ArXiv Pub Date : 2025-08-28

Mahdi Bagheri, Clemente Velasco-Annis, Jian Wang, Razieh Faghihpirayesh, Shadab Khan, Camilo Calixto, Camilo Jaimes, Lana Vasung, Abdelhakim Ouaalam, Onur Afacan, Simon K Warfield, Caitlin K Rollins, Ali Gholipour

{"title":"An MRI Atlas of the Human Fetal Brain: Reference and Segmentation Tools for Fetal Brain MRI Analysis.","authors":"Mahdi Bagheri, Clemente Velasco-Annis, Jian Wang, Razieh Faghihpirayesh, Shadab Khan, Camilo Calixto, Camilo Jaimes, Lana Vasung, Abdelhakim Ouaalam, Onur Afacan, Simon K Warfield, Caitlin K Rollins, Ali Gholipour","doi":"","DOIUrl":"","url":null,"abstract":"Accurate characterization of in-utero brain development is essential for understanding typical and atypical neurodevelopment. Building upon previous efforts to construct spatiotemporal fetal brain MRI atlases, we present the CRL-2025 fetal brain atlas, which is a spatiotemporal (4D) atlas of the developing fetal brain between 21 and 37 gestational weeks. This atlas is constructed from carefully processed MRI scans of 160 fetuses with typically-developing brains using a diffeomorphic deformable registration framework integrated with kernel regression on age. CRL-2025 uniquely includes detailed tissue segmentations, transient white matter compartments, and parcellation into 126 anatomical regions. This atlas offers significantly enhanced anatomical details over the CRL-2017 atlas, and is released along with the CRL diffusion MRI atlas with its newly created tissue segmentation and labels as well as deep learning-based multiclass segmentation models for fine-grained fetal brain MRI segmentation. The CRL-2025 atlas and its associated tools provide a robust and scalable platform for fetal brain MRI segmentation, groupwise analysis, and early neurodevelopmental research, and these materials are publicly released to support the broader research community.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12393236/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144982043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Nucleation feedback can drive establishment and maintenance of biased microtubule polarity in neurites. 成核反馈可以驱动神经突中偏置微管极性的建立和维持。

ArXiv Pub Date : 2025-08-28

Hannah G Scanlon, Gibarni Mahata, Anna C Nelson, Scott A McKinley, Melissa M Rolls, Maria-Veronica Ciocanel

{"title":"Nucleation feedback can drive establishment and maintenance of biased microtubule polarity in neurites.","authors":"Hannah G Scanlon, Gibarni Mahata, Anna C Nelson, Scott A McKinley, Melissa M Rolls, Maria-Veronica Ciocanel","doi":"","DOIUrl":"","url":null,"abstract":"The microtubule cytoskeleton is comprised of dynamic, polarized filaments that facilitate transport within the cell. Polarized microtubule arrays are key to facilitating cargo transport in long cells such as neurons. Microtubules also undergo dynamic instability, where the plus and minus ends of the filaments switch between growth and shrinking phases, leading to frequent microtubule turnover. Although microtubules often completely disassemble and new filaments nucleate, microtubule arrays have been observed to both maintain their biased orientation throughout the cell lifetime and to rearrange their polarity as an adaptive response to injury. Motivated by cytoskeleton organization in neurites, we propose a spatially-explicit stochastic model of microtubule arrays and investigate how nucleation of new filaments could generate biased polarity in a simple linear domain. Using a continuous-time Markov chain model of microtubule growth dynamics, we model and parameterize two experimentally-validated nucleation mechanisms: nucleation feedback, where the direction of filament growth depends on existing microtubule content, and a checkpoint mechanism, where microtubules that nucleate in a direction opposite to the majority experience frequent catastrophe. When incorporating these validated mechanisms into the spatial model, we find that nucleation feedback is sufficient to establish biased polarity in neurites of different lengths, and that the emergence and maintenance of biased polarity is relatively stable in spite of stochastic fluctuations. This work provides a framework to study the relationship between microtubule nucleation and polarity, and could extend to give insights into mechanisms that drive the formation of polarized filament arrays in other biological settings.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12407633/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145002169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DNA Dynamics in Dual Nanopore Tug-of-War. 双纳米孔拔河中的DNA动力学。

ArXiv Pub Date : 2025-08-28

Zezhou Liu, Wangwei Dong, Thomas St-Denis, Matheus Azevedo Silva Pessôa, Sajad Shiekh, Preethi Ravikumar, Walter Reisner

{"title":"DNA Dynamics in Dual Nanopore Tug-of-War.","authors":"Zezhou Liu, Wangwei Dong, Thomas St-Denis, Matheus Azevedo Silva Pessôa, Sajad Shiekh, Preethi Ravikumar, Walter Reisner","doi":"","DOIUrl":"","url":null,"abstract":"Solid state nanopores have emerged as powerful tools for single-molecule sensing, yet the rapid uncontrolled translocation of the molecule through the pore remains a key limitation. We have previously demonstrated that an active dual-nanopore system, consisting of two closely spaced pores operated via feedback controlled biasing, shows promise in achieving controlled, slowed-down translocation. Translocation control is achieved via capturing the DNA in a special tug-of-war configuration, whereby opposing electrophoretic forces at each pore are applied to a DNA molecule co-captured at the two pores. Here, we systematically explore translocation physics during DNA tug-of-war focusing on genomically relevant longer dsDNA using a T4-DNA model (166 kbp). We find that longer molecules can be trapped in tug-of-war states with an asymmetric partitioning of contour between the pores. Secondly, we explore the physics of DNA disengagement from a tug-of-war configuration, focusing on the dynamics of DNA free-end escape, in particular how the free-end velocity depends on pore voltage, DNA size and the presence of additional DNA strands between the pores (i.e. arising in the presence of folded translocation). These findings validate theoretical predictions derived from a first passage model and provide new insight into the physical mechanisms governing molecule disengagement in tug-of-war.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12407631/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145002142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Multi-Layered Framework for Modeling Human Biology: From Basic AI Agents to a Full-Body AI Agent. 人类生物学建模的多层框架：从基本的AI代理到全身AI代理。

ArXiv Pub Date : 2025-08-27

Aoqi Wang, Jiajia Liu, Jianguo Wen, Yangyang Luo, Zhiwei Fan, Liren Yang, Xi Hu, Ruihan Luo, Yankai Yu, Sophia Li, Weiling Zhao, Xiaobo Zhou

{"title":"A Multi-Layered Framework for Modeling Human Biology: From Basic AI Agents to a Full-Body AI Agent.","authors":"Aoqi Wang, Jiajia Liu, Jianguo Wen, Yangyang Luo, Zhiwei Fan, Liren Yang, Xi Hu, Ruihan Luo, Yankai Yu, Sophia Li, Weiling Zhao, Xiaobo Zhou","doi":"","DOIUrl":"","url":null,"abstract":"We envision the Full-Body AI Agent as a comprehensive AI system designed to simulate, analyze, and optimize the dynamic processes of the human body across multiple biological levels. By integrating computational models, machine learning tools, and experimental platforms, this system aims to replicate and predict both physiological and pathological processes, ranging from molecules and cells to tissues, organs, and entire body systems. Central to the Full-Body AI Agent is its emphasis on integration and coordination across these biological levels, enabling analysis of how molecular changes influence cellular behaviors, tissue responses, organ function, and systemic outcomes. With a focus on biological functionality, the system is designed to advance the understanding of disease mechanisms, support the development of therapeutic interventions, and enhance personalized medicine. We propose two specialized implementations to demonstrate the utility of this framework: (1) the metastasis AI Agent, a multi-scale metastasis scoring system that characterizes tumor progression across the initiation, dissemination, and colonization phases by integrating molecular, cellular, and systemic signals; and (2) the drug AI Agent, a system-level drug development paradigm in which a drug AI-Agent dynamically guides preclinical evaluations, including organoids and chip-based models, by providing full-body physiological constraints. This approach enables the predictive modeling of long-term efficacy and toxicity beyond what localized models alone can achieve. These two agents illustrate the potential of Full-Body AI Agent to address complex biomedical challenges through multi-level integration and cross-scale reasoning.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12407623/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145002180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Physically-Based Inverse Rendering Framework for PET Image Reconstruction. 基于物理的PET图像重构逆绘制框架。

ArXiv Pub Date : 2025-08-27

Yixin Li, Soroush Shabani Sichani, Zipai Wang, Wanbin Tan, Baptiste Nicolet, Xiuyuan Wang, David A Muller, Gloria C Chiang, Wenzel Jakob, Amir H Goldan

{"title":"Physically-Based Inverse Rendering Framework for PET Image Reconstruction.","authors":"Yixin Li, Soroush Shabani Sichani, Zipai Wang, Wanbin Tan, Baptiste Nicolet, Xiuyuan Wang, David A Muller, Gloria C Chiang, Wenzel Jakob, Amir H Goldan","doi":"","DOIUrl":"","url":null,"abstract":"Differentiable rendering has been widely adopted in computer graphics as a powerful approach to inverse problems, enabling efficient gradient-based optimization by differentiating the image formation process with respect to millions of scene parameters. Inspired by this paradigm, we propose a physically-based inverse rendering (IR) framework, the first ever platform for PET image reconstruction using Dr.Jit, for PET image reconstruction. Our method integrates Monte Carlo sampling with an analytical projector in the forward rendering process to accurately model photon transport and physical process in the PET system. The emission image is iteratively optimized using voxel-wise gradients obtained via automatic differentiation, eliminating the need for manually derived update equations. The proposed framework was evaluated using both phantom studies and clinical brain PET data acquired from a Siemens Biograph mCT scanner. Implementing the Maximum Likelihood Expectation Maximization (MLEM) algorithm across both the CASToR toolkit and our IR framework, the IR reconstruction achieved a higher signal-to-noise ratio (SNR) and improved image quality compared to CASToR reconstructions. In clinical evaluation compared with the Siemens Biograph mCT platform, the IR reconstruction yielded higher hippocampal standardized uptake value ratios (SUVR) and gray-to-white matter ratios (GWR), indicating enhanced tissue contrast and the potential for more accurate tau localization and Braak staging in Alzheimer's disease assessment. The proposed IR framework offers a physically interpretable and extensible platform for high-fidelity PET image reconstruction, demonstrating strong performance in both phantom and real-world scenarios.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12407624/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145002120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Next Layer: Augmenting Foundation Models with Structure-Preserving and Attention-Guided Learning for Local Patches to Global Context Awareness in Computational Pathology. 下一层：基于结构保留和注意力引导学习的基础模型在计算病理学中的局部斑块到全局上下文感知。

ArXiv Pub Date : 2025-08-27

Muhammad Waqas, Rukhmini Bandyopadhyay, Eman Showkatian, Amgad Muneer, Anas Zafar, Frank Rojas Alvarez, Maricel Corredor Marin, Wentao Li, David Jaffray, Cara Haymaker, John Heymach, Natalie I Vokes, Luisa Maren Solis Soto, Jianjun Zhang, Jia Wu

{"title":"The Next Layer: Augmenting Foundation Models with Structure-Preserving and Attention-Guided Learning for Local Patches to Global Context Awareness in Computational Pathology.","authors":"Muhammad Waqas, Rukhmini Bandyopadhyay, Eman Showkatian, Amgad Muneer, Anas Zafar, Frank Rojas Alvarez, Maricel Corredor Marin, Wentao Li, David Jaffray, Cara Haymaker, John Heymach, Natalie I Vokes, Luisa Maren Solis Soto, Jianjun Zhang, Jia Wu","doi":"","DOIUrl":"","url":null,"abstract":"Foundation models have recently emerged as powerful feature extractors in computational pathology, yet they typically omit mechanisms for leveraging the global spatial structure of tissues and the local contextual relationships among diagnostically relevant regions - key elements for understanding the tumor microenvironment. Multiple instance learning (MIL) remains an essential next step following foundation model, designing a framework to aggregate patch-level features into slide-level predictions. We present EAGLE-Net, a structure-preserving, attention-guided MIL architecture designed to augment prediction and interpretability. EAGLE-Net integrates multi-scale absolute spatial encoding to capture global tissue architecture, a top-K neighborhood-aware loss to focus attention on local microenvironments, and background suppression loss to minimize false positives. We benchmarked EAGLE-Net on large pan-cancer datasets, including three cancer types for classification (10,260 slides) and seven cancer types for survival prediction (4,172 slides), using three distinct histology foundation backbones (REMEDIES, Uni-V1, Uni2-h). Across tasks, EAGLE-Net achieved up to 3% higher classification accuracy and the top concordance indices in 6 of 7 cancer types, producing smooth, biologically coherent attention maps that aligned with expert annotations and highlighted invasive fronts, necrosis, and immune infiltration. These results position EAGLE-Net as a generalizable, interpretable framework that complements foundation models, enabling improved biomarker discovery, prognostic modeling, and clinical decision support.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12407628/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145002140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

drGT: Attention-Guided Gene Assessment of Drug Response Utilizing a Drug-Cell-Gene Heterogeneous Network. drGAT：利用药物-细胞-基因异构网络，在注意力引导下对药物反应进行基因评估。

ArXiv Pub Date : 2025-08-27

Yoshitaka Inoue, Hunmin Lee, Tianfan Fu, Augustin Luna

{"title":"drGT: Attention-Guided Gene Assessment of Drug Response Utilizing a Drug-Cell-Gene Heterogeneous Network.","authors":"Yoshitaka Inoue, Hunmin Lee, Tianfan Fu, Augustin Luna","doi":"","DOIUrl":"","url":null,"abstract":"A challenge in drug response prediction is result interpretation compared to established knowledge. drGT is a graph deep learning model that predicts sensitivity and aids in biomarker identification using attention coefficients (ACs). drGT leverages a heterogeneous graph composed of relationships drawn from drugs, genes, and cell line responses. The model is trained and evaluated using major benchmark datasets: Sanger GDSC, NCI60, and Broad CTRP, which cover a wide range of drugs and cancer cell lines. drGT demonstrates AUROC of up to 94.5% under random splitting, 84.4% for unseen drugs, and 70.6% for unseen cell lines, comparable to existing benchmark methods while also providing interpretability. Regarding interpretability, we review drug-gene co-occurrences by text-mining PubMed abstracts for high-coefficient genes mentioning particular drugs. Across 976 drugs from NCI60 with known drug-target interactions (DTIs), model predictions utilized both known DTIs (36.9%) as well as additional predictive associations, many supported by literature. In addition, we compare the drug-gene associations identified by drGT with those from an established DTI prediction model and find that 63.67% are supported by either PubMed literature or predictions from the DTI model. Further, we describe the utilization of ACs to identify affected biological processes by each drug via enrichment analyses, thereby enhancing biological interpretability. Code is available at https://github.com/sciluna/drGT.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11118660/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141155961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Chemotaxing E. coli do not count single molecules. 大肠杆菌不计算单分子。

ArXiv Pub Date : 2025-08-27

Henry H Mattingly, Keita Kamino, Jude Ong, Rafaela Kottou, Thierry Emonet, Benjamin B Machta

{"title":"Chemotaxing E. coli do not count single molecules.","authors":"Henry H Mattingly, Keita Kamino, Jude Ong, Rafaela Kottou, Thierry Emonet, Benjamin B Machta","doi":"","DOIUrl":"","url":null,"abstract":"Organisms use specialized sensors to measure their environments, but the fundamental principles that determine their accuracy remain largely unknown. In Escherichia coli chemotaxis, we previously found that gradient-climbing speed is bounded by the amount of information that cells acquire from their environment, and that E. coli operate near this bound. However, it remains unclear what prevents them from acquiring more information. Past work argued that E. coli's chemosensing is limited by the physics of molecules stochastically arriving at cells' receptors, without direct evidence. Here, we show instead that E. coli are far from this physical limit. To show this, we develop a theoretical approach that uses information rates to quantify how accurately behaviorally-relevant signals can be estimated from available observations: molecule arrivals for the physical limit; chemotaxis signaling activity for E. coli. Measuring these information rates in single-cell experiments across multiple background concentrations, we find that E. coli encode two orders of magnitude less information than the physical limit. Thus, E. coli chemosensing is limited by internal noise in signal processing rather than the physics of molecule diffusion, motivating investigation of what specific physical and biological constraints shaped the evolution of this prototypical sensory system.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11261978/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141749898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DeepAtlas: a tool for effective manifold learning. DeepAtlas：一个有效的流形学习工具。

ArXiv Pub Date : 2025-08-26

Serena Hughes, Timothy Hamilton, Tom Kolokotrones, Eric J Deeds

引用次数: 0

Using PyBioNetFit to Leverage Qualitative and Quantitative Data in Biological Model Parameterization and Uncertainty Quantification. 利用PyBioNetFit在生物模型参数化和不确定性量化中利用定性和定量数据。

ArXiv Pub Date : 2025-08-26

Ely F Miller, Abhishek Mallela, Jacob Neumann, Yen Ting Lin, William S Hlavacek, Richard G Posner

{"title":"Using PyBioNetFit to Leverage Qualitative and Quantitative Data in Biological Model Parameterization and Uncertainty Quantification.","authors":"Ely F Miller, Abhishek Mallela, Jacob Neumann, Yen Ting Lin, William S Hlavacek, Richard G Posner","doi":"","DOIUrl":"","url":null,"abstract":"Data generated in studies of cellular regulatory systems are often qualitative. For example, measurements of signaling readouts in the presence and absence of mutations may reveal a rank ordering of responses across conditions but not the precise extents of mutation-induced differences. Qualitative data are often ignored by mathematical modelers or are considered in an ad hoc manner, as in the study of Kocieniewski and Lipniacki (2013) [Phys Biol 10: 035006], which was focused on the roles of MEK isoforms in ERK activation. In this earlier study, model parameter values were tuned manually to obtain consistency with a combination of qualitative and quantitative data. This approach is not reproducible, nor does it provide insights into parametric or prediction uncertainties. Here, starting from the same data and the same ordinary differential equation (ODE) model structure, we generate formalized statements of qualitative observations, making these observations more reusable, and we improve the model parameterization procedure by applying a systematic and automated approach enabled by the software package PyBioNetFit. We also demonstrate uncertainty quantification (UQ), which was absent in the original study. Our results show that PyBioNetFit enables qualitative data to be leveraged, together with quantitative data, in parameterization of systems biology models and facilitates UQ. These capabilities are important for reliable estimation of model parameters and model analyses in studies of cellular regulatory systems and reproducibility.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12407630/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145002135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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