Computers in biology and medicine最新文献

{"title":"Rigorous multiple statistical test unveils combinations of preceding diseases at risk for the development of adult nephrotic syndrome","authors":"Katsuyuki Chida , Tetsuo Ishikawa , Akiko Hanai , Ayaka Hananoe , Yusuke Kashiwagi , Hiroto Hatakeyama , Shinsuke Sakai , Masayuki Mizui , Isao Matsui , Hajime Nagasu , Yoichi Takeuchi , Maki Shinzawa , Ryohei Yamamoto , Tomonori Kimura , Eiryo Kawakami","doi":"10.1016/j.compbiomed.2025.110360","DOIUrl":"10.1016/j.compbiomed.2025.110360","url":null,"abstract":"","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"192 ","pages":"Article 110360"},"PeriodicalIF":7.0,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143941207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Local mean suppression filter for effective background identification in fluorescence images","authors":"Bogdan Kochetov,&nbsp;Shikhar Uttam","doi":"10.1016/j.compbiomed.2025.110296","DOIUrl":"10.1016/j.compbiomed.2025.110296","url":null,"abstract":"<div><div>We present an easy-to-use, nonlinear filter for effective background identification in fluorescence microscopy images with dense and low-contrast foreground. The pixel-wise filtering is based on comparison of the pixel intensity with the mean intensity of pixels in its local neighborhood. The pixel is given a background or foreground label depending on whether its intensity is less than or greater than the mean respectively. Multiple labels are generated for the same pixel by computing mean expression values by varying neighborhood size. These labels are accumulated to decide the final pixel label. We demonstrate that the performance of our filter favorably compares with state-of-the-art image processing, machine learning, and deep learning methods. We present three use cases that demonstrate its effectiveness, and also show how it can be used in multiplexed fluorescence imaging contexts and as a pre-processing step in image segmentation. A fast implementation of the filter is available in Python 3 on <span><span>GitHub</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"192 ","pages":"Article 110296"},"PeriodicalIF":7.0,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143947135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The role of single cell transcriptomics for efficacy and toxicity profiling of chimeric antigen receptor (CAR) T cell therapies","authors":"Moritz Thomas ,&nbsp;Ruben Brabenec ,&nbsp;Lisa Gregor ,&nbsp;David Andreu-Sanz ,&nbsp;Emanuele Carlini ,&nbsp;Philipp Jie Müller ,&nbsp;Adrian Gottschlich ,&nbsp;Donjete Simnica ,&nbsp;Sebastian Kobold ,&nbsp;Carsten Marr","doi":"10.1016/j.compbiomed.2025.110332","DOIUrl":"10.1016/j.compbiomed.2025.110332","url":null,"abstract":"<div><div>CAR T cells are genetically modified T cells that target specific epitopes. CAR T cell therapy has proven effective in difficult-to-treat B cell cancers and is now expanding into hematology and solid tumors. To date, approved CAR therapies target only two specific epitopes on cancer cells. Identifying more suitable targets is challenged by the lack of truly cancer-specific structures and the potential for on-target off-tumor toxicity.</div><div>We analyzed gene expression of potential targets in single-cell data from cancer and healthy tissues. Because safety and efficacy can ultimately only be defined clinically, we selected approved and investigational targets for which clinical trail data are available. We generated atlases using &gt;300,000 cells from 48 patients with follicular lymphoma, multiple myeloma, and B-cell acute lymphoblastic leukemia, and integrated over 3 million cells from 35 healthy tissues, harmonizing datasets from over 300 donors. To contextualize findings, we compared target expression patterns with outcome data from clinical trials, linking target profiles to efficacy and toxicity, and ranked 15 investigational targets based on their similarity to approved ones. Target expression did not significantly correlate with reported clinical toxicities in patients undergoing therapy. This may be attributed to the intricate interplay of patient-specific variables, the limited amount of metadata, and the complexity underlying toxicity. Nevertheless, our study serves as a resource for retrospective and prospective target evaluation to improve the safety and efficacy of CAR therapies.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"192 ","pages":"Article 110332"},"PeriodicalIF":7.0,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143941206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A dynamic model using k-NN algorithm for predicting diabetes and breast cancer","authors":"Hussein A.A. Al-Khamees ,&nbsp;Nor Samsiah Sani ,&nbsp;Ahmed Sileh Gifal ,&nbsp;Luan Xiang Wei Liu ,&nbsp;Mohd Isrul Esa","doi":"10.1016/j.compbiomed.2025.110276","DOIUrl":"10.1016/j.compbiomed.2025.110276","url":null,"abstract":"<div><div>Healthcare remains a critical focus due to its direct impact on human well-being. Diabetes, currently the fastest-growing chronic disease globally, poses severe health risks, including cardiovascular complications and kidney failure. Simultaneously, breast cancer has become the most prevalent cancer among women, particularly those in their 40s, surpassing other types. Early detection and diagnosis of these two diseases remain a substantial challenge, yet they are crucial for reducing mortality rates. Machine learning algorithms emerged as powerful tools in healthcare for disease classification and prediction, with the k-nearest neighbors (k-NN) being one of the most widely used supervised learning algorithm. Different traditional machine learning methods have been proposed, which are heavily specialized for specific datasets. More deeply, traditional k-NN relies on a static k-value, which may not provide optimal results across diverse datasets. This paper proposes a dynamic k-NN model that adjusts ‘k’ value based on local data characteristics, enhancing prediction accuracy. The proposed model is testing on two publicly available datasets; PIMA Diabetes and Breast Cancer Wisconsin (BCW) datasets. Our results are evaluated using different metrics that are; accuracy, precision, recall, F1_score, and execution time. The results of these metrics are as follows; (81.17%, 97.37%), (83.33% 100%), (54.55%, 86.05%), and (65.93%, 92.5%) for PIMA and BCW datasets respectively. These results demonstrate that the proposed model outperformed several state-of-the-art models. Thus, further highlighting its effectiveness and efficiency in medical data classification.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"192 ","pages":"Article 110276"},"PeriodicalIF":7.0,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143941201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Individual thigh muscle and proximal femoral features predict displacement in femoral neck Fractures: An AI-driven CT analysis","authors":"Jun-Il Yoo ,&nbsp;Hyeon Su Kim ,&nbsp;Deog-Yoon Kim ,&nbsp;Dong-Won Byun ,&nbsp;Yong-Chan Ha ,&nbsp;Yong-Kyun Lee","doi":"10.1016/j.compbiomed.2025.110307","DOIUrl":"10.1016/j.compbiomed.2025.110307","url":null,"abstract":"<div><h3>Introduction</h3><div>Hip fractures, particularly among the elderly, impose a significant public health burden due to increased morbidity and mortality. Femoral neck fractures, commonly resulting from low-energy falls, can lead to severe complications such as avascular necrosis, and often necessitate total hip arthroplasty. This study harnesses AI to enhance musculoskeletal assessments by performing automatic muscle segmentation on whole thigh CT scans and detailed cortical measurements using the StradView program. The primary aim is to improve the prediction and prevention of severe femoral neck fractures, ultimately supporting more effective rehabilitation and treatment strategies.</div></div><div><h3>Methods</h3><div>This study measured anatomical features from whole thigh CT scans of 60 femoral neck fracture patients. An AI-driven individual muscle segmentation model (a dice score of 0.84) segmented 27 muscles in the thigh region, to calculate muscle volumes. Proximal femoral bone parameters were measured using StradView, including average cortical thickness, inner density and FWHM at four regions. Correlation analysis evaluated relationships between muscle features, cortical parameters, and fracture displacement. Machine learning models (Random Forest, SVM and Multi-layer Perceptron) predicted displacement using these variables.</div></div><div><h3>Results</h3><div>Correlation analysis showed significant associations between femoral neck displacement and trabecular density at the femoral neck/intertrochanter, as well as volumes of specific thigh muscles such as the Tensor fasciae latae. Machine learning models using a combined feature set of thigh muscle volumes and proximal femoral parameters performed best in predicting displacement, with the Random Forest model achieving an F1 score of 0.91 and SVM model 0.93.</div></div><div><h3>Conclusion</h3><div>Decreased volumes of the Tensor fasciae latae, Rectus femoris, and Semimembranosus muscles, coupled with reduced trabecular density at the femoral neck and intertrochanter, were significantly associated with increased fracture displacement. Notably, our SVM model—integrating both muscle and femoral features—achieved the highest predictive performance. These findings underscore the critical importance of muscle strength and bone density in rehabilitation planning and highlight the potential of AI-driven predictive models for improving clinical outcomes in femoral neck fractures.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"192 ","pages":"Article 110307"},"PeriodicalIF":7.0,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new braided model of the Amulet Amplatzer for accurate simulations of left atrial appendage occlusion procedures","authors":"Rafizul Islam Md ,&nbsp;Matthew T. Lee ,&nbsp;Andrew C. Cook ,&nbsp;Jonathan Weir-McCall ,&nbsp;Claire A. Martin ,&nbsp;Thomas W. Peach ,&nbsp;Gaetano Burriesci ,&nbsp;Giorgia M. Bosi","doi":"10.1016/j.compbiomed.2025.110355","DOIUrl":"10.1016/j.compbiomed.2025.110355","url":null,"abstract":"<div><div>Atrial Fibrillation (AF) is a cardiac disease altering the human heart rate. It is posing an increasing burden to society, with complications that lead to stroke and ischemic events from thromboembolisms, originating in the left atrial appendage (LAA). Percutaneous LAA occlusion (LAAO) is becoming an increasingly adopted preventive treatment option due to its minimally invasive nature. However, this treatment faces complex challenges: the heterogeneity of LAA morphologies limits the pre-operative planning and several procedures are associated with peri-device leakage from malposition and device-related thrombi. One of the two most commonly deployed LAAO devices (LAAODs) is the Amulet Amplatzer (AA), a mesh-like pacifier device. In-silico models have demonstrated their potential to serve as supporting tools for clinical planning, providing insight able to enhance the efficacy and safety of the intervention. Most of the computational studies approximate the AA to a closed surface model. In this work, we aimed to develop a more realistic and detailed structural model of the AA, capturing the mesh of wires. Experimental tests on the physical device were conducted to compare the behaviour of simplified closed surface models and the newly developed braided geometry. The results have demonstrated how closed surface models of the AA fail to capture the real deformation mechanism of the physical device. Conversely, the more realistic braided characterisation mimics more closely the changes in shape of the physical AA, by capturing the change in angles of the wires. Finally, the virtual deployment of the intertwined model into a patient-specific LAA resulted in a configuration similar to the clinically implanted AA.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"192 ","pages":"Article 110355"},"PeriodicalIF":7.0,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143934963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A biomechanical assessment of dental implant stability under the effect of changes in bone remodeling factors and implant elastic modulus","authors":"Amirhossein Jafariandehkordi ,&nbsp;Zahra Jafariandehkordi","doi":"10.1016/j.compbiomed.2025.110318","DOIUrl":"10.1016/j.compbiomed.2025.110318","url":null,"abstract":"<div><h3>Background and objective</h3><div>The simulation of the biological phenomena can assist physicians in development of the advanced therapeutic plans and methods based on the computer model results and engineering analysis. The application of these models can predict and evaluate the medical experiment output by removing the barriers of technical and practical limitations, and expensive laboratory equipment. This provides the scholars with the preview and insight to design practical experiments and save cost, energy, and time. The present study aimed to provide a calculational, finite element-based simulation of the mandible bone's remodeling under the osteoporosis condition to quantify and evaluate the stability of a dental implant using the indicator implant stability quotient (ISQ). The effects of the significant chemical factors involved in bone remodeling including osteoprotegerin (OPG), transforming growth factor beta (TGF-β), and the parathyroid hormone (PTH) as well as the effect of the implant's elastic modulus were also included in the model. The literature lacks a predictive combinatory model considering the comparative effects of these chemicals on the implant stability which was addressed by this study.</div></div><div><h3>Methods</h3><div>A 3D geometry of the mandible portion assembled with a Straumann implant geometry was developed using a CT image dataset. The model geometries were imported to finite element software for the definition of the material properties, boundary conditions, constraints, and mathematical relations. The model's basic parameters were based on other published works. A mesh of the geometry was created and bone remodeling partial differential equations were incorporated. The mastication force was assumed constant and the damping factor was considered zero in our study. Finally, the calculated resonance frequencies of the implant-mandible portion assembly were utilized to obtain and assess the implant stability quotient.</div></div><div><h3>Results</h3><div>The results showed that a 6 % increase in the dosage rates of TGF-β had the highest increase in ISQ values to 24.28 % from the baseline and the implant elastic modulus of 12.5 GPa caused a 21.55 % relative growth in the ISQ value of the baseline. The accuracy of the results was tested by the mesh convergence and sensitivity studies.</div></div><div><h3>Conclusions</h3><div>Based on the results, TGF-β had the most significant effect on the growth of ISQ, and the implant elastic modulus remarkably increased ISQ values when its value was close to the average values of the mandible equivalent elastic modulus.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"192 ","pages":"Article 110318"},"PeriodicalIF":7.0,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational prediction of Gore Excluder conformable endoprosthesis in the infrarenal aortic neck: results of the ACSSim study","authors":"Lucie Derycke ,&nbsp;Stéphane Avril ,&nbsp;Virgil Drouhard ,&nbsp;Jean-Noël Albertini ,&nbsp;Antoine Millon","doi":"10.1016/j.compbiomed.2025.110228","DOIUrl":"10.1016/j.compbiomed.2025.110228","url":null,"abstract":"<div><h3>Background and objective</h3><div>The Gore® Excluder® Conformable (EXCC) device offers a less invasive and less risky alternative to open surgery and complex endovascular repair of abdominal aortic aneurysms for patients with hostile aortic neck anatomies. Indeed, its specific structure has sufficient conformability to prevent proximal sealing complications. Nevertheless, its mechanical behavior is more complex than the one of standard devices, and in complex anatomies, its deployment in the proximal neck of the aortic aneurysm remains difficult to predict. The aim of the present study was to develop and validate a digital twin of EXCC deployment that could accurately predict proximal endoprosthesis sealing.</div></div><div><h3>Methods</h3><div>Twenty patients who underwent endovascular aortic aneurysm repair with the EXCC device for complex anatomies in one aortic center were selected. Endoprosthesis deployment in each aorta was simulated by the finite element (FE) method. We compared the positions predicted by the FE simulations with post-operative computed tomography angiography (CTA), focusing on the proximal axis angle, the stent center positions and stent-rings diameters through a principal component analysis.</div></div><div><h3>Results</h3><div>A successful FE simulation of endoprosthesis deployment could be performed for each of the twenty patients. Relative diameter and vector mean deviations were 4.65 ± 3.85 % and 3.00 ± 1.41 mm, respectively. Axis angle mean deviation was 10.64 ± 5.09°. Outputs show satisfying agreement between numerical simulations and post-operative CTA. Mean proximal apposition was 81.64 ± 11.35 %. Minimal and maximal endoprosthesis appositions were 54.27 % and 95.11 %, respectively.</div></div><div><h3>Conclusions</h3><div>The FE model predicted accurately stent-graft positions in 20 patients presenting complex anatomies. High endoprosthesis appositions were observed. This shows the potential of computer simulation to anticipate endoprosthesis proximal sealing complications such as endoleaks and migration before intervention.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"192 ","pages":"Article 110228"},"PeriodicalIF":7.0,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143941202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Suicide ideation detection based on documents dimensionality expansion","authors":"Nima Esmi ,&nbsp;Asadollah Shahbahrami ,&nbsp;Georgi Gaydadjiev ,&nbsp;Peter de Jonge","doi":"10.1016/j.compbiomed.2025.110266","DOIUrl":"10.1016/j.compbiomed.2025.110266","url":null,"abstract":"<div><div>Accurate and secure classifying informal documents related to mental disorders is challenging due to factors such as informal language, noisy data, cultural differences, personal information and mixed emotions. Conventional deep learning models often struggle to capture patterns in informal text, as they miss long-range dependencies, explain words and phrases literally, and have difficulty processing non-standard inputs like emojis. To address these limitations, we expand data dimensionality, transforming and fusing textual data and signs from a 1D to a 2D space. This enables the use of pre-trained 2D CNN models, such as AlexNet, Restnet-50, and VGG-16 removing the need to design and train new models from scratch. We apply this approach to a dataset of social media posts to classify informal documents as either related to suicide or non-suicide content. Our results demonstrate high classification accuracy, exceeding 99%. In addition, our 2D visual data representation conceals individual private information and helps explainability.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"192 ","pages":"Article 110266"},"PeriodicalIF":7.0,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The automatic pelvic screw corridor planning for intact pelvises based on deep learning deformable registration","authors":"Fujiao Ju ,&nbsp;Xudong Chai ,&nbsp;Jingxin Zhao ,&nbsp;Mingjie Dong","doi":"10.1016/j.compbiomed.2025.110304","DOIUrl":"10.1016/j.compbiomed.2025.110304","url":null,"abstract":"<div><div>Percutaneous screw fixation technique in pelvic trauma surgery is an extremely challenging operation that typically requires a trial-and-error insertion process under the guidance of continuous intraoperative X-ray. This process can be simplified by utilizing surgical navigation systems. Understanding the complexity of the intraosseous pelvis corridor is essential for establishing the optimal screw corridor, which further facilitates preoperative planning and intraoperative application. Traditional screw corridor search algorithms necessitate traversing the entrance and exit areas of the screw and calculating the distance from the corridor axis to the bone surface to ascertain the location of the screw. This process is computationally complex, and manual measurement by the physician is time consuming, labor intensive, and empirically dependent. In this study, we propose an automated planning algorithm for pelvic screw corridors based on deep learning deformable registration technology, which can efficiently and accurately identify the optimal screw corridors. Compared to traditional methods, the innovations of this study include: (1) the introduction of corridor safety range constraints on screw positioning, which enhances search efficiency; (2) the application of deep learning deformable registration to facilitate the automatic annotation of the screw entrance and exit areas, as well as the safety range of the corridor; and (3) the development of a highly efficient algorithm for optimal corridor searching, quickly determining the corridor without traversing the entrance and exit areas and enhancing efficiency via a vector-based diameter calculation method. The whole framework of the algorithm consists of three key components: atlas generation module, deformable registration and optimal corridor searching strategy. In the experiments, we test the performance of the proposed algorithm on 198 intact pelvises for calculating the optimal corridor of anterior column corridor and S1 sacroiliac screws. The results show that the new algorithm can increase the corridor diameter by 2.1%–3.3% compared to manual measurements, while significantly reducing the average time from 1038s and 3398s to 18.9s and 26.7s on anterior column corridor and S1 sacroiliac corridor, respectively, compared to the traditional screw searching algorithm. This demonstrates the advantages of the algorithm in terms of efficiency and accuracy. However, the current method is validated only on intact pelvises; further research is required for pelvic fracture scenarios.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"192 ","pages":"Article 110304"},"PeriodicalIF":7.0,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143941200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}