Tissue Engineering Part A最新文献

{"title":"Editorial for Special Issue on Artificial Intelligence in Tissue Engineering and Biology.","authors":"Jason L Guo, Michael Januszyk, Michael T Longaker","doi":"10.1089/ten.TEA.2024.0240","DOIUrl":"10.1089/ten.TEA.2024.0240","url":null,"abstract":"","PeriodicalId":56375,"journal":{"name":"Tissue Engineering Part A","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142006046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Code-Free Machine Learning Solutions for Microscopy Image Processing: Deep Learning.","authors":"Elizaveta Chechekhina, Nikita Voloshin, Konstantin Kulebyakin, Pyotr Tyurin-Kuzmin","doi":"10.1089/ten.TEA.2024.0014","DOIUrl":"10.1089/ten.TEA.2024.0014","url":null,"abstract":"<p><p>In recent years, there has been a significant expansion in the realm of processing microscopy images, thanks to the advent of machine learning techniques. These techniques offer diverse applications for image processing. Currently, numerous methods are used for processing microscopy images in the field of biology, ranging from conventional machine learning algorithms to sophisticated deep learning artificial neural networks with millions of parameters. However, a comprehensive grasp of the intricacies of these methods usually necessitates proficiency in programming and advanced mathematics. In our comprehensive review, we explore various widely used deep learning approaches tailored for the processing of microscopy images. Our emphasis is on algorithms that have gained popularity in the field of biology and have been adapted to cater to users lacking programming expertise. In essence, our target audience comprises biologists interested in exploring the potential of deep learning algorithms, even without programming skills. Throughout the review, we elucidate each algorithm's fundamental concepts and capabilities without delving into mathematical and programming complexities. Crucially, all the highlighted algorithms are accessible on open platforms without requiring code, and we provide detailed descriptions and links within our review. It's essential to recognize that addressing each specific problem demands an individualized approach. Consequently, our focus is not on comparing algorithms but on delineating the problems they are adept at solving. In practical scenarios, researchers typically select multiple algorithms suited to their tasks and experimentally determine the most effective one. It is worth noting that microscopy extends beyond the realm of biology; its applications span diverse fields such as geology and material science. Although our review predominantly centers on biomedical applications, the algorithms and principles outlined here are equally applicable to other scientific domains. Furthermore, a number of the proposed solutions can be modified for use in entirely distinct computer vision cases.</p>","PeriodicalId":56375,"journal":{"name":"Tissue Engineering Part A","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140332354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Revealing Early Spatial Patterns of Cellular Responsivity in Fiber-Reinforced Microenvironments.","authors":"Saitheja A Pucha, Maddie Hasson, Hanna Solomon, Gail E McColgan, Jennifer L Robinson, Sebastián L Vega, Jay M Patel","doi":"10.1089/ten.TEA.2024.0017","DOIUrl":"10.1089/ten.TEA.2024.0017","url":null,"abstract":"&lt;p&gt;&lt;p&gt;Fiber-reinforcement approaches have been used to replace aligned tissues with engineered constructs after injury or surgical resection, strengthening soft biomaterial scaffolds and replicating anisotropic, load-bearing properties. However, most studies focus on the macroscale aspects of these scaffolds, rarely considering the cell-biomaterial interactions that govern remodeling and extracellular matrix organization toward aligned neo-tissues. As initial cell-biomaterial responses within fiber-reinforced microenvironments likely influence the long-term efficacy of repair and regeneration strategies, here we elucidate the roles of spatial orientation, substrate stiffness, and matrix remodeling on early cell-fiber interactions. Bovine mesenchymal stromal cells (MSCs) were cultured in soft fibrin gels reinforced with a stiff 100 µm polyglycolide-co-caprolactone fiber. Gel stiffness and remodeling capacity were modulated by fibrinogen concentration and aprotinin treatment, respectively. MSCs were imaged at 3 days and evaluated for morphology, mechanoresponsiveness (nuclear Yes-associated protein [YAP] localization), and spatial features including distance and angle deviation from fiber. Within these constructs, morphological conformity decreased as a function of distance from fiber. However, these correlations were weak (&lt;i&gt;R&lt;/i&gt;&lt;sup&gt;2&lt;/sup&gt; = 0.01043 for conformity and &lt;i&gt;R&lt;/i&gt;&lt;sup&gt;2&lt;/sup&gt; = 0.05542 for nuclear YAP localization), illustrating cellular heterogeneity within fiber-enforced microenvironments. To better assess cell-fiber interactions, we applied machine-learning strategies to our heterogeneous dataset of cell-shape and mechanoresponsive parameters. Principal component analysis (PCA) was used to project 23 input parameters (not including distance) onto 5 principal components (PCs), followed by agglomerative hierarchical clustering to classify cells into 3 groups. These clusters exhibited distinct levels of morpho-mechanoresponse (combination of morphological conformity and YAP signaling) and were classified as high response (HR), medium response (MR), and low response (LR) clusters. Cluster distribution varied spatially, with most cells (61%) closest to the fiber (0-75 µm) belonging to the HR cluster, and most cells (55%) furthest from the fiber (225-300 µm) belonging to the LR cluster. Modulation of gel stiffness and fibrin remodeling showed differential effects for HR cells, with stiffness influencing the level of mechanoresponse and remodeling capacity influencing the location of responding cells. Together, these novel findings demonstrate early trends in cellular patterning of the fiber-reinforced microenvironment, showing how spatial orientation, substrate biophysical properties, and matrix remodeling may guide the amplitude and localization of cellular mechanoresponses. These trends may guide approaches to optimize the design of microscale scaffold architecture and substrate properties for enhancing organized tissue assembly at","PeriodicalId":56375,"journal":{"name":"Tissue Engineering Part A","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140186426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mapping Biomaterial Complexity by Machine Learning.","authors":"Eman Ahmed, Prajakatta Mulay, Cesar Ramirez, Gabriela Tirado-Mansilla, Eugene Cheong, Adam J Gormley","doi":"10.1089/ten.TEA.2024.0067","DOIUrl":"10.1089/ten.TEA.2024.0067","url":null,"abstract":"<p><p>Biomaterials often have subtle properties that ultimately drive their bespoke performance. Given this nuanced structure-function behavior, the standard scientific approach of one experiment at a time or design of experiment methods is largely inefficient for the discovery of complex biomaterials. More recently, high-throughput experimentation coupled with machine learning methods has matured beyond expert users allowing scientists and engineers from diverse backgrounds to access these powerful data science tools. As a result, we now have the opportunity to strategically utilize all available data from high-throughput experiments to train efficacious models and map the structure-function behavior of biomaterials for their discovery. Herein, we discuss this necessary shift to data-driven determination of structure-function properties of biomaterials as we highlight how machine learning is leveraged in identifying physicochemical cues for biomaterials in tissue engineering, gene delivery, drug delivery, protein stabilization, and antifouling materials. We also discuss data-mining approaches that are coupled with machine learning to map biomaterial functions that reduce the load on experimental approaches for faster biomaterial discovery. Ultimately, harnessing the prowess of machine learning will lead to accelerated discovery and development of optimal biomaterial designs.</p>","PeriodicalId":56375,"journal":{"name":"Tissue Engineering Part A","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141972340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MyoFInDer: An AI-Based Tool for Myotube Fusion Index Determination.","authors":"Antoine Weisrock, Rebecca Wüst, Maria Olenic, Pauline Lecomte-Grosbras, Lieven Thorrez","doi":"10.1089/ten.TEA.2024.0049","DOIUrl":"10.1089/ten.TEA.2024.0049","url":null,"abstract":"<p><p>The fusion index is a key indicator for quantifying the differentiation of a myoblast population, which is often calculated manually. In addition to being time-consuming, manual quantification is also error prone and subjective. Several software tools have been proposed for addressing these limitations but suffer from various drawbacks, including unintuitive interfaces and limited performance. In this study, we describe MyoFInDer, a Python-based program for the automated computation of the fusion index of skeletal muscle. At the core of MyoFInDer is a powerful artificial intelligence-based image segmentation model. MyoFInDer also determines the total nuclei count and the percentage of stained area and allows for manual verification and correction. MyoFInDer can reliably determine the fusion index, with a high correlation to manual counting. Compared with other tools, MyoFInDer stands out as it minimizes the interoperator variability, minimizes process time and displays the best correlation to manual counting. Therefore, it is a suitable choice for calculating fusion index in an automated way, and gives researchers access to the high performance and flexibility of a modern artificial intelligence model. As a free and open-source project, MyoFInDer can be modified or extended to meet specific needs.</p>","PeriodicalId":56375,"journal":{"name":"Tissue Engineering Part A","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141238837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hematoxylin and Eosin Architecture Uncovers Clinically Divergent Niches in Pancreatic Cancer.","authors":"Jason L Guo, David M Lopez, Shamik Mascharak, Deshka S Foster, Anum Khan, Michael F Davitt, Alan T Nguyen, Austin R Burcham, Malini S Chinta, Nicholas J Guardino, Michelle Griffin, Elisabeth Miller, Michael Januszyk, Shyam S Raghavan, Teri A Longacre, Daniel J Delitto, Jeffrey A Norton, Michael T Longaker","doi":"10.1089/ten.TEA.2024.0039","DOIUrl":"10.1089/ten.TEA.2024.0039","url":null,"abstract":"<p><p>Pancreatic ductal adenocarcinoma (PDAC) represents one of the only cancers with an increasing incidence rate and is often associated with intra- and peri-tumoral scarring, referred to as desmoplasia. This scarring is highly heterogeneous in extracellular matrix (ECM) architecture and plays complex roles in both tumor biology and clinical outcomes that are not yet fully understood. Using hematoxylin and eosin (H&E), a routine histological stain utilized in existing clinical workflows, we quantified ECM architecture in 85 patient samples to assess relationships between desmoplastic architecture and clinical outcomes such as survival time and disease recurrence. By utilizing unsupervised machine learning to summarize a latent space across 147 local (e.g., fiber length, solidity) and global (e.g., fiber branching, porosity) H&E-based features, we identified a continuum of histological architectures that were associated with differences in both survival and recurrence. Furthermore, we mapped H&E architectures to a CO-Detection by indEXing (CODEX) reference atlas, revealing localized cell- and protein-based niches associated with outcome-positive versus outcome-negative scarring in the tumor microenvironment. Overall, our study utilizes standard H&E staining to uncover clinically relevant associations between desmoplastic organization and PDAC outcomes, offering a translatable pipeline to support prognostic decision-making and a blueprint of spatial-biological factors for modeling by tissue engineering methods.</p>","PeriodicalId":56375,"journal":{"name":"Tissue Engineering Part A","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence Applications in Oral Cancer and Oral Dysplasia.","authors":"Chi T Viet, Michael Zhang, Neeraja Dharmaraj, Grace Y Li, Alexander T Pearson, Victoria A Manon, Anupama Grandhi, Ke Xu, Bradley E Aouizerat, Simon Young","doi":"10.1089/ten.TEA.2024.0096","DOIUrl":"10.1089/ten.TEA.2024.0096","url":null,"abstract":"<p><p>Oral squamous cell carcinoma (OSCC) is a highly unpredictable disease with devastating mortality rates that have not changed over the past decades, in the face of advancements in treatments and biomarkers, which have improved survival for other cancers. Delays in diagnosis are frequent, leading to more disfiguring treatments and poor outcomes for patients. The clinical challenge lies in identifying those patients at the highest risk of developing OSCC. Oral epithelial dysplasia (OED) is a precursor of OSCC with highly variable behavior across patients. There is no reliable clinical, pathological, histological, or molecular biomarker to determine individual risk in OED patients. Similarly, there are no robust biomarkers to predict treatment outcomes or mortality in OSCC patients. This review aims to highlight advancements in artificial intelligence (AI)-based methods to develop predictive biomarkers of OED transformation to OSCC or predictive biomarkers of OSCC mortality and treatment response. Biomarkers such as S100A7 demonstrate promising appraisal for the risk of malignant transformation of OED. Machine learning-enhanced multiplex immunohistochemistry workflows examine immune cell patterns and organization within the tumor immune microenvironment to generate outcome predictions in immunotherapy. Deep learning (DL) is an AI-based method using an extended neural network or related architecture with multiple \"hidden\" layers of simulated neurons to combine simple visual features into complex patterns. DL-based digital pathology is currently being developed to assess OED and OSCC outcomes. The integration of machine learning in epigenomics aims to examine the epigenetic modification of diseases and improve our ability to detect, classify, and predict outcomes associated with epigenetic marks. Collectively, these tools showcase promising advancements in discovery and technology, which may provide a potential solution to addressing the current limitations in predicting OED transformation and OSCC behavior, both of which are clinical challenges that must be addressed in order to improve OSCC survival.</p>","PeriodicalId":56375,"journal":{"name":"Tissue Engineering Part A","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141749842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Endothelial Cells Increase Mesenchymal Stem Cell Differentiation in Scaffold-Free 3D Vascular Tissue.","authors":"William G DeMaria, Andre E Figueroa-Milla, Abigail Kaija, Anne E Harrington, Benjamin Tero, Larisa Ryzhova, Lucy Liaw, Marsha W Rolle","doi":"10.1089/ten.TEA.2024.0122","DOIUrl":"10.1089/ten.TEA.2024.0122","url":null,"abstract":"<p><p>In this study, we present a versatile, scaffold-free approach to create ring-shaped engineered vascular tissue segments using human mesenchymal stem cell-derived smooth muscle cells (hMSC-SMCs) and endothelial cells (ECs). We hypothesized that incorporation of ECs would increase hMSC-SMC differentiation without compromising tissue ring strength or fusion to form tissue tubes. Undifferentiated hMSCs and ECs were co-seeded into custom ring-shaped agarose wells using four different concentrations of ECs: 0%, 10%, 20%, and 30%. Co-seeded EC and hMSC rings were cultured in SMC differentiation medium for a total of 22 days. Tissue rings were then harvested for histology, Western blotting, wire myography, and uniaxial tensile testing to examine their structural and functional properties. Differentiated hMSC tissue rings comprising 20% and 30% ECs exhibited significantly greater SMC contractile protein expression, endothelin-1 (ET-1)-meditated contraction, and force at failure compared with the 0% EC rings. On average, the 0%, 10%, 20%, and 30% EC rings exhibited a contractile force of 0.745 ± 0.117, 0.830 ± 0.358, 1.31 ± 0.353, and 1.67 ± 0.351 mN (mean ± standard deviation [SD]) in response to ET-1, respectively. Additionally, the mean maximum force at failure for the 0%, 10%, 20%, and 30% EC rings was 88.5 ± 36. , 121 ± 59.1, 147 ± 43.1, and 206 ±  0.8 mN (mean ± SD), respectively. Based on these results, 30% EC rings were fused together to form tissue-engineered blood vessels (TEBVs) and compared with 0% EC TEBV controls. The addition of 30% ECs in TEBVs did not affect ring fusion but did result in significantly greater SMC protein expression (calponin and smoothelin). In summary, co-seeding hMSCs with ECs to form tissue rings resulted in greater contraction, strength, and hMSC-SMC differentiation compared with hMSCs alone and indicates a method to create a functional 3D human vascular cell coculture model.</p>","PeriodicalId":56375,"journal":{"name":"Tissue Engineering Part A","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141899052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 Matrix Stiffness And Viscosity on Lipid Phenotype And Fat Lineage Potential.","authors":"Chelsea J Stephens, Reina Kobayashi, Daniel C Berry, Jonathan T Butcher","doi":"10.1089/ten.TEA.2024.0149","DOIUrl":"10.1089/ten.TEA.2024.0149","url":null,"abstract":"<p><p>Autologous fat transfer is a common procedure that patients undergo to rejuvenate large soft tissue defects. However, these surgeries are complicated by limited tissue sources, donor-site morbidity, and necrosis. While the biofabrication of fat tissue can serve as a clinical option for reconstructive surgery, the influence of matrix mechanics, specifically stiffness and viscosity, on adipogenesis requires further elucidation. Additionally, the effects of these mechanical parameters on metabolic and thermogenic fat potential have yet to be investigated. In this study, gelatin methacryloyl (GelMA) polymers with varying degrees of methacrylation (DoM) were fabricated to create matrices with different stiffnesses and viscosities. Human adipose-derived mesenchymal stem cells were then encapsulated in mechanically tunable GelMA and underwent adipogenesis to investigate the effects of matrix mechanics on lipid phenotype and fat potential. Mechanical testing confirmed that GelMA stiffness was regulated by DoM and weight composition, whereas viscosity was determined by the latter. Further work revealed that while lipid phenotype became more enriched as matrix stiffness and viscosity declined, the potential toward metabolic and thermogenic fat appeared to be more viscous dependent rather than stiffness dependent. In addition, fatty acid binding protein 4 and uncoupling protein 1 gene expression exhibited viscous-dependent behavior despite comparable levels of peroxisome proliferator-activated receptor gamma. However, despite the superior role of viscosity, lipid quantity and mitochondrial abundance demonstrated stiffness-dependent behavior. Overall, this work revealed that matrix viscosity played a more superior role than stiffness in driving adipogenesis and distinguishing between metabolic and thermogenic fat potential. Ultimately, this differentiation in fat production is important for engineering ideal adipose tissue for large soft tissue defects.</p>","PeriodicalId":56375,"journal":{"name":"Tissue Engineering Part A","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142010025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward the Development of a Tissue Engineered Gradient Utilizing CRISPR-Guided Gene Modulation.","authors":"Jacob D Weston, Brooke Austin, Hunter Levis, Jared Zitnay, Jeffrey A Weiss, Brandon Lawrence, Robby D Bowles","doi":"10.1089/ten.TEA.2023.0352","DOIUrl":"10.1089/ten.TEA.2023.0352","url":null,"abstract":"<p><p>Cellular, compositional, and mechanical gradients are found throughout biological tissues, especially in transition zones between tissue types. Yet, strategies to engineer such gradients have proven difficult due to the complex nature of these tissues. Current strategies for tissue engineering complex gradients often utilize stem cells; however, these multipotent cells require direction from environmental cues, which can be difficult to control both <i>in vitro</i> and <i>in vivo</i>. In this study, we utilize clustered regularly-interspaced short palindromic repeats (CRISPR)-guided gene modulation to direct the differentiation of multipotent adipose-derived stem cells (ASCs) to demonstrate the effectiveness of CRISPR-engineered cells in tissue engineering applications. Specifically, we screen CRISPR-interference (CRISPRi) constructs targeting the promotors of selected osteogenic inhibitors and demonstrate that ASC osteogenic differentiation and mineral deposition can be regulated with CRISPRi targeting of Noggin without the use of exogenous growth factors in tissue engineered constructs. As a proof of concept, we combine three technologies developed out of our laboratories to demonstrate the controlled deposition of these engineered cells in a gradient with CRISPR-activation multiplex-engineered aggrecan/collagen type-II-chondrogenic ASCs on a high density anisotropic type I collagen construct to create a cell and tissue gradient similar to the fibrocartilage-to-mineralized-fibrocartilage gradient in the enthesis. Our results display the promise of CRISPR-engineered ASCs to produce tissue gradients, similar to what is observed in native tissue.</p>","PeriodicalId":56375,"journal":{"name":"Tissue Engineering Part A","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139699003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}