BioMedical Engineering OnLine最新文献

{"title":"Enhanced recovery after surgery in laparoscopic radical gastrectomy for gastric cancer: efficacy, safety, meta-analysis.","authors":"Han-Mei Hu, Hong-Yu Zhou, Li-Li Guo, Ling-Ling Chen, Zheng Shen","doi":"10.1186/s12938-025-01504-3","DOIUrl":"10.1186/s12938-025-01504-3","url":null,"abstract":"<p><strong>Objective: </strong>To systematically evaluate the efficacy and safety of enhanced recovery after surgery (ERAS) involving laparoscopic radical gastrectomy for gastric cancer (GC).</p><p><strong>Methods: </strong>A systematic literature search was conducted across four English databases (PubMed, the Cochrane Library, EMBASE, Web of Science) and two Chinese databases (China National Knowledge Infrastructure, Wanfang Database). The search period extended from database inception to 31 December 2024. Meta-analysis was performed using RevMan (v.5.3) software.</p><p><strong>Results: </strong>Thirteen randomised controlled trials (RCTs) involving 1,915 patients (953 in the experimental group, 962 in the control group) were included. Compared with the control group, the experimental group had shorter postoperative hospital stays, fewer complications, less intraoperative blood loss, shorter time to first flatus, first oral food intake and ambulation and lower C-reactive protein levels on days 1, 3 and 5, but a higher readmission rate. No deaths occurred.</p><p><strong>Conclusion: </strong>Enhanced recovery after surgery significantly enhances recovery and reduces complications in cases of laparoscopic radical gastrectomy, demonstrating both efficacy and safety. Despite a higher readmission rate, it represents a beneficial perioperative strategy worthy of clinical adoption.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"25 1","pages":"21"},"PeriodicalIF":4.6,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12884609/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146148839","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":"Performance of machine learning algorithms in diffusion tensor imaging of movement disorders: an exploratory meta-analysis.","authors":"Mohammad Amin Fathollahi, Yashar Khani, Hesam Bayati, Saman Zaman, Atousa Mahmoudi, Zahra Vatani, Hamidreza Amiri, Narges Norouzkhani, Fatemeh Zahra Idjadi, Sheida Karami, Mohammadamin Naghizadeh, Zahra Jalali Varnamkhasti, Mohammad Saeed Soleimani, Farbod Khosravi, Amir Hossein Golestan, Mahsa Asadi Anar, Mohsen Shahba, Alireza Ghaedamini, Melika Arab Bafrani","doi":"10.1186/s12938-026-01528-3","DOIUrl":"10.1186/s12938-026-01528-3","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Machine learning (ML) applied to diffusion tensor imaging (DTI) has emerged as a promising tool for detecting microstructural brain alterations in movement disorders. However, existing studies vary widely in design, sample size, imaging pipelines, and analytic rigor, resulting in high methodological heterogeneity that limits quantitative comparability.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objectives: &lt;/strong&gt;This exploratory meta-analysis and narrative synthesis aimed to characterize performance trends, methodological diversity, and sources of variability among ML models trained on DTI data for classifying movement disorders, rather than to infer a single pooled diagnostic effect. This was designated exploratory because extreme heterogeneity prevented confirmatory pooled effect inference, so the analysis focused on describing performance distributions and methodological patterns rather than estimating a unified diagnostic effect.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A systematic search of PubMed, Web of Science, and Scopus identified human studies applying ML algorithms to DTI for diagnostic or classification purposes. Accuracy, sensitivity, specificity, and the area under the curve (AUC) were extracted, with multiple imputation used for incomplete metrics with missingness rates below 40%. Random-effects modeling was employed to provide descriptive summaries, and subgroup analyses were conducted to explore trends across disorders, model architectures, and imaging modalities. Study qualities were assessed with JBI tools.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Forty-six studies (2016-2024) were included, spanning Parkinson's disease, Tourette syndrome, and essential tremor. Reported performance was generally high (median AUC ≈ 0.91), but between-study heterogeneity was extreme (I&lt;sup&gt;2&lt;/sup&gt; = 94.7%), indicating that studies were estimating distinct effects. Disorder-specific subgroup AUCs varied markedly: Essential Tremor (0.95), Parkinson's (0.90), Tourette's (0.88), and Other (0.79). Deep learning and radiomics-based models have reported higher accuracies, but they were often trained on small, single-center cohorts (37-139 participants), which limits their external validity. Pooled statistics were presented descriptively to illustrate performance ranges despite high heterogeneity, and were not interpreted as confirmatory effect sizes.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;ML models using DTI demonstrate high internal performance across studies, although generalizability remains limited across multiple movement disorders; however, current evidence remains exploratory due to small sample sizes, methodological fragmentation, and a lack of standardized imaging pipelines. Rather than confirmatory inference, these findings provide a descriptive map of emerging trends in ML-DTI diagnostics. Future progress will depend on data harmonization initiatives, multicenter collaborations, and federated learning frameworks that can support reproducible, generalizabl","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2026-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13032706/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146137173","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":"Walking rehabilitation in incomplete spinal cord injury: evaluating the impact of robotic exoskeleton-assisted training.","authors":"Fater A Khadour, Younes A Khadour, Gouda Xiang, Xiuli Dao","doi":"10.1186/s12938-026-01532-7","DOIUrl":"10.1186/s12938-026-01532-7","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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).</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p><p><strong>Trial registration: </strong>The Chinese Clinical Trial Register (ChiCTR) includes this study under registration number ChiCTR2000041186, dated December 21, 2020.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12973721/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146130931","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":"Quantitative evaluation of computational fluid dynamics application development in the cardiovascular field through literature retrieval and bibliometric analysis.","authors":"Fan Gao, Tingting Yang, Jian Wang, Baoyou Zhang","doi":"10.1186/s12938-026-01534-5","DOIUrl":"10.1186/s12938-026-01534-5","url":null,"abstract":"<p><strong>Background and objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12973748/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146123413","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":"Temporal machine learning framework for diabetic foot ulcer healing trajectory prediction.","authors":"Reza Basiri, Asem Saleh, Shehroz S Khan, Milos R Popovic","doi":"10.1186/s12938-026-01529-2","DOIUrl":"10.1186/s12938-026-01529-2","url":null,"abstract":"<p><strong>Objectives: </strong>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.</p><p><strong>Methods: </strong>Longitudinal data from 268 patients with 329 distinct ulcers across 890 appointments were analyzed. Features (n <math><mo>=</mo></math> 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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12964848/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146123543","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":"Beyond graphene: the MXene era in bioprinting.","authors":"Mananki Gutthedhar, Kishore Hazarika, Vijay Shankar Kumawat, N S Raviraja, Subrata Bandhu Ghosh, Kirthanashri S Vasanthan, Sanchita Bandyopadhyay-Ghosh","doi":"10.1186/s12938-026-01520-x","DOIUrl":"10.1186/s12938-026-01520-x","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12973904/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146123403","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":"Removal of cardiogenic oscillations during pressure support ventilation using sliding window singular spectrum analysis: proof-of-concept.","authors":"Parwane P Pagano, Edward J Ciaccio","doi":"10.1186/s12938-026-01536-3","DOIUrl":"10.1186/s12938-026-01536-3","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Materials and methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12973676/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146123516","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 evaluation of a vision pose-tracking based Beighton score tool for generalized joint hypermobility in individuals with suspected Ehlers-Danlos syndromes.","authors":"Andrea Sabo, Babak Taati, Amol Deshpande, Nimish Mittal","doi":"10.1186/s12938-026-01527-4","DOIUrl":"10.1186/s12938-026-01527-4","url":null,"abstract":"<p><strong>Background: </strong>Generalized joint hypermobility (GJH) is often challenging to assess, but its presence could suggest a syndromic diagnosis of Ehlers-Danlos Syndromes (EDS).</p><p><strong>Objective: </strong>An automated and objective method for estimating joint hypermobility with Beighton score using short video clips is proposed.</p><p><strong>Method: </strong>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).</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12964692/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146117656","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":"The effect of using patient-specific guides for total knee replacement without hardware removal in complex post-traumatic arthritis: an in-vitro study.","authors":"Chi-Pin Hsu, Chun-Chieh Chen, Yi-Sheng Chan, Chen-Te Wu, Jeng-Ywan Jeng, Alvin Chao-Yu Chen","doi":"10.1186/s12938-026-01514-9","DOIUrl":"10.1186/s12938-026-01514-9","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12997892/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146112215","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":"Risk factors and predicted model for clinical relapse after antiviral therapy cessation in chronic hepatitis B patients: a retrospective real-world data analysis.","authors":"Xiaoli Zhang, Meiyan Mao, Yunxia Zhang, Xiuxi Li","doi":"10.1186/s12938-026-01530-9","DOIUrl":"10.1186/s12938-026-01530-9","url":null,"abstract":"<p><strong>Background: </strong>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).</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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).</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12947437/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146096758","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}