BioMedical Engineering OnLine最新文献

{"title":"Predicting the perioperative transfusion risk of proximal femoral antirotation nailing (PFNA) for elderly patients with intertrochanteric fractures: a new predictive nomogram.","authors":"Donglei Wei, Yage Jiang, Xingcan Long, Nanchang Huang, Jianhui Xiang, Jianwen Cheng, Jinmin Zhao","doi":"10.1186/s12938-025-01419-z","DOIUrl":"https://doi.org/10.1186/s12938-025-01419-z","url":null,"abstract":"<p><strong>Background: </strong>Proximal femoral antirotation nailing (PFNA) for treating elderly patients with intertrochanteric fractures (EIFs) is often associated with substantial hidden blood loss. Perioperative blood transfusion to restore the lost blood has no effect on postoperative mortality and it increases the risk of postoperative infection. The goal of this study was to develop and validate a nomogram for predicting the risk of perioperative transfusion and intervening ahead of time to reduce the risk in EIF patients receiving PFNA.</p><p><strong>Methods: </strong>This study retrospectively examined and collected risk factors associated with transfusion in EIF patients treated with PFNA. Random forest with least absolute shrinkage and selection operator (LASSO) regression analysis was used to select characteristic variables and construct nomograms with the screening variables. The predictive model's discriminatory efficacy and calibration efficacy were assessed by receiver operating characteristic (ROC) curves, C-index, and calibration curves, respectively. Clinical usefulness was assessed by decision curve analysis (DCA).</p><p><strong>Results: </strong>The final nomogram consisted of five predictor variables: lower preoperative haemoglobin (HGB), age, preoperative urea, preoperative albumin, and surgical position. The nomogram showed good discriminatory and calibration efficacy with an area under the curve (AUC) value of 0.865 and a calibration curve highly approximating the ideal curve. In internal validation, the C-index of the model was calculated to be 0.823, indicating that the model exhibited superior predictive power.</p><p><strong>Conclusions: </strong>The nomogram constructed from preoperative HGB, age, urea, albumin, and surgical position can be used to predict more accurately the risk of perioperative transfusion in EIF patients treated with PFNA. Validation of the accuracy of this predictive model requires multicenter, prospective, and larger populations.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"24 1","pages":"80"},"PeriodicalIF":2.9,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144538032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A hybrid multi-instance learning-based identification of gastric adenocarcinoma differentiation on whole-slide images.","authors":"Mudan Zhang, Xinhuan Sun, Wuchao Li, Yin Cao, Chen Liu, Guilan Tu, Jian Wang, Rongpin Wang","doi":"10.1186/s12938-025-01407-3","DOIUrl":"10.1186/s12938-025-01407-3","url":null,"abstract":"<p><strong>Objective: </strong>To investigate the potential of a hybrid multi-instance learning model (TGMIL) combining Transformer and graph attention networks for classifying gastric adenocarcinoma differentiation on whole-slide images (WSIs) without manual annotation.</p><p><strong>Methods and materials: </strong>A hybrid multi-instance learning model is proposed based on the Transformer and the graph attention network, called TGMIL, to classify the differentiation of gastric adenocarcinoma. A total of 613 WSIs from patients with gastric adenocarcinoma were retrospectively collected from two different hospitals. According to the differentiation of gastric adenocarcinoma, the data were divided into four groups: normal group (n = 254), well differentiation group (n = 166), moderately differentiation group (n = 75), and poorly differentiation group (n = 118). The gold standard of differentiation classification was blindly established by two gastrointestinal pathologists. The WSIs were randomly split into a training dataset consisting of 494 images and a testing dataset consisting of 119 images. Within the training set, the WSI count of the normal, well, moderately, and poorly differential groups was 203, 131, 62, and 98 individuals, respectively. Within the test set, the corresponding WSI count was 51, 35, 13, and 20 individuals.</p><p><strong>Results: </strong>The TGMIL model developed for the differential prediction task exhibited remarkable efficiency when considering sensitivity, specificity, and the area under the curve (AUC) values. We also conducted a comparative analysis to assess the efficiency of five other models, namely MIL, CLAM_SB, CLAM_MB, DSMIL, and TransMIL, in classifying the differentiation of gastric cancer. The TGMIL model achieved a sensitivity of 73.33% and a specificity of 91.11%, with an AUC value of 0.86.</p><p><strong>Conclusions: </strong>The hybrid multi-instance learning model TGMIL could accurately classify the differentiation of gastric adenocarcinoma using WSI without the need for labor-intensive and time-consuming manual annotations, which will improve the efficiency and objectivity of diagnosis.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"24 1","pages":"79"},"PeriodicalIF":2.9,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12199488/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144494093","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 deep-learning models incorporating knee alignment information for predicting ground reaction force during walking.","authors":"Tommy Sugiarto, Yi-Jia Lin, Hsiao-Liang Tsai, Chi-Tien Sun, Wei-Chun Hsu","doi":"10.1186/s12938-025-01409-1","DOIUrl":"10.1186/s12938-025-01409-1","url":null,"abstract":"<p><strong>Background: </strong>Wearable sensors combined with deep-learning models are increasingly being used to predict biomechanical variables. Researchers have focused on either simple neural networks or complex pretrained models with multiple layers. In addition, studies have rarely integrated knee alignment information or the side affected by injury as features to improve model predictions. In this study, we compared the performance of selected model architectures, including complex pretrained models, in predicting three-dimensional (3D) ground reaction force (GRF) data during level walking by using data obtained from motion capture systems and wearable accelerometers.</p><p><strong>Results: </strong>Ten deep-learning models for predicting the 3D GRF were developed using motion capture and accelerometer data with or without subject-specific features. Incorporating subject-specific features improved prediction accuracy for all models except the long short-term memory (LSTM) model. A two-dimensional (2D)-CNN-LSTM hybrid model achieved the best results. Established models, such as ResNet50 and Inception, performed better when trained with pretrained ImageNet weights and subject-specific features, underscoring the value of pretrained knowledge and subject-specific information for improving accuracy. However, these models did not outperform the custom hybrid models in predicting time-series 3D GRF data, indicating that larger models do not necessarily perform better for time-series applications but do always have greater computational demands.</p><p><strong>Conclusion: </strong>Incorporating subject-specific features, such as alignment information, enhanced the accuracy of GRF predictions during walking. Complex pretrained models were outperformed by custom hybrid models for time-series 3D GRF prediction during walking. Custom models with lower computational demands and using alignment features are a more efficient and effective choice for applications requiring accurate and resource-efficient predictions.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"24 1","pages":"78"},"PeriodicalIF":2.9,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12186330/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144473978","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":"Emerging technologies in airway management: a narrative review of intubation robotics and anatomical structure recognition algorithms.","authors":"Weixiong Chen, Yu Tian, Yingjie Wang, Lili Feng, Mannan Abdul, Shuangshuang Li, Wenxian Li, Yuan Han","doi":"10.1186/s12938-025-01408-2","DOIUrl":"10.1186/s12938-025-01408-2","url":null,"abstract":"<p><p>In recent years, the medical field has seen significant advancements in the field of robotics and artificial intelligence (AI). However, many healthcare professionals still find these technologies unfamiliar and complex, especially regarding their use during airway management. This review covers the current capabilities of robots and AI in tracheal intubation (TI), providing new insights that advocate for the broader adoption of these technologies to improve airway management. A literature review on robotics and AI in TI was conducted through searches in the PubMed, Web of Science, and IEEE Xplore databases. Drawing on a classification framework derived from expert opinions and existing literature, these studies are categorized into six key stages. Most of these technologies remain in the testing and validation phases, with only a few having reached commercialization. The primary goal of these robotic and AI systems is to enhance the success rate and operational efficiency of intubation while mitigating the persistent shortage of medical resources and supporting telemedicine. However, ongoing attention is required to address challenges such as high costs, a shortage of interdisciplinary talent, and ethical concerns related to medical bias and data security. Robots and AI are beginning to play a significant role in TI. Although many of these technologies remain in the theoretical stage of clinical application, their potential to enhance clinical practice is substantial, provided they are implemented as complementary tools that support rather than substitute the expertise of healthcare professionals. AI-powered robots show great potential as assistive tools for optimizing intubation maneuvers, whereas clinical decision-making (e.g., determining the necessity of intubation) remains under the supervision of physicians.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"24 1","pages":"77"},"PeriodicalIF":2.9,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12186397/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144473924","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":"Research on automatic assessment of the severity of unilateral vocal cord paralysis based on Mel-spectrogram and convolutional neural networks.","authors":"Shuaichi Ma, Wenwen Liao, Yi Zhang, Fan Zhang, Yimiao Wang, Zhiyan Lu, Chen Zhao, Jianbo Yu, Peijie He","doi":"10.1186/s12938-025-01401-9","DOIUrl":"10.1186/s12938-025-01401-9","url":null,"abstract":"<p><strong>Background: </strong>This study aims to develop an AI-powered platform using Mel-spectrogram analysis and convolutional neural networks (CNN) to automate the severity assessment of unilateral vocal fold paralysis (UVCP) through voice analysis, providing an objective basis for individualized clinical treatment plans.</p><p><strong>Methods: </strong>To accurately identify the severity of UVCP, this study developed the CNN model TripleConvNet. Voice samples were collected from 131 healthy individuals and 292 confirmed UVCP patients from the Eye and ENT Hospital of Fudan University. Based on vocal fold compensation function, the patients were divided into three groups: decompensated (84 cases), partially compensated (98 cases), and fully compensated (110 cases). Using Mel-spectrograms and their first- and second-order differential features as inputs, the TripleConvNet model classified patients by severity and was systematically evaluated for its performance in UVCP severity grading tasks.</p><p><strong>Results: </strong>TripleConvNet achieved a classification accuracy of 74.3% in distinguishing between healthy voices and the UVCP decompensated, partially compensated, and fully compensated groups.</p><p><strong>Conclusion: </strong>This study demonstrates the potential of deep learning-based non-invasive voice analysis for precise grading of UVCP severity. The proposed method offers a promising clinical tool to assist physicians in disease assessment and personalized treatment planning.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"24 1","pages":"76"},"PeriodicalIF":2.9,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12181906/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144339809","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":"Prediction of risk factors of plastic bronchitis in children with severe Mycoplasma pneumoniae pneumonia.","authors":"Shiyin Mu, Jia Zhai, Yongsheng Guo, Bing Huang, Yingxue Zou","doi":"10.1186/s12938-025-01410-8","DOIUrl":"10.1186/s12938-025-01410-8","url":null,"abstract":"<p><strong>Background: </strong>Plastic bronchitis (PB) is a rare but potentially life-threatening condition that requires particular attention in pediatric patients, specifically those presenting with severe Mycoplasma pneumoniae pneumonia (SMPP). This study aimed to identify risk factors associated with PB in children with SMPP and develop a comprehensive risk factor scoring system.</p><p><strong>Methods: </strong>A retrospective analysis was conducted on SMPP patients who underwent bronchoscopy between January 2018 and October 2023. Based on bronchoscopic and pathological examination results, patients were categorized into PB (n = 142) and non-PB (n = 274) groups. Clinical manifestations, laboratory data, and imaging findings were analyzed. Risk factors for PB in SMPP children were identified through univariate and multivariate logistic regression analyses. A nomogram model incorporating independent risk factors was developed, and a PB risk factor scoring system was established. Model validation was performed through a prospective validation study.</p><p><strong>Results: </strong>Among 416 SMPP children (197 males, 219 females), mean age at disease onset was 6.9 ± 2.9 years and 6.6 ± 2.8 years in the PB and Non-PB groups, respectively. Multivariate logistic regression analysis identified eight independent predictors of PB in SMPP children: dyspnea, decreased breath sounds, neutrophil-to-lymphocyte ratio (NLR), lactate dehydrogenase (LDH), mean platelet volume to platelet ratio (MPV/PLT), pleural effusion, ≥ 2/3 lobe consolidation, and atelectasis. The nomogram prediction model demonstrated excellent discriminative ability (AUC = 0.92, 95% CI 0.892-0.948, P < 0.005) and strong calibration between predicted and observed outcomes. In the prospective validation cohort (n = 565), the scoring system effectively stratified patients into risk categories: high-risk (71.62% PB incidence), intermediate-risk (59.79%), and low-risk (5.33%), with statistically significant inter-group differences (P < 0.001). The PB group exhibited significantly longer hospitalization durations, extended glucocorticoid treatment periods, higher proportions of glucocorticoid therapy utilization, and a greater frequency of bronchoscopy interventions (≥ 2 sessions) compared to the Non-PB group (all P < 0.05).</p><p><strong>Conclusions: </strong>In this study, we developed and validated a nomogram to PB in children with SMPP. This model serves as a clinically practical tool for early PB identification, enabling physicians to initiate timely interventions and optimize disease management strategies.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"24 1","pages":"75"},"PeriodicalIF":2.9,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12181858/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144339808","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 comprehensive review of heart rate measurement using remote photoplethysmography and deep learning.","authors":"Uday Debnath, Sungho Kim","doi":"10.1186/s12938-025-01405-5","DOIUrl":"10.1186/s12938-025-01405-5","url":null,"abstract":"<p><p>With the widespread availability of consumer-grade cameras, interest in heart rate (HR) measurement using remote photoplethysmography (rPPG) has grown significantly. rPPG is a noninvasive optical technique that uses camera to measure heart rate by analyzing light reflectance due to blood flow changes beneath the skin from any parts of the body, mostly facial regions. However, it faces challenges such as motion artifacts and sensitivity to varying lighting conditions. The rapid advancement of deep learning techniques in recent years has driven numerous studies to integrate these models with rPPG for HR detection in remote health monitoring systems. This study provides a comprehensive review of both conventional approaches and recent developments in rPPG and deep learning algorithms. A comparative analysis highlighted the superior accuracy of deep learning methods over conventional techniques in non-contact HR estimation. Based on a review of 145 articles encompassing different methodologies, signal processing strategies, and deep learning algorithms, our study identifies existing research gaps and explores future research opportunities for real-world applications.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"24 1","pages":"73"},"PeriodicalIF":2.9,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12181896/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144336302","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":"Effect of humeral rotation on rotator cuff strain, loading and kinematics: an in vitro study.","authors":"Inês Santos, Lieselotte Pichler, Matthias F Pietschmann, Mark Tauber, Peter E Müller","doi":"10.1186/s12938-025-01406-4","DOIUrl":"10.1186/s12938-025-01406-4","url":null,"abstract":"<p><strong>Background: </strong>Despite its main function as abductor, the role of the supraspinatus as stabilizer and rotator cannot be neglected. A supraspinatus tear may not only influence humeral head rotation during abduction but also the strength and loading of the acting (intact) rotator cuff muscles. The purpose of this study was to investigate the effect of constrained humeral rotation and elevation on rotator cuff loading, strain and kinematics with intact and torn cuff conditions.</p><p><strong>Methods: </strong>Active humeral elevation until 30° was simulated in twelve fresh-frozen cadaver shoulders with free humeral rotation and blocked humeral rotation. The loading protocol was applied to the intact rotator cuff, and after a 50% and 100% wide (full-thickness) crescent-shaped (n = 6) and reverse L-shaped (n = 6) tears were created in the supraspinatus tendon.</p><p><strong>Results: </strong>Constrained humeral rotation led to an increase in supraspinatus loading force and maximum supraspinatus strain for both tear shapes. Range of motion was significantly reduced in 7 of the 12 specimens due to blocked humeral rotation. In the 100% wide reverse L-shaped tear group, constrained rotation led to an anterior translation of humeral head, in contrast to the posterior translation observed with free rotation.</p><p><strong>Conclusions: </strong>Blocking humeral head rotation leads to an increase in supraspinatus and infraspinatus strains. According to its function as external rotator of the shoulder, the strain in the infraspinatus was higher at the beginning of abduction. However, small rotator cuff tears might not biomechanically result in increased humeral rotation, possibly because the load on the infraspinatus is compensated by the subscapularis.</p><p><strong>Level of evidence: </strong>Basic Science Study; Biomechanics.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"24 1","pages":"74"},"PeriodicalIF":2.9,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12181920/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144336303","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":"Global, regional, and national burden of acute leukemia and its risk factors from 1990 to 2021 and predictions to 2040: findings from the global burden of disease study 2021.","authors":"Xinpu Han, Zhangjun Yun, Zhu Liu, Yuping Si, Shaodan Tian, Yu Zhang, Yubo Qi, Chengyuan Xue, Meichen Cui, Xu Wen, Yayue Zhang, Li Hou","doi":"10.1186/s12938-025-01403-7","DOIUrl":"10.1186/s12938-025-01403-7","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Despite therapeutic advances, acute leukemia (AL) continues to impose a substantial global health burden, with persistently high incidence and mortality rates. Notably, the prediction is that the number of incidence and mortality of acute myeloid leukemia (AML) cases will continue to increase to 184,287.88 and 165,537.59, respectively, by 2040. Using Global Burden of Disease Study (GBD) 2021 data, we assessed the worldwide AL burden from 1990 to 2021, analyzing trends by sex to inform public health strategies.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;To delineate the burden of AL, we reported incidence, prevalence, mortality, and disability-adjusted life year (DALY) rates at global, regional, and national levels, with these estimates including age-standardized rates (ASRs) per 100,000 population and average annual percentage changes (AAPCs) from 1990 to 2021 which were stratified by age, sex, and socio-demographic index (SDI), and computed ASRs and AAPCs using linear regression. Temporal trends were analyzed using an age-period-cohort (APC) model with intrinsic estimation (principal component regression). Risk factor attribution quantified contributions of 21 behavioral, environmental, and metabolic exposures to AL-related DALYs. For inequality assessment, we applied the Slope Index of Inequality and Concentration Index to evaluate absolute and relative disparities in AL burden across regions and countries. Future projections (2040) were modeled via a Bayesian APC framework with Integrated Nested Laplace Approximation. All statistical analyses were performed using R software (version R 4.4.1). The GATHER (Guidelines for Accurate and Transparent Health Estimates Reporting) statement provides a framework to ensure the transparency, reproducibility, and quality of health estimates reporting.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Between 1990 and 2021, global ASRs of AL declined, except for acute lymphoblastic leukemia (ALL) age-standardized prevalence rates (ASPR) (AAPC 0.84, 95% CI 0.59-1.10). Case numbers rose overall, though ALL mortality and DALYs decreased. AML burden correlated positively with SDI (ρ &gt; 0, P &lt; 0.001), with the highest number of incident and mortality cases in Western Europe. For ALL, SDI showed positive associations with ASIR and ASPR (ρ &gt; 0, P &lt; 0.001) but negative correlations with ASMR and ASDALR (ρ &lt; 0, P &lt; 0.001), with East Asia being the most severely affected region. High body mass index (BMI), smoking, and benzene/formaldehyde exposure were key AL DALY risk factors. High BMI and smoking predominate in developed countries, and the proportion of DALYs decreases progressively as SDI decreases. Occupational exposures prevailed in developing countries. Population growth drove most absolute increases. Males bore a greater AL burden. AML risk rose with age, whereas ALL displayed bimodal peaks (&lt; 5 and &gt; 40 years). Notably, while ASRs in AL are projected to decline slightly and the number of ALL to decrease by ","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"24 1","pages":"72"},"PeriodicalIF":2.9,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12150532/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144265198","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":"Construction of a deep learning-based predictive model to evaluate the influence of mechanical stretching stimuli on MMP-2 gene expression levels in fibroblasts.","authors":"Ruozu Xiao, Haowei Zhou, Zhen Shi, Rong Huang, Yuheng Zhang, Jing Li","doi":"10.1186/s12938-025-01399-0","DOIUrl":"10.1186/s12938-025-01399-0","url":null,"abstract":"<p><strong>Background: </strong>Matrix metalloproteinase-2 (MMP-2) secretion homeostasis, governed by the multifaceted interplay of skin stretching, is a pivotal determinant influencing wound healing dynamics. This investigation endeavors to devise an artificial intelligence (AI) prediction framework delineating the modulation of MMP-2 expression under stretching conditions, thereby unravelling profound insights into the mechanobiological orchestration of MMP-2 secretion and fostering novel mechanotherapeutic strategies targeted at MMP-2 modulation.</p><p><strong>Methods: </strong>Employing a bespoke mechanical tensile loading apparatus, diverse mechanical tensile stimuli were administered to fibroblasts, with parameters such as tensile shape and frequency duration constituting the mechanical loading regimen. Furthermore, reverse transcription polymerase chain reaction (RT‒PCR) assays were conducted to measure MMP-2 gene expression levels in fibroblasts subjected to mechanical stretching. Subsequently, the resulting data were partitioned into training and validation cohorts at a 7:3 ratio, facilitating the development of the deep learning (DL) model via a back propagation neural network predicated on the training set. An external validation set was also curated by culling pertinent literature from the PubMed database to assess the predictive ability of the model.</p><p><strong>Results: </strong>Analysis of 336 data points related to MMP-2 gene expression via RT‒PCR corroborated the variability in MMP-2 gene expression levels in response to distinct mechanical stretching regimens. Consequently, a DL model was successfully crafted via the backpropagation algorithm to delineate the impact of mechanical stretching stimuli on MMP-2 gene expression levels. The model, characterized by an R² value of 0.73, evinced a commendable fit with the training data set, elucidating the intricate interplay between the input features and the target variable. Notably, the model exhibited minimal prediction errors, as evidenced by a root mean square error (RMSE) of 0.42 and a mean absolute error (MAE) of 0.28. Furthermore, the model showcased robust generalization capabilities during validation, yielding R² values of 0.70 and 0.71 for the validation and external validation sets, respectively, revealing its predictive accuracy.</p><p><strong>Conclusions: </strong>The DL model fashioned through the backpropagation algorithm adeptly forecasts the impact of mechanical stretching stimuli on MMP-2 gene expression levels in fibroblasts with relative precision. These findings provide a foundation for the modulation of MMP homeostasis via mechanical stretching to expedite the healing of recalcitrant chronic refractory wound (CRW).</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"24 1","pages":"71"},"PeriodicalIF":2.9,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12139300/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144233070","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}