Bioengineering最新文献

{"title":"CXR-Seg: A Novel Deep Learning Network for Lung Segmentation from Chest X-Ray Images.","authors":"Sadia Din, Muhammad Shoaib, Erchin Serpedin","doi":"10.3390/bioengineering12020167","DOIUrl":"10.3390/bioengineering12020167","url":null,"abstract":"<p><p>Over the past decade, deep learning techniques, particularly neural networks, have become essential in medical imaging for tasks like image detection, classification, and segmentation. These methods have greatly enhanced diagnostic accuracy, enabling quicker identification and more effective treatments. In chest X-ray analysis, however, challenges remain in accurately segmenting and classifying organs such as the lungs, heart, diaphragm, sternum, and clavicles, as well as detecting abnormalities in the thoracic cavity. Despite progress, these issues highlight the need for improved approaches to overcome segmentation difficulties and enhance diagnostic reliability. In this context, we propose a novel architecture named CXR-Seg, tailored for semantic segmentation of lungs from chest X-ray images. The proposed network mainly consists of four components, including a pre-trained EfficientNet as an encoder to extract feature encodings, a spatial enhancement module embedded in the skip connection to promote the adjacent feature fusion, a transformer attention module at the bottleneck layer, and a multi-scale feature fusion block at the decoder. The performance of the proposed CRX-Seg was evaluated on four publicly available datasets (MC, Darwin, and Shenzhen for chest X-rays, and TCIA for brain flair segmentation from MRI images). The proposed method achieved a Jaccard index, Dice coefficient, accuracy, sensitivity, and specificity of 95.63%, 97.76%, 98.77%, 98.00%, and 99.05%on MC; 91.66%, 95.62%, 96.35%, 95.53%, and 96.94% on V7 Darwin COVID-19; and 92.97%, 96.32%, 96.69%, 96.01%, and 97.40% on the Shenzhen Tuberculosis CXR Dataset, respectively. Conclusively, the proposed network offers improved performance in comparison with state-of-the-art methods, and better generalization for the semantic segmentation of lungs from chest X-ray images.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 2","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11851456/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143498273","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":"Comparative Study on Three Different Designs of Locking Mechanisms in Total Knee Arthroplasty.","authors":"Byung Woo Cho, Hyoung-Taek Hong, Yong-Gon Koh, Kwan Kyu Park, Kyoung-Tak Kang","doi":"10.3390/bioengineering12020169","DOIUrl":"10.3390/bioengineering12020169","url":null,"abstract":"<p><p>The locking mechanism of the fixed-bearing tibial insert is a crucial factor in total knee arthroplasty. Previous studies have predominantly been retrieval-based, with no research examining the forces required for disassembly and assembly based on the design of the tibial insert's locking mechanism. This study aimed to measure the disassembly and assembly forces of three different locking mechanism designs. Group 1 featured a dovetail design, Group 2 had a peripheral rim design, and Group 3 combined a snap-fit mechanism with a dovetail design. Among the groups, Group 1 exhibited the highest disassembly force (379 ± 42 N), followed by Group 3 (342 ± 58) and then Group 2 (269 ± 18). Similarly, Group 1 also demonstrated the highest assembly force (71 ± 3); however, Group 3 showed a lower assembly force (48.7 ± 2.1) compared to Group 2 (49.7 ± 1.5). These results suggest that design modifications can produce mechanisms requiring minimal assembly force while maintaining strong resistance to disassembly. Due to its snap-pit structure, Group 3 exhibited the lowest assembly force while utilizing the dovetail mechanism to demonstrate a strong disassembly force. The rigorous analysis and robust methodology employed in this study ensure the reliability of the findings, which can serve as a reference for future research and development in this field.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 2","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11852057/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143498119","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":"Microwave-Assisted Optimization of Polyvinyl Alcohol Cryogel (PVA-C) Manufacturing for MRI Phantom Production.","authors":"Ivan Vogt, Martin Volk, Emma-Luise Kulzer, Janis Seibt, Maciej Pech, Georg Rose, Oliver S Grosser","doi":"10.3390/bioengineering12020171","DOIUrl":"10.3390/bioengineering12020171","url":null,"abstract":"<p><strong>Objectives: </strong>Anthropomorphic phantoms offer a promising solution to minimize animal testing, enable medical training, and support the efficient development of medical devices. The adjustable mechanical, biochemical, and imaging properties of the polyvinyl alcohol cryogel (PVA-C) make it an appropriate phantom material for mimicking soft tissues. Conventional manufacturing (CM) of aqueous solutions requires constant stirring, using a heated water bath, and monitoring.</p><p><strong>Methods: </strong>To explore potential improvements in the dissolution of PVA crystals in water, a microwave-based manufacturing method (MWM) was employed. Samples created using CM and MWM (<i>n</i> = 14 each) were compared. Because PVA-C is a multifunctional phantom material (e.g., in magnetic resonance imaging (MRI)), its MRI properties (T1/T2 relaxation times) and elasticity were determined.</p><p><strong>Results: </strong>T1 relaxation times did not significantly differ between the two methods (<i>p</i> = 0.3577), whereas T2 and elasticity for the MWM were significantly higher than those for the CM (<i>p</i> < 0.001). The MWM reduced the production time by 11% and decreased active user involvement by 93%.</p><p><strong>Conclusions: </strong>The MWM offers a promising, easily implementable, and time-efficient method for manufacturing PVA-C-based phantoms. Nevertheless, manufacturing-related microstructural properties and sample molding require further study.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 2","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11851462/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143498394","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":"Characterizing Metabolic Shifts in Septic Murine Kidney Tissue Using 2P-FLIM for Early Sepsis Detection.","authors":"Stella Greiner, Mahyasadat Ebrahimi, Marko Rodewald, Annett Urbanek, Tobias Meyer-Zedler, Michael Schmitt, Ute Neugebauer, Jürgen Popp","doi":"10.3390/bioengineering12020170","DOIUrl":"10.3390/bioengineering12020170","url":null,"abstract":"<p><p>In this study, thin mouse kidney sections from healthy mice and those infected leading to acute and chronic sepsis were examined with two-photon excited fluorescence lifetime imaging (2P-FLIM) using the endogenous fluorescent coenzymes nicotinamide adenine dinucleotide (NADH) and flavin adenine dinucleotide (FAD). The results presented show that this approach is a powerful tool for investigating cell metabolism in thin tissue sections. An adapted measurement routine was established for these samples by performing a spectral scan, identifying a combination of two excitation wavelengths and two detection ranges suitable for detailed scan images of NADH and FAD. Selected positions in thin slices of the renal cortex of nine mice (three healthy, three with chronic sepsis, and three with acute sepsis) were studied using 2P-FLIM. In addition, overview images were obtained using two-photon excited fluorescence (2PEF) intensity. This study shows that healthy kidney slices differ considerably from those with acute sepsis with regard to their fluorescence lifetime signatures. The latter shows a difference in metabolism between the inner and outer cortex, indicating that outer cortical tubular cells switch their metabolism from oxidative phosphorylation to glycolysis in kidneys from mice with acute sepsis and back in later stages, as seen for mice with chronic infections. These findings suggest that 2P-FLIM could serve as a powerful tool for early-stage sepsis diagnosis and monitoring metabolic recovery during treatment.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 2","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11851710/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143497596","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":"Predicting Health-Related Quality of Life Using Social Determinants of Health: A Machine Learning Approach with the All of Us Cohort.","authors":"Tadesse M Abegaz, Muktar Ahmed, Askal Ayalew Ali, Akshaya Srikanth Bhagavathula","doi":"10.3390/bioengineering12020166","DOIUrl":"10.3390/bioengineering12020166","url":null,"abstract":"<p><p>This study applied machine learning (ML) algorithms to predict health-related quality of life (HRQOL) using comprehensive social determinants of health (SDOH) features. Data from the All of Us dataset, comprising participants with complete HRQOL and SDOH records, were analyzed. The primary outcome was HRQOL, which encompassed physical and mental health components, while SDOH features included social, educational, economic, environmental, and healthcare access factors. Three ML algorithms, namely logistic regression, XGBoost, and Random Forest, were tested. The models achieved accuracy ranges of 0.73-0.77 for HRQOL, 0.70-0.71 for physical health, and 0.72-0.77 for mental health, with corresponding area under the curve ranges of 0.81-0.84, 0.74-0.76, and 0.83-0.85, respectively. Emotional stability, activity management, spiritual beliefs, and comorbidity were identified as key predictors. These findings underscore the critical role of SDOH in predicting HRQOL and suggests future research to focus on applying such models to diverse patient populations and specific clinical conditions.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 2","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11851811/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143498441","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 in Biomedical Engineering and Its Influence on Healthcare Structure: Current and Future Prospects.","authors":"Divya Tripathi, Kasturee Hajra, Aditya Mulukutla, Romi Shreshtha, Dipak Maity","doi":"10.3390/bioengineering12020163","DOIUrl":"10.3390/bioengineering12020163","url":null,"abstract":"<p><p>Artificial intelligence (AI) is a growing area of computer science that combines technologies with data science to develop intelligent, highly computation-able systems. Its ability to automatically analyze and query huge sets of data has rendered it essential to many fields such as healthcare. This article introduces you to artificial intelligence, how it works, and what its central role in biomedical engineering is. It brings to light new developments in medical science, why it is being applied in biomedicine, key problems in computer vision and AI, medical applications, diagnostics, and live health monitoring. This paper starts with an introduction to artificial intelligence and its major subfields before moving into how AI is revolutionizing healthcare technology. There is a lot of emphasis on how it will transform biomedical engineering through the use of AI-based devices like biosensors. Not only can these machines detect abnormalities in a patient's physiology, but they also allow for chronic health tracking. Further, this review also provides an overview of the trends of AI-enabled healthcare technologies and concludes that the adoption of artificial intelligence in healthcare will be very high. The most promising are in diagnostics, with highly accurate, non-invasive diagnostics such as advanced imaging and vocal biomarker analyzers leading medicine into the future.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 2","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11851410/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143498362","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":"Patellar Dislocation Patients Had Lower Bone Mineral Density and Hounsfield Unit Values in the Knee Joint Compared to Patients with Anterior Cruciate Ligament Ruptures: A Focus on Cortical Bone in the Tibia.","authors":"Yue Wu, Yiting Wang, Haijun Wang, Shaowei Jia, Yingfang Ao, Xi Gong, Zhenlong Liu","doi":"10.3390/bioengineering12020165","DOIUrl":"10.3390/bioengineering12020165","url":null,"abstract":"<p><p>Anterior cruciate ligament (ACL) rupture and patellar dislocation (PD) are common knee injuries. Dual-energy X-ray absorptiometry (DXA) and computed tomography (CT) are widely used clinical diagnostic tools. The aim was to investigate the characteristics of knee bone mineral density (BMD) in patients with ACL rupture and PD and to explore the relationship between BMD and Hounsfield unit (HU) values. This prospective cross-sectional study included 32 ACL rupture and 32 PD patients assessed via DXA and CT. BMD and CT measurements were taken from regions of interest in the femoral and tibial condyles. Statistical analyses included t-tests and mixed-effects models. The results showed that BMD in the PD group was significantly lower than in the ACL group (<i>p</i> < 0.05). The HU values of cortical bone in the femur and tibia differed significantly between the ACL group and the PD group (<i>p</i> < 0.05). The BMD of the femur and tibia showed significant correlations with the HU values of cancellous bone and cortical bone (<i>p</i> < 0.05). The conclusion was that PD patients had lower BMD and HU values in the femur and tibia compared to patients with ACL ruptures, particularly in the cortical bone of the tibia, and there was a strong correlation between HU value and BMD.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 2","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11852032/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143498438","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":"Biomechanical Analysis of Cycle-Tempo Effects on Motor Control Among Jump Rope Elites.","authors":"Qi Zhou, Yufeng Liu, Jianguo Kang, Xiuping Wang, Kai Zhang, Gongbing Shan","doi":"10.3390/bioengineering12020162","DOIUrl":"10.3390/bioengineering12020162","url":null,"abstract":"<p><p>Jump rope is a widely applied basic training technique in various sports, yet it is understudied biomechanically. This study investigates the impact of cycle-tempo-induced motor control changes in elite jump rope athletes, addressing the biomechanical gap of cyclic skill control. The hypothesis posited two accelerations per jump cycle-one in front of and one behind the body-and anticipated that increased cycle frequency would alter the distribution of acceleration time within a cycle. Using 3D motion capture technology, 12 young elite jump rope athletes were analyzed at 100, 140, and 180 revolutions per minute (rpm). The kinematic parameters obtained confirmed the presence of two distinct accelerations per cycle. As tempo increased, the percentage of rear acceleration time increased from 9.58% at 100 rpm to 17.42% at 180 rpm, while front acceleration time decreased from 39.03% at 100 rpm to 31.40% at 180 rpm, along with peak velocities increasing from 12.94 m/s at 100 rpm to 22.74 m/s at 180 rpm significantly (<i>p</i> < 0.01). Rope trajectory analysis indicated a consistent movement pattern across tempos, primarily in the sagittal plane. Variations in skill control revealed shorter contact phases, decreasing from 61.53% at 100 rpm to 48.25% at 180 rpm, as well as a reduced vertical range of motion for the center of gravity (from 0.15 body height at 100 rpm to 0.06 body height at 180 rpm) and feet (from 0.05 body height at 100 rpm to 0.03 body height at 180 rpm) (<i>p</i> < 0.05). Significant reductions were also observed in the flexion/extension range of motion for the hip (from 22.31° at 100 rpm to 3.47° at 180 rpm), knee (from 49.31° at 100 rpm to 9.35° at 180 rpm), and ankle (from 52.99° at 100 rpm to 21.41° at 180 rpm) (<i>p</i> < 0.01). These findings enhance the understanding of motor control adaptations to different tempos and have practical implications for developing coaching programs aimed at optimizing performance, stability, and efficiency in jump rope training.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 2","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11851774/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143498332","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 High-Frequency Temporal-Interference Alternative Current Stimulation Device Using Pulse Amplitude Modulation with Push-Pull Current Sources.","authors":"Jia-Hao Bai, Szu-Chi Huang, Po-Lei Lee, Kuo-Kai Shyu, Chao-Jen Huang, Tsung-Chih Chen, Sheng-Ji Lai","doi":"10.3390/bioengineering12020164","DOIUrl":"10.3390/bioengineering12020164","url":null,"abstract":"<p><p>This study proposes a high-frequency Pulse Amplitude-Modulation Temporal-Interference (PAM-TI) current stimulation device, which utilizes two sets of Amplitude-modulated transcranial alternating current stimulation (AM-tACS): one AM frequency at f0 (where f0 = 2 kHz) (source 1) and the other AM frequency at f1 = f0 + △f (where f1 = 2.01 kHz) (source 2), to generate a △f (where △f = 10 Hz) envelope modulated at a fc (where fc = 100 kHz) high carrier frequency. The high carrier frequency reduces body impedance and conserves more stimulation power, allowing it to penetrate the skin and reach the subcutaneous region. The proposed PAM-TI technique elevates the two current sources to a 100 kHz carrier frequency. Instead of the challenges associated with generating high-frequency stimulation currents using an MCU and DAC, the proposed PAM-TI stimulation device achieves this by simply utilizing a pair of complementary pulse-width modulations (PWMs). The push-pull technique is employed to balance the charging currents between the anode and cathode, synchronizing the current timing of Source 1 and Source 2 under the fc modulation condition. To minimize signal attenuation, the PAM circuit is integrated directly into the electrode, ensuring the high-frequency signal is generated close to the body and preventing degradation from long wires. Additionally, a dry pin-type spring-loaded electrode is used to reduce interference caused by hair when placed on the head. The device's validity and current directionality were verified using a scalp tissue-mimicking phantom composed of agar and saline.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 2","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11852082/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143498306","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":"Autism Data Classification Using AI Algorithms with Rules: Focused Review.","authors":"Abdulhamid Alsbakhi, Fadi Thabtah, Joan Lu","doi":"10.3390/bioengineering12020160","DOIUrl":"10.3390/bioengineering12020160","url":null,"abstract":"<p><p>Autism Spectrum Disorder (ASD) presents challenges in early screening due to its varied nature and sophisticated early signs. From a machine-learning (ML) perspective, the primary challenges include the need for large, diverse datasets, managing the variability in ASD symptoms, providing easy-to-understand models, and ensuring ASD predictive models that can be employed across different populations. Interpretable or explainable classification algorithms, like rule-based or decision tree, play a crucial role in dealing with some of these issues by offering classification models that can be exploited by clinicians. These models offer transparency in decision-making, allowing clinicians to understand reasons behind diagnostic decisions, which is critical for trust and adoption in medical settings. In addition, interpretable classification algorithms facilitate the identification of important behavioural features and patterns associated with ASD, enabling more accurate and explainable diagnoses. However, there is a scarcity of review papers focusing on interpretable classifiers for ASD detection from a behavioural perspective. Thereby this research aimed to conduct a recent review on rule-based classification research works in order to provide added value by consolidating current research, identifying gaps, and guiding future studies. Our research would enhance the understanding of these techniques, based on data used to generate models and obtain performance by trying to highlight early detection and intervention ways for ASD. Integrating advanced AI methods like deep learning with rule-based classifiers can improve model interpretability, exploration, and accuracy in ASD-detection applications. While this hybrid approach has feature selection relevant features that can be detected in an efficient manner, rule-based classifiers can provide clinicians with transparent explanations for model decisions. This hybrid approach is critical in clinical applications like ASD, where model content is as crucial as achieving high classification accuracy.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 2","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11852354/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143498364","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}