Bioengineering最新文献

{"title":"Pressure-Relief Effect of Post-Op Shoes Depends on Correct Usage While Walking.","authors":"Claudia Döhner, Christian Soost, Sam Steinhöfer, Jan A Graw, Christopher Bliemel, Artur Barsumyan, Rene Burchard","doi":"10.3390/bioengineering12050489","DOIUrl":"10.3390/bioengineering12050489","url":null,"abstract":"<p><p>Post-op shoes (POSs) are commonly used after forefoot surgery to protect the surgical site. However, there are insufficient data on their impact on forefoot load during the rollover phase of walking. This study aims to analyze the effects of a commonly used POS on plantar pressures under the forefoot and to assess whether improper usage could affect pressure patterns. Sixteen healthy volunteers underwent three different walking tests on a straight tartan track. The test setting included walking barefoot, as well as normal walking and a modified heel-accentuated \"limping\" gait while wearing a common POS. The pressure distribution over the forefoot regions of interest was measured using sensor insoles and a pressure-measuring plate on the ground. Results show that only the heel-accentuated \"limping\" gait in the POS led to a significant reduction in pressure values over all anatomical regions compared to the normal barefoot gait. Furthermore, higher pressure values were found over the lesser toes during normal walking in the POS compared to normal barefoot walking. The findings highlight that the protective function of a POS relies on proper use, specifically the correct gait pattern. If used incorrectly, POS may even have unfavorable effects on the pressure on the operated forefoot and possibly even increase the risk of delayed healing or complications in comparison to barefoot walking. Therefore, strategies such as patient training in proper walking techniques should be incorporated into postoperative care.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 5","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12109111/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144156292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging Function Intersectionality and Multi-Modal Cerebrovascular Reactivity Measures for the Derivation of Individualized Intracranial Pressure Thresholds in Acute Traumatic Neural Injury.","authors":"Kevin Y Stein, Logan Froese, Alwyn Gomez, Amanjyot Singh Sainbhi, Nuray Vakitbilir, Abrar Islam, Tobias Bergmann, Frederick A Zeiler","doi":"10.3390/bioengineering12050485","DOIUrl":"10.3390/bioengineering12050485","url":null,"abstract":"<p><p>It has been proposed that subject-specific intracranial pressure (ICP) thresholds can be feasibly derived using the relationship between cerebrovascular reactivity and ICP. Such individualized intracranial pressure (iICP) thresholds have been suggested to have more robust associations with long-term outcomes of post-traumatic brain injury (TBI) than current guideline-based thresholds. However, both existing works have derived iICP using solely the pressure reactivity index (PRx) and a threshold of +0.20. Therefore, the goal of this study was to validate prior works and compare various cerebrovascular reactivity indices for their utility in deriving iICP. A custom iICP derivation algorithm was developed. Then, using existing archived human datasets from the Winnipeg Acute TBI Database, iICP thresholds were derived using three cerebrovascular reactivity indices: PRx, the pulse amplitude index (PAx), and the RAC (correlation (R) between the pulse amplitude of ICP (A) and cerebral perfusion pressure (C)). The yield of iICP derivation was found to vary significantly, depending on the cerebrovascular reactivity index and threshold used. A logistic regression analysis was then used to evaluate and compare the abilities of each index-derived iICP to predict the 6-month outcome. Among all index-threshold combinations tested, only PRx > 0 was able to produce an iICP that was able to outperform guideline-based ICP thresholds. PRx-based iICP seems to be superior to both PAx- and RAC-based iICP for predicting long-term outcomes. However, further work is needed to identify the ideal cerebrovascular reactivity thresholds for iICP derivation.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 5","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12109465/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144156330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning Prediction of Left Ventricular Assist Device Thrombosis from Acoustic Harmonic Power.","authors":"Kent D Carlson, Dan Dragomir-Daescu, Barry A Boilson","doi":"10.3390/bioengineering12050484","DOIUrl":"10.3390/bioengineering12050484","url":null,"abstract":"<p><p>Left ventricular assist device (LVAD) thrombosis typically presents late and may have devastating consequences for patients. While LVAD pump thrombosis is uncommon with current pump designs, many patients worldwide remain supported with previous generations of LVADs, including the HeartWare device (HVAD). Researchers have focused on investigating the acoustic signatures of LVADs to enable earlier detection and treatment of this condition. This study explored the use of machine learning algorithms to predict thrombosis from harmonic power values determined from the acoustic signatures of a cohort of HVAD patients (<i>n</i> = 11). The current dataset was too small to develop a predictive model for new data, but exhaustive cross validation indicated that machine learning models using the first two or the first three harmonic power values both resulted in reasonable prediction accuracy of the thrombosis outcome. Furthermore, when principal component analysis (PCA) was applied to the harmonic power variables from these promising models, the use of the resulting PCA variables in machine learning models further increased the thrombosis outcome prediction accuracy. K-nearest neighbor (KNN) models gave the best predictive accuracy for this dataset. Future work with a larger HVAD recording dataset is necessary to develop a truly predictive model of HVAD thrombosis. Such a predictive model would provide clinicians with a marker to detect HVAD thrombosis based directly on pump performance, to be used along with current clinical markers.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 5","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12109467/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144156332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Computational Study on Renal Artery Anatomy in Patients Treated with Fenestrated or Branched Endovascular Aneurysm Repair.","authors":"Yuzhu Wang, Yuna Sang, Wendong Li, Minjie Zhou, Yushun Zhao, Xiaodong He, Chao Wang, Xiaoqiang Li, Zhao Liu","doi":"10.3390/bioengineering12050482","DOIUrl":"10.3390/bioengineering12050482","url":null,"abstract":"<p><p>(1) Background: Renal artery occlusion after F/B EVAR for abdominal aortic aneurysm is a serious complication that may require re-intervention, and understanding the hemodynamic mechanisms by which it occurs is essential to optimize the surgical procedure. (2) Methods: We used computational fluid dynamics (CFD) to analyze the impact of various parameters on blood flow. Theoretical vascular models were constructed based on the common dimensions and angles of aortic stents and branch arteries in clinical practice. Actual case models were constructed from CT image data of six patients treated with F/B-EVAR. Data were collected for analysis after simulation and calculation by FLUENT software. (3) Results: Theoretical model simulations showed that a larger tilt angle of the branch stent, smaller branch entry depth, and larger branch stent diameter were beneficial for blood flow. In the case models, a significant difference in the tilt angle of the renal artery stents was observed between the high- and low-flow groups, while the differences in entry depth and branch stent diameter were not significant. Occluded renal arteries had lower WSS values than patent ones. (4) Conclusions: This study offers valuable guidance for optimizing stent placement in F/B EVAR to mitigate renal artery occlusion risk.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 5","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12109362/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144156087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Engagement and Stress Concentration Evaluation of a Novel Two-Part Compression Screw: A Preliminary Finite Element Analysis.","authors":"Chia-Hao Hsu, Chih-Kuang Wang, Yan-Hsiung Wang, Sung-Yen Lin, Cheng-Chang Lu, Yin-Chih Fu, Hsuan-Ti Huang, Chung-Hwan Chen, Pei-Hsi Chou","doi":"10.3390/bioengineering12050483","DOIUrl":"10.3390/bioengineering12050483","url":null,"abstract":"<p><p><b>Background/Objectives:</b> This novel compression screw design offers potential benefits due to its two-part structure and can be used for various bone types, much like a conventional single-piece compression screw. However, full engagement may not always occur after final compression in clinical practice. This study aimed to verify the hypothesized optimal mechanical strength when the two parts are nearly fully combined and to determine a recommended engagement range based on stress distribution and concentration using finite element analysis. <b>Methods:</b> Ten models representing different combinations of the two screw parts (ranging from 10% to 100% of the engagement length, at 10% intervals) were simulated to determine the acceptable engagement percentage. Pull-out and bending load simulations were performed using finite element models. Extreme clinical loading conditions were simulated, including 1000 N pull-out forces and a 1 Nm bending moment at the screw head. <b>Results:</b> Finite element analysis revealed two stress concentration points. The pull-out load simulation showed that combinations with 100% engagement merged the two stress concentrations into one without force superposition, while combinations with less than 30% engagement should be avoided. In the bending load simulation, higher stress was observed for combinations with less than 90% engagement. A lower level of engagement increases the bending moment, which might be the major factor affecting the von Mises stress. <b>Conclusions:</b> Surgeons should be instructed on how to use the screw correctly and select the most appropriate screw size or length for the two parts to achieve an effective combination. Excessive bending or pull-out forces, or improper use with poor combinations, may cause the middle interval to strip or the screw to break or pull out. An engagement of more than 90% is recommended, while less than 30% is considered dangerous. This study provides biomechanical insights into this novel two-part screw design and its important clinical implications.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 5","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12108918/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144156121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modelling the Ki67 Index in Synthetic HE-Stained Images Using Conditional StyleGAN Model.","authors":"Lucia Piatriková, Katarína Tobiášová, Andrej Štefák, Dominika Petríková, Lukáš Plank, Ivan Cimrák","doi":"10.3390/bioengineering12050476","DOIUrl":"10.3390/bioengineering12050476","url":null,"abstract":"<p><p>Hematoxylin and Eosin (HE) staining is the gold standard in histopathological examination of cancer tissue, representing the first step towards cancer diagnosis. The second step is a series of immunohistochemical stainings, including cell proliferation markers called the Ki67 index. Deep learning models offer promising solutions for improving medical diagnostics, while generative models provide additional explainability of predictive models that is essential for their adoption in clinical practice. Our previous work introduced a novel approach that utilises a conditional StyleGAN model for generating HE-stained images conditioned on the Ki67 index. This study proposes to employ this model for generating sequences of HE-stained images reflecting varying Ki67 index values. Sequences enable exploration of hidden relationships between HE and Ki67 staining and can enhance the explainability of predictive models, e.g., by generating counterfactual examples. While our previous research focused on assessing the quality of generated HE images, this study extends that work by evaluating the model's ability to capture Ki67-related variations in HE-stained images. Additionally, expert pathologists evaluated generated sequences and proposed criteria for assessing their relevance. Our findings demonstrate the potential of the conditional StyleGAN model as part of an explainable framework for analysing and predicting immunohistochemical information from HE-stained images. Results highlight the relevance of generative models in histopathology and their potential applications in cancer progression analysis.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 5","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12109013/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144156144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 Approaches for Geographic Atrophy Segmentation: A Systematic Review and Meta-Analysis.","authors":"Aikaterini Chatzara, Eirini Maliagkani, Dimitra Mitsopoulou, Andreas Katsimpris, Ioannis D Apostolopoulos, Elpiniki Papageorgiou, Ilias Georgalas","doi":"10.3390/bioengineering12050475","DOIUrl":"10.3390/bioengineering12050475","url":null,"abstract":"<p><p>Geographic atrophy (GA) is a progressive retinal disease associated with late-stage age-related macular degeneration (AMD), a significant cause of visual impairment in senior adults. GA lesion segmentation is important for disease monitoring in clinical trials and routine ophthalmic practice; however, its manual delineation is time-consuming, laborious, and subject to inter-grader variability. The use of artificial intelligence (AI) is rapidly expanding within the medical field and could potentially improve accuracy while reducing the workload by facilitating this task. This systematic review evaluates the performance of AI algorithms for GA segmentation and highlights their key limitations from the literature. Five databases and two registries were searched from inception until 23 March 2024, following the PRISMA methodology. Twenty-four studies met the prespecified eligibility criteria, and fifteen were included in this meta-analysis. The pooled Dice similarity coefficient (DSC) was 0.91 (95% CI 0.88-0.95), signifying a high agreement between the reference standards and model predictions. The risk of bias and reporting quality were assessed using QUADAS-2 and CLAIM tools. This review provides a comprehensive evaluation of AI applications for GA segmentation and identifies areas for improvement. The findings support the potential of AI to enhance clinical workflows and highlight pathways for improved future models that could bridge the gap between research settings and real-world clinical practice.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 5","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12108927/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144156245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection of Sleep Posture via Humidity Fluctuation Analysis in a Sensor-Embedded Pillow.","authors":"Won-Ho Jun, Youn-Sik Hong","doi":"10.3390/bioengineering12050480","DOIUrl":"10.3390/bioengineering12050480","url":null,"abstract":"<p><p>This study presents a novel method for detecting sleep posture changes-specifically tossing and turning-by monitoring variations in humidity using an array of humidity sensors embedded at regular intervals within a memory-foam pillow. Unlike previous approaches that rely primarily on temperature or pressure sensors, our method leverages the observation that humidity fluctuations are more pronounced during movement, enabling the more sensitive detection of posture changes. We demonstrate that dynamic patterns in humidity data correlate strongly with physical motion during sleep. To identify these transitions, we applied the Pruned Exact Linear Time (PELT) algorithm, which effectively segmented the time series based on abrupt changes in humidity. Furthermore, we converted humidity fluctuation curves into image representations and employed a transfer-learning-based model to classify sleep postures, achieving accurate recognition performance. Our findings highlight the potential of humidity sensing as a reliable modality for non-invasive sleep monitoring. In this study, we propose a novel method for detecting tossing and turning during sleep by analyzing changes in humidity captured by a linear array of sensors embedded in a memory foam pillow. Compared to temperature data, humidity data exhibited more significant fluctuations, which were leveraged to track head movement and infer sleep posture. We applied a rolling smoothing technique and quantified the cumulative deviation across sensors to identify posture transitions. Furthermore, the PELT algorithm was utilized for precise change-point detection. To classify sleep posture, we converted the humidity time series into images and implemented a transfer learning model using a Vision Transformer, achieving a classification accuracy of approximately 96%. Our results demonstrate the feasibility of a sleep posture analysis using only humidity data, offering a non-intrusive and effective approach for sleep monitoring.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 5","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12108824/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144156143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bioengineered Approaches for Esophageal Regeneration: Advancing Esophageal Cancer Therapy.","authors":"Jae-Seok Kim, Hyoryung Nam, Eun Chae Kim, Hun-Jin Jeong, Seung-Jae Lee","doi":"10.3390/bioengineering12050479","DOIUrl":"10.3390/bioengineering12050479","url":null,"abstract":"<p><p>Esophageal cancer (EC) is the eighth leading cause of cancer-related deaths globally, largely due to its late-stage diagnosis and aggressive progression. Esophagectomy remains the primary treatment, typically requiring organ-based reconstruction techniques such as gastric pull-up or colonic interposition. However, these reconstruction methods often lead to severe complications, significantly reducing the quality of life of patients. To address these limitations, tissue engineering has emerged as a promising alternative, offering bioengineered patch-type and tubular-type scaffolds designed to restore both structural integrity and functional regeneration. Recent advancements in three-dimensional (3D) biofabrication-including 3D bioprinting, electrospinning, and other cutting-edge techniques-have facilitated the development of patient-specific constructs with improved biocompatibility. Despite significant advancements, critical challenges persist in achieving mechanical durability, multilayered cellular organization, and physiological resilience post-transplantation. Ongoing research continues to address these limitations and enhance clinical applicability. Therefore, this review aims to examine recent advancements in esophageal tissue engineering, with a focus on key biofabrication techniques, preclinical animal models, and the major translational challenges that must be addressed for successful clinical application.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 5","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12109178/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144156219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancements in Medical Radiology Through Multimodal Machine Learning: A Comprehensive Overview.","authors":"Imran Ul Haq, Mustafa Mhamed, Mohammed Al-Harbi, Hamid Osman, Zuhal Y Hamd, Zhe Liu","doi":"10.3390/bioengineering12050477","DOIUrl":"10.3390/bioengineering12050477","url":null,"abstract":"<p><p>The majority of data collected and obtained from various sources over a patient's lifetime can be assumed to comprise pertinent information for delivering the best possible treatment. Medical data, such as radiographic and histopathology images, electrocardiograms, and medical records, all guide a physician's diagnostic approach. Nevertheless, most machine learning techniques in the healthcare field emphasize data analysis from a single modality, which is insufficiently reliable. This is especially evident in radiology, which has long been an essential topic of machine learning in healthcare because of its high data density, availability, and interpretation capability. In the future, computer-assisted diagnostic systems must be intelligent to process a variety of data simultaneously, similar to how doctors examine various resources while diagnosing patients. By extracting novel characteristics from diverse medical data sources, advanced identification techniques known as multimodal learning may be applied, enabling algorithms to analyze data from various sources and eliminating the need to train each modality. This approach enhances the flexibility of algorithms by incorporating diverse data. A growing quantity of current research has focused on the exploration of extracting data from multiple sources and constructing precise multimodal machine/deep learning models for medical examinations. A comprehensive analysis and synthesis of recent publications focusing on multimodal machine learning in detecting diseases is provided. Potential future research directions are also identified. This review presents an overview of multimodal machine learning (MMML) in radiology, a field at the cutting edge of integrating artificial intelligence into medical imaging. As radiological practices continue to evolve, the combination of various imaging and non-imaging data modalities is gaining increasing significance. This paper analyzes current methodologies, applications, and trends in MMML while outlining challenges and predicting upcoming research directions. Beginning with an overview of the different data modalities involved in radiology, namely, imaging, text, and structured medical data, this review explains the processes of modality fusion, representation learning, and modality translation, showing how they boost diagnosis efficacy and improve patient care. Additionally, this review discusses key datasets that have been instrumental in advancing MMML research. This review may help clinicians and researchers comprehend the spatial distribution of the field, outline the current level of advancement, and identify areas of research that need to be explored regarding MMML in radiology.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 5","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12108733/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144156140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}