrix remodeling, fibrosis, loss of tissue compliance, and vaginal stenosis. Current management remains largely supportive and is constrained by short local residence, limited anatomical adaptability, variable adherence, and insufficient control of radiation-specific tissue damage. This Mini-Review examines polymeric biomaterials for the prevention and repair of radiation-induced vaginal injury from a pathobiology-material-function perspective. Three complementary strategies are discussed: local polymeric formulations and mucoadhesive hydrogels for mucosal protection and sustained delivery; personalized dilators, stents, and adaptive devices for maintaining vaginal patency; and regenerative scaffolds and tissue-engineered constructs for restoring epithelial, vascular, stromal, and smooth-muscle compartments. Conventional hyaluronic-acid formulations have progressed furthest clinically, whereas responsive hydrogels, shape-adaptive devices, extracellular-matrix-derived materials, and cell- or exosome-based scaffolds remain predominantly preclinical. Importantly, much of the evidence derives from acute irradiation models, non-irradiated reconstruction studies, or engineering prototypes and therefore does not yet demonstrate durable reversal of chronic fibrosis or stenosis. Future development should prioritize stage-specific multifunctional interventions, fractionated irradiation models, standardized structural and functional outcomes, and rigorous oncological safety assessment. Acellular hydrogels, cell-free hybrid scaffolds, and removable drug-eluting devices may offer the most practical near-term translational routes.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"13 10","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149261656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Overcoming Vascular Graft Challenges in Tissue Engineering: A Review.","authors":"Riya Gandhi, Nora Molten, Selorm Dente, Quint Stein, Angel Artega, Emmett Reid, Narutoshi Hibino","doi":"10.3390/bioengineering13101170","DOIUrl":"https://doi.org/10.3390/bioengineering13101170","url":null,"abstract":"<p><p>The development of patient-specific vascular constructs is essential for advancing clinically effective 3D-bioprinted and tissue-engineered grafts. Optimizing cellular composition and promoting robust vascularization within engineered tissues is important for building an effective vascular construct. In cardiac and vascular patches in particular, insufficient microvascular development often leads to diffusion limitations, ischemia, and eventual necrosis, representing a major barrier to clinical translation. This review examines the challenges of designing constructs that are both anatomically functional and adequately vascularized. We discuss current strategies for tailoring cell ratios and enhancing vascular maturation, as well as emerging technologies in bioprinting, biomaterials, and computational design. Together, these innovations highlight future directions for creating grafts capable of functional integration and long-term survival in patients.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"13 10","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149261623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid Multimodal Fusion of Raw ECG Signals and Derived Measurements for Stroke Classification.","authors":"Soyun Im, Chulho Kim, Yu-Seop Kim","doi":"10.3390/bioengineering13101167","DOIUrl":"https://doi.org/10.3390/bioengineering13101167","url":null,"abstract":"<p><p>Stroke is a leading cause of death and long-term disability, and auxiliary methods for rapid patient identification are needed. Previous studies based on electrocardiography (ECG) have attempted stroke classification using quantitative measurements and future stroke risk prediction using raw signals. However, measurements do not preserve all waveform detail, and models using raw signals alone may not sufficiently learn quantitative characteristics from limited data. We therefore propose a hybrid fusion framework that integrates the raw 12-lead signal and the quantitative measurements of the same ECG record. It combines a signal-only model, a feature-only model, and an interaction early fusion model that couples the two inputs at the raw data and representation levels, averaging their logits for the final prediction. Evaluation used 4147 ECG records from 4020 patients in a single-institution retrospective cohort, with patient-level splits. The proposed model achieved an F1-score of 77.6%, an area under the receiver operating characteristic curve (AUC) of 0.832, and a Brier score of 0.169. Among the evaluated models, the proposed framework achieved the highest AUC and lowest Brier score. The AUC improved by 0.034 over the best unimodal model, and an AUC of 0.820 was maintained under a year-based temporal holdout. These results suggest that integrating complementary representations of the same ECG at multiple levels can improve stroke classification. The framework could potentially serve as an auxiliary decision-support tool for identifying patients who require further evaluation, using ECG characteristics statistically associated with stroke.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"13 10","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149261577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Influence of a Bacteriophage Cocktail as a Biocontrol Strategy on Sulfate-Reducing Bacteria in a Pilot-Scale System.","authors":"Marcella Silva Vieira, Roberto Sousa Dias, Helena Santiago Lima, Maíra Paula de Sousa, Cynthia Canêdo da Silva, Sérgio Oliveira de Paula","doi":"10.3390/bioengineering13101166","DOIUrl":"https://doi.org/10.3390/bioengineering13101166","url":null,"abstract":"<p><p>Sulfate-reducing bacteria (SRB) play crucial roles in microbiologically induced corrosion (MIC), especially in anaerobic environments where the production of hydrogen sulfide (H₂S) and biofilm formation accelerate metal degradation. Conventional treatments, such as THPS-based biocides, while effective, face limitations relating to the development of resistance and poor biofilm penetration. In this study, we evaluated the use of a non-specific bacteriophage cocktail as a biological control strategy against a mixed SRB culture (AP) biofilm and related corrosion, utilizing a pilot-scale system with AISI 1020 carbon steel coupons. Over 38 days, we monitored H₂S production, optical density, and ATP concentrations, along with the surface characterization of steel coupons through scanning electron microscopy (SEM) and profilometry. Metagenomic sequencing revealed changes in microbial community composition, including reductions in sulfate consumers and shifts in dominant taxa. SEM and profilometric analyses showed disrupted and less aggressive biofilms on treated surfaces, often exhibiting filamentous structures. Diversity indices indicated the modulation of microbial richness and composition over time. These findings demonstrate the potential of bacteriophage cocktails to reshape complex microbial communities and reduce MIC, providing a sustainable alternative to chemical biocides in large-scale industrial settings.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"13 10","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149261628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}