Bioengineering最新文献

{"title":"A Hybrid Soft Sensor Approach Combining Partial Least-Squares Regression and an Unscented Kalman Filter for State Estimation in Bioprocesses.","authors":"Lucas Hermann, Andreas Kremling","doi":"10.3390/bioengineering12060654","DOIUrl":"10.3390/bioengineering12060654","url":null,"abstract":"<p><p>Real-time information on key state variables during fermentation is crucial for the effective optimization and control of bioprocesses. Specialized sensors for online or at-line monitoring of these variables are often associated with high costs, especially during early-stage process optimization. In this study, fed-batch processes of an L-phenylalanine (L-phe) production process were carried out using a recombinant <i>Escherichia coli</i> strain under varying inducer concentrations. The available online process variables from the L-phe production process were used to estimate the state variables biomass, glycerol, L-phe, acetate, and L-tyrosine (L-tyr) via partial least-squares regression (PLSR). These predictions were then incorporated as measurements into an unscented Kalman filter (UKF). The filter uses a coarse-grained model as a state estimator, which, in addition to extracellular variables, also provides information on intracellular states. The results of PLSR showed very good prediction accuracy for L-phe, moderate accuracy for glycerol, biomass, and L-tyr and poor performance for acetate concentrations. In combination with the UKF, the estimation of the L-phe concentrations was greatly improved compared to the CGM, whereas further improvement is still needed for the remaining state variables.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 6","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12189592/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144493908","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":"AI-Powered Spectral Imaging for Virtual Pathology Staining.","authors":"Adam Soker, Maya Almagor, Sabine Mai, Yuval Garini","doi":"10.3390/bioengineering12060655","DOIUrl":"10.3390/bioengineering12060655","url":null,"abstract":"<p><p>Pathological analysis of tissue biopsies remains the gold standard for diagnosing cancer and other diseases. However, this is a time-intensive process that demands extensive training and expertise. Despite its importance, it is often subjective and not entirely error-free. Over the past decade, pathology has undergone two major transformations. First, the rise in whole slide imaging has enabled work in front of a computer screen and the integration of image processing tools to enhance diagnostics. Second, the rapid evolution of Artificial Intelligence has revolutionized numerous fields and has had a remarkable impact on humanity. The synergy of these two has paved the way for groundbreaking research aiming for advancements in digital pathology. Despite encouraging research outcomes, AI-based tools have yet to be actively incorporated into therapeutic protocols. This is primary due to the need for high reliability in medical therapy, necessitating a new approach that ensures greater robustness. Another approach for improving pathological diagnosis involves advanced optical methods such as spectral imaging, which reveals information from the tissue that is beyond human vision. We have recently developed a unique rapid spectral imaging system capable of scanning pathological slides, delivering a wealth of critical diagnostic information. Here, we present a novel application of spectral imaging (SI) for virtual Hematoxylin and Eosin (H&E) staining using a custom-built, rapid Fourier-based SI system. Unstained human biopsy samples are scanned, and a Pix2Pix-based neural network generates realistic H&E-equivalent images. Additionally, we applied Principal Component Analysis (PCA) to the spectral information to examine the effect of down sampling the data on the virtual staining process. To assess model performance, we trained and tested models using full spectral data, RGB, and PCA-reduced spectral inputs. The results demonstrate that PCA-reduced data preserved essential image features while enhancing statistical image quality, as indicated by FID and KID scores, and reducing computational complexity. These findings highlight the potential of integrating SI and AI to enable efficient, accurate, and stain-free digital pathology.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 6","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12189724/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144494000","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 Outperforms Physicians in General Medical Knowledge, Except in the Paediatrics Domain: A Cross-Sectional Study.","authors":"Joana Miranda, Raquel Pereira-Silva, João Guichard, Jorge Meneses, Andreia Neves Carreira, Daniela Seixas","doi":"10.3390/bioengineering12060653","DOIUrl":"10.3390/bioengineering12060653","url":null,"abstract":"<p><p>Generative artificial intelligence (genAI) shows promising results in clinical practice. This study compared a GPT-4-turbo virtual assistant with physicians from Italy, France, Spain, and Portugal on medical knowledge derived from national exams while analysing knowledge retention over time and domain-specific performance. Via a digital platform, 17,144 physicians provided 221,574 answers to 600 exam questions between December 2022 and February 2024. Physicians were stratified by years since graduation and specialty, and the assistant answered the same questions in each native language. Differences in proportions of correct answers were tested with binomial logistic regression (odds ratios, 95% CI) or Fisher's exact test (α = 0.05). The assistant outperformed physicians in all countries (72-96% vs. 46-62%; logistic regression, <i>p</i> < 0.001). Physicians also trailed the assistant across most knowledge domains (<i>p</i> < 0.001), except paediatrics (45% vs. 52%; Fisher, <i>p</i> = 0.60). Accuracy declined with seniority, falling 4-10% between the youngest and oldest cohorts (logistic regression, <i>p</i> < 0.001). Overall, genAI exceeds practising doctors on broad medical knowledge and may help counter knowledge attrition, though paediatrics remains a domain requiring targeted refinement.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 6","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12190018/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144493982","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":"OCT in Oncology and Precision Medicine: From Nanoparticles to Advanced Technologies and AI.","authors":"Sanam Daneshpour Moghadam, Bogdan Maris, Ali Mokhtari, Claudia Daffara, Paolo Fiorini","doi":"10.3390/bioengineering12060650","DOIUrl":"10.3390/bioengineering12060650","url":null,"abstract":"<p><p>Optical Coherence Tomography (OCT) is a relatively new medical imaging device that provides high-resolution and real-time visualization of biological tissues. Initially designed for ophthalmology, OCT is now being applied in other types of pathologies, like cancer diagnosis. This review highlights its impact on disease diagnosis, biopsy guidance, and treatment monitoring. Despite its advantages, OCT has limitations, particularly in tissue penetration and differentiating between malignant and benign lesions. To overcome these challenges, the integration of nanoparticles has emerged as a transformative approach, which significantly enhances contrast and tumor vascularization at the molecular level. Gold and superparamagnetic iron oxide nanoparticles, for instance, have demonstrated great potential in increasing OCT's diagnostic accuracy through enhanced optical scattering and targeted biomarker detection. Beyond these innovations, integrating OCT with multimodal imaging methods, including magnetic resonance imaging (MRI), positron emission tomography (PET), and ultrasound, offers a more comprehensive approach to disease assessment, particularly in oncology. Additionally, advances in artificial intelligence (AI) and biosensors have further expanded OCT's capabilities, enabling real-time tumor characterization and optimizing surgical precision. However, despite these advancements, clinical adoption still faces several hurdles. Issues related to nanoparticle biocompatibility, regulatory approvals, and standardization need to be addressed. Moving forward, research should focus on refining nanoparticle technology, improving AI-driven image analysis, and ensuring broader accessibility to OCT-guided diagnostics. By tackling these challenges, OCT could become an essential tool in precision medicine, facilitating early disease detection, real-time monitoring, and personalized treatment for improved patient outcomes.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 6","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12189282/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144494059","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":"Hierarchical Swin Transformer Ensemble with Explainable AI for Robust and Decentralized Breast Cancer Diagnosis.","authors":"Md Redwan Ahmed, Hamdadur Rahman, Zishad Hossain Limon, Md Ismail Hossain Siddiqui, Mahbub Alam Khan, Al Shahriar Uddin Khondakar Pranta, Rezaul Haque, S M Masfequier Rahman Swapno, Young-Im Cho, Mohamed S Abdallah","doi":"10.3390/bioengineering12060651","DOIUrl":"10.3390/bioengineering12060651","url":null,"abstract":"<p><p>Early and accurate detection of breast cancer is essential for reducing mortality rates and improving clinical outcomes. However, deep learning (DL) models used in healthcare face significant challenges, including concerns about data privacy, domain-specific overfitting, and limited interpretability. To address these issues, we propose BreastSwinFedNetX, a federated learning (FL)-enabled ensemble system that combines four hierarchical variants of the Swin Transformer (Tiny, Small, Base, and Large) with a Random Forest (RF) meta-learner. By utilizing FL, our approach ensures collaborative model training across decentralized and institution-specific datasets while preserving data locality and preventing raw patient data exposure. The model exhibits strong generalization and performs exceptionally well across five benchmark datasets-BreakHis, BUSI, INbreast, CBIS-DDSM, and a Combined dataset-achieving an F1 score of 99.34% on BreakHis, a PR AUC of 98.89% on INbreast, and a Matthews Correlation Coefficient (MCC) of 99.61% on the Combined dataset. To enhance transparency and clinical adoption, we incorporate explainable AI (XAI) through Grad-CAM, which highlights class-discriminative features. Additionally, we deploy the model in a real-time web application that supports uncertainty-aware predictions and clinician interaction and ensures compliance with GDPR and HIPAA through secure federated deployment. Extensive ablation studies and paired statistical analyses further confirm the significance and robustness of each architectural component. By integrating transformer-based architectures, secure collaborative training, and explainable outputs, BreastSwinFedNetX provides a scalable and trustworthy AI solution for real-world breast cancer diagnostics.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 6","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12189839/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144494045","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":"The Risks Associated with Inhalation Exposure to Cosmetics and Potential for Assessment Using Lung Organoids.","authors":"Yiguang Li, Xin Luo, Rong Hu, Lifeng Tang, Qi Xiang","doi":"10.3390/bioengineering12060652","DOIUrl":"10.3390/bioengineering12060652","url":null,"abstract":"<p><p>This review addresses the exposure risks associated with the inhalation of aerosolized cosmetic products and explores the utility of lung organoids in assessing these risks. Aerosolized cosmetics such as sprays pose potential health hazards through inhalation, necessitating a thorough evaluation of exposure levels. Traditional methods for assessing inhalation risks have limitations, prompting the exploration of more sophisticated models. Lung organoids, three-dimensional structures derived from stem cells, offer a biologically relevant model for studying lung responses to inhaled substances. This review discusses the construction of lung organoids, their characteristics, and the advantages that they provide over conventional models. Furthermore, it examines existing studies that have employed lung organoids to evaluate the effects of cosmetic inhalation exposure, highlighting the potential of this approach to enhance the safety assessments of cosmetic products. We aim to establish lung organoids as a reliable tool for future research, ensuring the safety and regulatory compliance of cosmetics.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 6","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12189234/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144494078","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":"Toward Integrative Biomechanical Models of Osteochondral Tissues: A Multilayered Perspective.","authors":"Bruna Silva, Marco Domingos, Sandra Amado, Juliana R Dias, Paula Pascoal-Faria, Ana C Maurício, Nuno Alves","doi":"10.3390/bioengineering12060649","DOIUrl":"10.3390/bioengineering12060649","url":null,"abstract":"<p><p>Understanding the complex mechanical behavior of osteochondral tissues in silico is essential for improving experimental models and advancing research in joint health and degeneration. This review provides a comprehensive analysis of the constitutive models currently used to represent the different layers of the osteochondral region, from articular cartilage to subchondral bone, including intermediate regions such as the tidemark and the calcified cartilage layer. Each layer exhibits unique structural and mechanical properties, necessitating a layer-specific modeling approach. Through critical comparison of existing mathematical models, the viscoelastic model is suggested as a pragmatic starting point for modeling articular cartilage zones, the tidemark, and the calcified cartilage layer, as it captures essential time-dependent behaviors such as creep and stress relaxation while ensuring computational efficiency for initial coupling studies. On the other hand, a linear elastic model was identified as an optimal starting point for both the subchondral bone plate and the subchondral trabecular bone, reflecting their dense and stiff nature, and providing a coherent framework for early-stage multilayer integration. This layered modeling approach enables the development of physiologically coherent and computationally efficient representations of osteochondral region modeling. Furthermore, by establishing a layer-specific modeling approach, this review paves the way for modular in silico simulations through the coupling of computational models. Such an integrative framework supports scaffold design, in vitro experimentation, preclinical validation, and the mechanobiological exploration of osteochondral degeneration and repair. These efforts are essential for deepening our understanding of tissue responses under both physiological and pathological conditions. Ultimately, this work provides a robust theoretical foundation for future in silico and in vitro studies aimed at advancing osteochondral tissue regeneration strategies.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 6","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12190186/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144494079","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":"Large-Scale Expansion of Suspension Cells in an Automated Hollow-Fiber Perfusion Bioreactor.","authors":"Eric Bräuchle, Maria Knaub, Laura Weigand, Elisabeth Ehrend, Patricia Manns, Antje Kremer, Hugo Fabre, Halvard Bonig","doi":"10.3390/bioengineering12060644","DOIUrl":"10.3390/bioengineering12060644","url":null,"abstract":"<p><p>Bioreactors enable scalable cell cultivation by providing controlled environments for temperature, oxygen, and nutrient regulation, maintaining viability and enhancing expansion efficiency. Automated systems improve reproducibility and minimize contamination risks, making them ideal for high-density cultures. While fed-batch bioreactors dominate biologics production, continuous systems like perfusion cultures offer superior resource efficiency and productivity. The Quantum hollow-fiber perfusion bioreactor supports cell expansion via semi-permeable capillary membranes and a closed modular design, allowing continuous media exchange while retaining key molecules. We developed a multiple-harvest protocol for suspension cells in the Quantum system, yielding 2.5 × 10¹⁰ MEL-745A cells within 29 days, with peak densities of 4 × 10⁷ cells/mL-a 15-fold increase over static cultures. Viability averaged 91.3%, with biweekly harvests yielding 3.1 × 10⁹ viable cells per harvest. Continuous media exchange required more basal media to maintain glucose and lactate levels but meaningfully less growth supplement than the 2D culture. Stable transgene expression suggested phenotypic stability. Automated processing reduced hands-on time by one-third, achieving target cell numbers 12 days earlier than 2D culture. Despite higher media use, total costs for the automated were lower compared to the manual process. Quantum enables high-density suspension cell expansion with cost advantages over conventional methods.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 6","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12189701/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144494050","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":"Active Contours Connected Component Analysis Segmentation Method of Cancerous Lesions in Unsupervised Breast Histology Images.","authors":"Vincent Majanga, Ernest Mnkandla, Zenghui Wang, Donatien Koulla Moulla","doi":"10.3390/bioengineering12060642","DOIUrl":"10.3390/bioengineering12060642","url":null,"abstract":"<p><p>Automatic segmentation of nuclei on breast cancer histology images is a basic and important step for diagnosis in a computer-aided diagnostic approach and helps pathologists discover cancer early. Nuclei segmentation remains a challenging problem due to cancer biology and the variability of tissue characteristics; thus, their detection in an image is a very tedious and time-consuming task. In this context, overlapping nuclei objects present difficulties in separating them by conventional segmentation methods; thus, active contours can be employed in image segmentation. A major limitation of the active contours method is its inability to resolve image boundaries/edges of intersecting objects and segment multiple overlapping objects as a single object. Therefore, we present a hybrid active contour (connected component + active contours) method to segment cancerous lesions in unsupervised human breast histology images. Initially, this approach prepares and pre-processes data through various augmentation methods to increase the dataset size. Then, a stain normalization technique is applied to these augmented images to isolate nuclei features from tissue structures. Secondly, morphology operation techniques, namely erosion, dilation, opening, and distance transform, are used to highlight foreground and background pixels while removing overlapping regions from the highlighted nuclei objects on the image. Consequently, the connected components method groups these highlighted pixel components with similar intensity values and assigns them to their relevant labeled component to form a binary mask. Once all binary-masked groups have been determined, a deep-learning recurrent neural network (RNN) model from the Keras architecture uses this information to automatically segment nuclei objects having cancerous lesions on the image via the active contours method. This approach, therefore, uses the capabilities of connected components analysis to solve the limitations of the active contour method. This segmentation method is evaluated on an unsupervised, augmented human breast cancer histology dataset of 15,179 images. This proposed method produced a significant evaluation result of 98.71% accuracy score.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 6","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12189801/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144493916","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":"Channel-Dependent Multilayer EEG Time-Frequency Representations Combined with Transfer Learning-Based Deep CNN Framework for Few-Channel MI EEG Classification.","authors":"Ziang Liu, Kang Fan, Qin Gu, Yaduan Ruan","doi":"10.3390/bioengineering12060645","DOIUrl":"10.3390/bioengineering12060645","url":null,"abstract":"<p><p>The study of electroencephalogram (EEG) signals is crucial for understanding brain function and has extensive applications in clinical diagnosis, neuroscience, and brain-computer interface technology. This paper addresses the challenge of recognizing motor imagery EEG signals with few channels, which is essential for portable and real-time applications. A novel framework is proposed that applies a continuous wavelet transform to convert time-domain EEG signals into two-dimensional time-frequency representations. These images are then concatenated into channel-dependent multilayer EEG time-frequency representations (CDML-EEG-TFR), incorporating multidimensional information of time, frequency, and channels, allowing for a more comprehensive and enriched brain representation under the constraint of few channels. By adopting a deep convolutional neural network with EfficientNet as the backbone and utilizing pre-trained weights from natural image datasets for transfer learning, the framework can simultaneously learn temporal, spatial, and channel features embedded in the CDML-EEG-TFR. Moreover, the transfer learning strategy effectively addresses the issue of data sparsity in the context of a few channels. Our approach enhances the classification accuracy of motor imagery EEG signals in few-channel scenarios. Experimental results on the BCI Competition IV 2b dataset show a significant improvement in classification accuracy, reaching 80.21%. This study highlights the potential of CDML-EEG-TFR and the EfficientNet-based transfer learning strategy in few-channel EEG signal classification, laying a foundation for practical applications and further research in medical and sports fields.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 6","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12189518/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144494009","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}