BioMedical Engineering OnLine最新文献

{"title":"Synergistic enhancement of peripheral nerve regeneration using electrospun polylactic acid conduits with leupeptin and methylprednisolone in rats.","authors":"Xiangchang Cao, Fuzhou Yang, Yaping Zhu, Changqing Bai, Jinwu Tang, Jionghua Dai, Luo Huang, Qiming Chen","doi":"10.1186/s12938-026-01565-y","DOIUrl":"10.1186/s12938-026-01565-y","url":null,"abstract":"<p><p>Peripheral nerve injuries pose significant clinical challenges, leading to sensory and motor impairments, neuralgia, and socioeconomic burdens due to suboptimal recovery. This study evaluated the efficacy of electrospun polylactic acid (PLA) conduits combined with leupeptin and methylprednisolone for nerve regeneration in a rat sciatic nerve defect model. Fifty adult male Sprague-Dawley rats were randomized into five groups (n = 10/group): control, saline, leupeptin (0.5 mg/mL), glucocorticoid (30 mg/mL methylprednisolone), and combined (leupeptin + methylprednisolone). A 10-mm sciatic nerve gap was created and bridged with PLA conduits filled with the respective treatments. Functional recovery was assessed via the sciatic functional index (SFI) from weeks 4-16 postsurgery. Histological analyses (hematoxylin-eosin, Masson, and Ponceau red staining) and immunofluorescence for collagen I/III were performed at week 16. The combined group showed significantly higher SFI scores (e.g., - 20.5 ± 4.2 at week 16 vs. - 45.3 ± 5.1 in saline; P < 0.001), enhanced regeneration, reduced fibrosis, and improved myelination compared to others. These results indicate that the combined approach optimizes the regenerative microenvironment, warranting further translational studies.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2026-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13214232/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147670153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Paincontrol: identity-preserving pain expression transfer with generative diffusion models.","authors":"Yasamin Zarghami, Muhammad Muzammil, Vida Adeli, Hailey Reimer, Thomas Hadjistavropoulos, Babak Taati","doi":"10.1186/s12938-026-01561-2","DOIUrl":"10.1186/s12938-026-01561-2","url":null,"abstract":"<p><p>Automated pain detection models rely on large, diverse facial expression datasets; yet pain expression data are scarce due to privacy concerns and ethical constraints. This study explores synthetic pain expression generation as a potential solution, investigating whether expression transfer methods are sufficiently accurate to expand training sets by transferring expressions from a limited set of real data to a virtually unlimited set of identities. This work represents an initial step toward the use of privacy-preserving generative AI for synthetic data generation to improve in pain detection. We introduce PainControl, a novel facial landmark-guided method for identity-preserving pain expression transfer. Rather than using motion signals from video, as in prior methods, our method adapts a ControlNet approach, using text and dense facial landmarks to control fine-grained facial muscle activations while maintaining identity consistency through image embeddings. Identity preservation enables the generation of demographically diverse datasets, improving generalizability across populations. We compare PainControl to existing expression transfer methods and evaluate (i) perceptual realism, (ii) identity preservation, (iii) facial action unit (AU) transfer accuracy, and (iv) downstream pain detection performance. Our model produces realistic synthetic pain expressions, outperforming baselines in human-rated Likert-scale assessments. However, AU transfer analysis reveals challenges in accurately synthesizing expression intensities-particularly for AU4 (Brow Lowering) and AU43 (Eye Closure), which are crucial for pain recognition. When used to augment real pain datasets, synthetic images did not improve classifier performance, likely due to artifacts, AU misalignment, and the lack of temporal motion cues. Critically, however, our experiments in data-scarce regimes, where real pain expressions are extremely limited, demonstrate that synthetic augmentation through PainControl provides significant and consistent performance gains for pain detection models. These results validate that our ControlNet-based approach has reached sufficient maturity and accuracy to serve as a reliable data augmentation tool in pain detection domains, particularly in scenarios, where acquiring real data are prohibitively difficult or ethically constrained. This finding establishes the practical viability of expression transfer methods for addressing the fundamental data scarcity challenge in affective computing and clinical pain assessment. These results highlight both the promise and current limitations of generative models for pain expression, pointing to future research in AU alignment and temporal modeling for clinical-grade applications.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2026-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13214276/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147670096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence in microbiology: implications for metagenomics, diagnostics, and AMR surveillance.","authors":"Renu Khangarot, Vandana Kumari, Rajeev Mishra, Abhijeet Singh","doi":"10.1186/s12938-026-01568-9","DOIUrl":"10.1186/s12938-026-01568-9","url":null,"abstract":"<p><p>Artificial intelligence (AI) is now a key player in modern microbiology, as it enables high-resolution analyses of genomic, metagenomic, and clinical data for the monitoring of infectious disease and antimicrobial resistance (AMR). Considerable advancements in deep learning, transformer-based sequence models, graph neural networks, and multimodal architectures have greatly improved microbial classification accuracy, antibiotic resistance gene (ARG) detection, and resistance prediction. Taking metagenomic sequencing into consideration, these advancements have contributed to the development of sensitive, scalable, and non-invasive methods to profile microbiomes, determine novel resistance, and monitor AMR trends at the population level. This review summarizes recent advances in AI-aided microbiology, with a particular emphasis on AMR surveillance. Specific topics include deep learning frameworks for ARG annotation, emerging approaches to identifying new resistance genes, and multimodal applications (genomic and clinical metadata) aimed at improving phenotype prediction. The role of metagenome-assembled genomes (MAGs) to enhance AMR surveillance efforts is noted, along with their noted limitations relative to isolate genomes. The discussion includes the examination of explainable AI (XAI) techniques including SHAP, attention mechanism approaches, and gradient-based attribution approaches, with the aim of increasing transparency and clinical explainability. We also cover potential applications including AI-enabled non-invasive fecal microbiome diagnostics, laboratory automation, and environmental surveillance. While there has been significant progress, unresolved issues exist relating to dataset variations, liability of models to datasets, interpretability, and regulatory approval. Overcoming these barriers, however, will require standardized frameworks for these workflows, privacy-preserving federated learning methods, and interpretable AI frameworks for clinical and public health tools. AI could fundamentally change AMR surveillance by allowing for earlier resistance detection, advanced risk assessment recommendation, and improved monitoring strategies globally.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2026-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13214064/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147662050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feasibility of a live-stream dance class for people with chronic stroke.","authors":"Sarah Gregman, Wade W Michaelchuk, Lauren C Belfiore, Kara K Patterson","doi":"10.1186/s12938-026-01567-w","DOIUrl":"10.1186/s12938-026-01567-w","url":null,"abstract":"<p><strong>Background: </strong>Adapted dance is a promising intervention to improve physical and psychosocial well-being post-stroke. At-home participation with a live-streamed dance program could address barriers to in-person attendance (e.g., limited community ambulation, and transportation). However, psychosocial benefits may be diminished because of reduced social interactions. The primary study objective was to assess the feasibility and safety of a live-streamed dance program for chronic stroke. Secondary objectives were to characterize participants who chose live-stream vs in-person options and quantify pre-post-changes in balance, gait and social connection.</p><p><strong>Methods: </strong>People with chronic stroke chose to attend a live-streamed adapted dance program in-person or at home twice a week for 4 weeks. Feasibility criteria were tracked, and participants were characterized with self-report (Center for Epidemiologic Studies-Depression Scale; CES-D) and performance-based measures (e.g., Montreal Cognitive Assessment and Chedoke McMaster Assessment) at baseline. Pre-post-secondary measures included gait speed, Mini-Balance Evaluation Systems Test, Activities of Balance Confidence Scale, and Inclusion of Community in Self scale. Unpaired median/mean differences in baseline clinical presentation were used to compare groups. Paired median/mean differences were used to examine change in secondary outcomes.</p><p><strong>Results: </strong>Interest and enrollment rates for both groups combined were 87% and 38%, respectively. Of those enrolled, 8/13 chose in-person and 5/13 chose live stream. In-person and live-stream attendance rates were 83% and 89%, and retention rates were 80% and 75%, respectively. There were no major adverse events related to the classes. There were 3 unrelated minor events of fatigue and 1 minor event of muscle soreness as expected with exercise. At baseline, the in-person group had greater depressive symptoms (CES-D score, median [IQR] difference: 11.5 [-21.5, -5]), and faster mean gait speed (-25.8 cm/s [-50.98, 0.006]) than the live-stream group. There were no pre-post-changes in secondary outcome measures.</p><p><strong>Conclusions: </strong>A live-streamed dance intervention featuring in-class and at-home participation is safe and feasible for people with chronic stroke. These results will inform a future randomized controlled trial of a live-stream dance program with a longer duration while considering how factors such as gait function and mood may relate to the choice between in-person and at-home attendance.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13214060/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147643950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lower-limb prosthetic mechanisms: recent progress, control innovations and barriers to real-world adoption.","authors":"Syed Riyas Ahamed, Sandip Saha, Awani Bhushan","doi":"10.1186/s12938-026-01558-x","DOIUrl":"10.1186/s12938-026-01558-x","url":null,"abstract":"<p><p>Lower-limb impairments caused by amputation, stroke or paralysis severely impact mobility and independence. Assistive technologies for these conditions include passive prostheses, powered limbs and robotic exoskeletons, each offering distinct advantages in simplicity, functionality and adaptability. Recent advancements have significantly enhanced these systems through innovations in biomechanics, intelligent control and materials engineering. Improvements in biomechanical modelling and neuromuscular interfaces such as electromyography (EMG) and pressure-based sensors have enhanced user intent recognition and facilitated more adaptive gait control. Intelligent control strategies, including machine learning algorithms, variable impedance control and phase-based coordination, enable responsive adaptation to different terrains and movement demands. In parallel, advancements in materials such as carbon fibre composites and 3D-printed polymers have enabled the creation of lightweight, robust and customisable components that enhance user comfort and device performance. These innovations demonstrate meaningful gains in gait symmetry, stability and metabolic efficiency. However, major challenges remain in developing intuitive and reliable control outside laboratory settings, achieving seamless adaptation to unpredictable terrain, ensuring long-term user comfort and fit and reducing weight and energy consumption to support full-day use. Limited real-world clinical validation and high cost further restrict widespread adoption. Continued progress will depend on integrated, human-centred co-design that combines efficient actuation, robust multimodal sensing, intelligent control and clinically meaningful evaluation. This review synthesises current research trends and highlights priority directions towards prosthetic systems that are more functional, accessible and capable of supporting confident everyday mobility.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13214069/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147643891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A randomised controlled study of the efficacy of Internet of Things-based telerespiratory rehabilitation for chronic respiratory diseases.","authors":"Mengmeng Wu, Wenjuan Xu, Xin Zhao, Ningning Fang, Kaishu Li","doi":"10.1186/s12938-026-01562-1","DOIUrl":"10.1186/s12938-026-01562-1","url":null,"abstract":"<p><strong>Objective: </strong>To explore IoT-based remote respiratory rehabilitation for chronic respiratory disease patients at home.</p><p><strong>Design: </strong>This was a randomised controlled trial.</p><p><strong>Setting: </strong>The study took place in patients' homes (post-discharge) and the Department of Respiratory and Critical Care Medicine of Binzhou People's Hospital, Shandong First Medical University (inpatient period).</p><p><strong>Participants: </strong>123 patients (mean age 68.85 ± 10.72 years, 55% male) with chronic respiratory diseases.</p><p><strong>Interventions: </strong>Patients were randomly assigned to either a 6-month Internet of Things (IoT)-based remote respiratory rehabilitation programme after hospital discharge (study group, n = 60) or usual care with conventional respiratory rehabilitation (control group, n = 63).</p><p><strong>Main outcome measures: </strong>The primary outcomes were the 6-min walking test (6MWT) and lung function (forced vital capacity [FVC], forced expiratory volume in 1 s [FEV1] and peak expiratory flow [PEF]). The secondary outcomes included the modified Medical Research Council (mMRC) dyspnoea index, diaphragm thickness and rehospitalisation rate. Outcomes were assessed at baseline (during hospitalisation, approximately 4 weeks before the intervention started) and after 6 months.</p><p><strong>Results: </strong>The study group showed significantly greater improvements in 6MWT (mean difference 63.74 m, 95% CI 45.21-82.27), FVC (0.54 L, 95% CI 0.43-0.65), FEV1 (0.68 L, 95% CI 0.54-0.82), PEF (73.16 L/min, 95% CI 46.22-100) and diaphragm thickness (6.79 mm, 95% CI 4.68-8.90) than the control group (all p < 0.001). The between-group difference in 6MWT (63.74 m) exceeded the minimal clinically important difference (30 m) for chronic respiratory diseases.</p><p><strong>Conclusion: </strong>A 6-month IoT-based remote respiratory rehabilitation programme significantly improved exercise capacity, lung function, dyspnoea index and respiratory muscle strength and reduced rehospitalisations when compared with conventional rehabilitation in patients with chronic respiratory diseases.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2026-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13214317/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147632333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and validation of a machine learning model for predicting 6-month mortality in patients with infective endocarditis.","authors":"Xiujuan Wu, Yue Han, Weili Liu, Song Lin, Jie Yuan, Yannan Ma, Chuanyu Gao","doi":"10.1186/s12938-026-01559-w","DOIUrl":"10.1186/s12938-026-01559-w","url":null,"abstract":"<p><strong>Objective: </strong>This study aimed to develop and validate a machine learning (ML)-based model for predicting 6-month all-cause mortality in patients diagnosed with infective endocarditis (IE), using retrospective data from a single tertiary care center. Additionally, key prognostic features were identified through model interpretability analysis.</p><p><strong>Methods: </strong>A cohort of 444 patients with IE was retrospectively assessed and randomly divided into a training set (n = 310) and a test set (n = 134). Following feature selection, a Cox proportional hazards model and four ML-based survival models: Random Survival Forest, Extremely Randomized Survival Trees (EST), eXtreme Gradient Boosting, and Support Vector Machine were established. Model performance was assessed using the concordance index (C-index), time-dependent area under the curve (AUC), and Kaplan-Meier survival analysis. The model demonstrating optimal performance underwent interpretability analysis using Shapley additive explanations and an exploratory online tool was developed to demonstrate its functionality.</p><p><strong>Results: </strong>Nine predictors were selected for model construction: hemoglobin concentration, presence of severe valvular regurgitation, occurrence of septic shock, New York Heart Association (NYHA) classification, serum albumin level, intracerebral hemorrhage, neutrophil percentage, history of coronary heart disease, and receipt of surgical intervention. The EST model demonstrated the highest predictive performance in the test set (C-index: 0.852), with the smallest discrepancy in performance between the training and test datasets, indicating favorable generalizability. Time-dependent AUCs at 30, 90, and 180 days were 0.957, 0.925, and 0.855, respectively. Surgical intervention was modeled as a protective factor, associated with lower mortality risk, while intracerebral hemorrhage, higher NYHA class, severe valvular regurgitation, and septic shock were associated with increased risk.</p><p><strong>Conclusions: </strong>The EST model demonstrated a high predictive accuracy for 6-month mortality in patients with IE, outperforming both conventional and other ML models. Its robust performance and interpretability suggest that, following rigorous external and multicenter validation, it may serve as a hypothesis-generating tool for risk stratification. It is not yet validated for direct clinical application and should currently be considered exploratory.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2026-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13085757/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147621725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing hemodynamic changes in diabetic patients with and without peripheral arterial disease using dynamic vascular optical spectroscopy.","authors":"Rabah Al Abdi, Nesreen Saadeh, Heba Hijazi, Nabil Al-Zoubi, Alessandro Marone, Nisha Maheshwari, Andreas H Hielscher","doi":"10.1186/s12938-026-01564-z","DOIUrl":"10.1186/s12938-026-01564-z","url":null,"abstract":"<p><strong>Background: </strong>Peripheral Arterial Disease (PAD) affects approximately 230 million people globally and is prevalent in 20%-50% of individuals with diabetes. Current diagnostic tools, such as the ankle-brachial index, have limited sensitivity in diabetic populations. This study utilizes dynamic vascular optical spectroscopy (DVOS) at thigh occlusion pressures of 60 and 100 mmHg to examine hemodynamic responses in the feet of diabetic patients, primarily aiming to enhance the detection of PAD in this group. We conducted a prospective cohort study involving 118 diabetic patients with PAD (50 males; age: 59.1 ± 7.9 years) and 118 diabetic subjects without PAD (52 males; age: 54.8 ± 13.7 years). DVOS was used to measure changes in oxyhemoglobin (ΔHbO), hemoglobin flow (HF), and tissue oxygen consumption (VO₂) in the foot during occlusions. Group differences were assessed using the Mann-Whitney U test, and multivariable logistic regression was used to adjust for age, sex, diabetes duration, and hypertension duration, BMI, and smoking status. Diagnostic performance was measured using ROC analysis with stratified fivefold cross-validation.</p><p><strong>Results: </strong>The 60-mmHg occlusion revealed clearer hemodynamic differences than the 100-mmHg. During 60-mmHg occlusion, mean values for ΔHbO (2.0 ± 1.74 μmol), HF (0.86 ± 0.49 μmol/s), and VO₂ (0.040 ± 0.022 mL O₂/100 mL tissue/min) were significantly lower in the PAD group compared to the non-PAD group (ΔHbO: 6.81 ± 4.11 μmol; HF: 2.48 ± 0.85 μmol/s; VO₂: 0.066 ± 0.035 mL O₂/100 mL/min; p < 0.00625). After adjustment for covariates, HF during the 60-mmHg occlusion remained the strongest discriminator for PAD, with an AUC of 0.983, accuracy of 0.918, sensitivity of 0.932, and specificity of 0.904 (mean across folds).</p><p><strong>Conclusions: </strong>Hemodynamic parameters measured by DVOS during thigh venous occlusion differ significantly between diabetic patients with and without PAD. Our findings suggest that 60-mmHg occlusion is more effective than 100 mmHg for eliciting PAD-sensitive hemodynamic parameters. These results highlight the potential sensitivity of DVOS to PAD-related vascular impairment during low-pressure thigh occlusion.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13085621/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147589855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a vasopressor control module for testing hemorrhagic shock resuscitation controllers.","authors":"David Berard, Michael D Lopez, Austin Ruiz, Jonathan Marrero Bermudez, Rachel Gathright, Tina M Rodgers, Sofia I Hernandez Torres, Caroline Gusson Shimoura, Evan Ross, Eric J Snider","doi":"10.1186/s12938-026-01552-3","DOIUrl":"10.1186/s12938-026-01552-3","url":null,"abstract":"<p><strong>Introduction: </strong>Patients in hemorrhagic shock who are unresponsive to fluid administration can potentially benefit from and may even require vasopressor therapy. However, manual vasopressor titration in mass casualty or resource-limited settings can be arduous, increasing the risk of under- or over-treatment. Closed-loop vasopressor adaptive resuscitation controllers (V-ARCs) offer a potential solution, but their development is hindered when the iterative tuning and design process is primarily dependent on large-scale animal studies. To solve this problem, we developed a Vasopressor Control Module (VCM) for a hardware-in-loop automated testbed for resuscitation controllers (HATRC) to enable the systematic evaluation of V-ARCs.</p><p><strong>Methods and results: </strong>The VCM's design was informed by vasopressor administration data captured in a hemorrhagic shock swine model, which revealed four key physiological variables that defined the hemodynamic response to vasopressor infusion: lag time, real response, overshoot, and pressure-time responsiveness. The incorporation of these variables enabled the VCM to replicate physiological variability, dose-dependent responsiveness, and disturbance conditions representative of the clinical setting. Proof-of-concept testing was achieved by comparing multiple V-ARC designs under different testing conditions and successfully differentiating their performance.</p><p><strong>Conclusions: </strong>This work establishes a flexible, physiologically grounded platform for vasopressor controller development, reducing dependence on animal testing while enabling rapid and robust controller evaluation. Future work will expand physiological modeling, incorporate additional hemodynamic variables, and support multiagent resuscitation.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2026-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13141433/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147519663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Degradable piezoelectric KNN/PLLA nanofibers for promoting osteogenesis and angiogenesis in bone regeneration.","authors":"Qiuyi Zhu, Ziteng Zhao, Anlin Liu, Yunbo Guan, Yun Yang, Yang Xia, Yao Shu, Yantao Zhao","doi":"10.1186/s12938-026-01560-3","DOIUrl":"10.1186/s12938-026-01560-3","url":null,"abstract":"<p><strong>Background: </strong>Replicating the endogenous electromechanical microenvironment of bone remains a significant challenge in regenerative medicine. This study aims to develop a promising scaffold by integrating piezoelectric K_0.5Na_0.5NbO₃/poly (KNN) nanoparticles into poly (L-lactic acid) (PLLA) nanofibers to promote bone healing.</p><p><strong>Methods: </strong>KNN/PLLA nanofibers were electrospun and verified via X-ray diffraction (XRD). Scanning Electron Microscope (SEM) was used to characterize morphology and assess biocompatibility on 9 wt% KNN/PLLA. The distribution of KNN was analyzed via energy dispersive spectroscopy (EDS). The mechanical properties were evaluated through Universal Testing Machine (UTM). Piezoelectric properties were quantified using an electrostatic voltmeter and Piezoresponse Force Microscopy (PFM), while Niobium (Nb) ion release was measured via inductively coupled plasma (ICP) analysis. Osteogenic differentiation was evaluated through cell proliferation, quantitative real-time PCR (qRT-PCR) for osteogenic markers osteocalcin (OCN) and runt-related transcription factor 2 (RUNX2), alkaline phosphatase (ALP) and Alizarin Red S (ARS) assays for osteogenesis, and tube formation for angiogenesis.</p><p><strong>Results: </strong>XRD confirmed successful KNN loading. Tensile tests showed that KNN incorporation enhanced mechanical properties. ICP analysis detected Nb release, reflecting the degradation. Increasing KNN content reduced fiber diameter and enhanced piezoelectricity. SEM verified biocompatibility via cell growth on 9 wt% KNN. Notably, KNN loading dose dependently upregulated OCN and RUNX2 expression, enhanced ALP activity and ARS staining, and promoted angiogenesis.</p><p><strong>Conclusion: </strong>The 9 wt% KNN/PLLA nanofibers exhibited superior physicochemical and mechanical properties, a sevenfold increase in piezoelectric output. The nanofibers significantly enhanced bone regeneration, evidenced by upregulated osteogenic markers (OCN/RUNX2) and markedly improved ALP activity (60%) and ARS mineralization (70%). Coupled with favorable degradation and enhanced angiogenesis, the nanofibers demonstrate high potential for functional bone tissue engineering.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2026-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13220530/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147509371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}