bioRxiv : the preprint server for biology最新文献

{"title":"Single-cell type analysis of wing premotor circuits in the ventral nerve cord of <i>Drosophila melanogaster</i>.","authors":"Erica Ehrhardt, Samuel C Whitehead, Shigehiro Namiki, Ryo Minegishi, Igor Siwanowicz, Kai Feng, Hideo Otsuna, Geoffrey W Meissner, David Stern, Jim Truman, David Shepherd, Michael H Dickinson, Kei Ito, Barry J Dickson, Itai Cohen, Gwyneth M Card, Wyatt Korff","doi":"10.1101/2023.05.31.542897","DOIUrl":"10.1101/2023.05.31.542897","url":null,"abstract":"<p><p>To perform most behaviors, animals must send commands from higher-order processing centers in the brain to premotor circuits that reside in ganglia distinct from the brain, such as the mammalian spinal cord or insect ventral nerve cord. How these circuits are functionally organized to generate the great diversity of animal behavior remains unclear. An important first step in unraveling the organization of premotor circuits is to identify their constituent cell types and create tools to monitor and manipulate these with high specificity to assess their functions. This is possible in the tractable ventral nerve cord of the fly. To generate such a toolkit, we used a combinatorial genetic technique (split-GAL4) to create 195 sparse transgenic driver lines targeting 196 individual cell types in the ventral nerve cord. These included wing and haltere motoneurons, modulatory neurons, and interneurons. Using a combination of behavioral, developmental, and anatomical analyses, we systematically characterized the cell types targeted in our collection. In addition, we identified correspondences between the cells in this collection and a recent connectomic data set of the ventral nerve cord. Taken together, the resources and results presented here form a powerful toolkit for future investigations of neuronal circuits and connectivity of premotor circuits while linking them to behavioral outputs.</p>","PeriodicalId":72407,"journal":{"name":"bioRxiv : the preprint server for biology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10312520/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9791678","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}

{"title":"Mapping Alzheimer's Molecular Pathologies in Large-Scale Connectomics Data: A Publicly Accessible Correlative Microscopy Resource.","authors":"Xiaomeng Han, Peter H Li, Shuohong Wang, Tim Blakely, Sneha Aggarwal, Bhavika Gopalani, Morgan Sanchez, Richard Schalek, Yaron Meirovitch, Zudi Lin, Daniel Berger, Yuelong Wu, Fatima Aly, Sylvie Bay, Benoît Delatour, Pierre Lafaye, Hanspeter Pfister, Donglai Wei, Viren Jain, Hidde Ploegh, Jeff Lichtman","doi":"10.1101/2023.10.24.563674","DOIUrl":"10.1101/2023.10.24.563674","url":null,"abstract":"<p><p>Connectomics using volume-electron-microscopy enables mapping and analysis of neuronal networks, revealing insights into neural circuit function and dysfunction. In Alzheimer's disease (AD), where amyloid-β (Aβ) and hyperphosphorylated-Tau (pTau) are implicated, connectomics offers an approach to unravel how these molecules contribute to circuit alterations by enabling the study of these molecules within the context of the complete local neuronal and glial milieu. We present a volumetric-correlated-light-and-electron microscopy (vCLEM) protocol using fluorescent nanobodies to localize Aβ and pTau within a large-scale connectomics dataset from the hippocampus of the 3xTg AD mouse model. A key outcome of this work is a publicly accessible vCLEM dataset, featuring fluorescent labeling of Aβ and pTau in the ultrastructural context with segmented neurons, glia, and synapses. This dataset provides a unique resource for exploring AD pathology in the context of connectomics and fosters collaborative opportunities in neurodegenerative disease research. As a proof-of-principle, we uncovered new localizations of Aβ and pTau, including pTau-positive spine-like protrusions at the axon initial segment and changes in the number and size of synapses near Aβ plaques. Our vCLEM approach facilitates the discovery of both molecular and structural alterations within large-scale EM data, advancing connectomics research in Alzheimer's and other neurodegenerative diseases.</p>","PeriodicalId":72407,"journal":{"name":"bioRxiv : the preprint server for biology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10634883/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92157587","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}

{"title":"Towards a standard benchmark for phenotype-driven variant and gene prioritisation algorithms: PhEval - Phenotypic inference Evaluation framework.","authors":"Yasemin Bridges, Vinicius de Souza, Katherina G Cortes, Melissa Haendel, Nomi L Harris, Daniel R Korn, Nikolaos M Marinakis, Nicolas Matentzoglu, James A McLaughlin, Christopher J Mungall, Aaron Odell, David Osumi-Sutherland, Peter N Robinson, Damian Smedley, Julius Ob Jacobsen","doi":"10.1101/2024.06.13.598672","DOIUrl":"10.1101/2024.06.13.598672","url":null,"abstract":"<p><strong>Background: </strong>Computational approaches to support rare disease diagnosis are challenging to build, requiring the integration of complex data types such as ontologies, gene-to-phenotype associations, and cross-species data into variant and gene prioritisation algorithms (VGPAs). However, the performance of VGPAs has been difficult to measure and is impacted by many factors, for example, ontology structure, annotation completeness or changes to the underlying algorithm. Assertions of the capabilities of VGPAs are often not reproducible, in part because there is no standardised, empirical framework and openly available patient data to assess the efficacy of VGPAs - ultimately hindering the development of effective prioritisation tools.</p><p><strong>Results: </strong>In this paper, we present our benchmarking tool, PhEval, which aims to provide a standardised and empirical framework to evaluate phenotype-driven VGPAs. The inclusion of standardised test corpora and test corpus generation tools in the PhEval suite of tools allows open benchmarking and comparison of methods on standardised data sets.</p><p><strong>Conclusions: </strong>PhEval and the standardised test corpora solve the issues of patient data availability and experimental tooling configuration when benchmarking and comparing rare disease VGPAs. By providing standardised data on patient cohorts from real-world case-reports and controlling the configuration of evaluated VGPAs, PhEval enables transparent, portable, comparable and reproducible benchmarking of VGPAs. As these tools are often a key component of many rare disease diagnostic pipelines, a thorough and standardised method of assessment is essential for improving patient diagnosis and care.</p>","PeriodicalId":72407,"journal":{"name":"bioRxiv : the preprint server for biology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11195176/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141447751","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}

{"title":"Guided Differentiation of Pluripotent Stem Cells into Heterogeneously Differentiating Cultures of Cardiac Cells.","authors":"Erik McIntire, Kenneth A Barr, Natalia M Gonzales, Olivia L Allen, Yoav Gilad","doi":"10.1101/2023.07.21.550072","DOIUrl":"10.1101/2023.07.21.550072","url":null,"abstract":"<p><p>In principle, induced pluripotent stem cells (iPSCs) can differentiate into any cell type in the body. The challenge is to find a way to rapidly expand the dimensionality of cell types and cell states we can characterize. To address this, we developed a guided differentiation protocol to produce heterogeneous differentiating cultures of cardiac cell types (cardiac HDCs) in 16 days. Cardiac HDCs are three-dimensional, rhythmically contracting cell aggregates that harbor a temporally and functionally diverse range of cardiac-relevant cell types. We characterize cardiac HDCs from 47 iPSC lines using single-cell RNA-sequencing to identify cardiomyocytes, epicardial cells, cardiac fibroblasts, endothelial cells, and hematopoietic cells, along with both ectodermal and endodermal derivatives. This guided differentiation approach prioritizes simplicity by minimizing the reagents and steps required, thereby enabling rapid and cost-effective experimental throughput. We expect cardiac HDCs to provide a scalable cardiac model for population-level studies of gene regulatory variation and gene-by-environment interactions.</p>","PeriodicalId":72407,"journal":{"name":"bioRxiv : the preprint server for biology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/99/31/nihpp-2023.07.21.550072v1.PMC10370173.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9927211","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}

{"title":"Septin organization is regulated by the Gpa1 Ubiquitination Domain and Endocytic Machinery during the yeast pheromone response.","authors":"Cory P Johnson, Sudati Shrestha, Andrew Hart, Katherine F Jarvis, Loren E Genrich, Sarah G Latario, Nicholas Leclerc, Tetiana Systuk, Matthew Scandura, Remi P Geohegan, André Khalil, Joshua B Kelley","doi":"10.1101/2023.06.16.545321","DOIUrl":"10.1101/2023.06.16.545321","url":null,"abstract":"<p><p>The septin cytoskeleton plays a key role in the morphogenesis of the yeast mating projection, forming structures at the base of the projection. The yeast mating response uses the G-protein coupled receptor (GPCR), Ste2, to detect mating pheromone and initiate mating projection morphogenesis. Desensitization of the Gα, Gpa1, by the Regulator of G-protein Signaling (RGS), Sst2, is required for proper septin organization and morphogenesis. We hypothesized that Gpa1 would utilize known septin regulators to control septin organization. We found that single deletions of the septin chaperone Gic1, the Cdc42 GAP Bem3, and the endocytic adaptor proteins Ent1 and Ent2 rescued the polar cap accumulation of septins in the hyperactive Gα. We hypothesized that hyperactive Gα might increase the rate of endocytosis of a pheromone-responsive cargo, thereby altering where septins are localized. Mathematical modeling predicted that changes in endocytosis could explain the septin organizations we find in WT and mutant cells. Our results show that Gpa1-induced disorganization of septins requires clathrin-mediated endocytosis. Both the GPCR and the Gα are known to be internalized by clathrin-mediated endocytosis during the pheromone response. Deletion of the GPCR C-terminus to block internalization partially rescued septin organization. However, deleting the Gpa1 ubiquitination domain required for its endocytosis completely abrogated septin accumulation at the polarity site. Our data support a model where the location of endocytosis serves as a spatial mark for septin structure assembly and that desensitization of the Gα delays its endocytosis sufficiently that septins are placed peripheral to the site of Cdc42 polarity.</p>","PeriodicalId":72407,"journal":{"name":"bioRxiv : the preprint server for biology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/d7/58/nihpp-2023.06.16.545321v1.PMC10312744.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9751602","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}

{"title":"Gene expression variation across genetically identical individuals predicts reproductive traits.","authors":"Amy K Webster, John H Willis, Erik Johnson, Peter Sarkies, Patrick C Phillips","doi":"10.1101/2023.10.13.562270","DOIUrl":"10.1101/2023.10.13.562270","url":null,"abstract":"<p><p>In recent decades, genome-wide association studies (GWAS) have been the major approach to understand the biological basis of individual differences in traits and diseases. However, GWAS approaches have limited predictive power to explain individual differences, particularly for complex traits and diseases in which environmental factors play a substantial role in their etiology. Indeed, individual differences persist even in genetically identical individuals, although fully separating genetic and environmental causation is difficult in most organisms. To understand the basis of individual differences in the absence of genetic differences, we measured two quantitative reproductive traits in 180 genetically identical young adult <i>Caenorhabditis elegans</i> roundworms in a shared environment and performed single-individual transcriptomics on each worm. We identified hundreds of genes for which expression variation was strongly associated with reproductive traits, some of which depended on individuals' historical environments and some of which was random. Multiple small sets of genes together were highly predictive of reproductive traits, explaining on average over half and over a quarter of variation in the two traits. We manipulated mRNA levels of predictive genes to identify a set of causal genes, demonstrating the utility of this approach for both prediction and understanding underlying biology. Finally, we found that the chromatin environment of predictive genes was enriched for H3K27 trimethylation, suggesting that gene expression variation may be driven in part by chromatin structure. Together, this work shows that individual, non-genetic differences in gene expression are both highly predictive and causal in shaping reproductive traits.</p>","PeriodicalId":72407,"journal":{"name":"bioRxiv : the preprint server for biology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10592811/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49694486","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}

{"title":"Multimodal identification of the mouse brain using simultaneous Ca ²⁺ imaging and fMRI.","authors":"Francesca Mandino, Corey Horien, Xilin Shen, Gabriel Desrosiers-Grégoire, Wendy Luo, Marija Markicevic, R Todd Constable, Xenophon Papademetris, Mallar M Chakravarty, Richard F Betzel, Evelyn M R Lake","doi":"10.1101/2024.05.24.594620","DOIUrl":"10.1101/2024.05.24.594620","url":null,"abstract":"<p><p>Individual differences in neuroimaging are of interest to clinical and cognitive neuroscientists based on their potential for guiding the personalized treatment of various heterogeneous neurological conditions and diseases. Despite many advantages, the workhorse in this arena, BOLD (blood-oxygen-level-dependent) functional magnetic resonance imaging (fMRI) suffers from low spatiotemporal resolution and specificity as well as a propensity for noise and spurious signal corruption. To better understand individual differences in BOLD-fMRI data, we can use animal models where fMRI, alongside complementary but more invasive contrasts, can be accessed. Here, we apply simultaneous wide-field fluorescence calcium imaging and BOLD-fMRI in mice to interrogate individual differences using a connectome-based identification framework adopted from the human fMRI literature. This approach yields high spatiotemporal resolution cell-type specific signals (here, from glia, excitatory, as well as inhibitory interneurons) from the whole cortex. We found mouse multimodal connectome-based identification to be successful and explored various features of these data.</p>","PeriodicalId":72407,"journal":{"name":"bioRxiv : the preprint server for biology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11142213/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141201477","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}

{"title":"Multimodal delineation of a layer of effector function among exhausted CD8 T cells in tumors.","authors":"Arja Ray, Molly Bassette, Kenneth H Hu, Lomax F Pass, Tristan Courau, Bushra Samad, Alexis Combes, Vrinda Johri, Brittany Davidson, Katherine Wai, Patrick Ha, Grace Hernandez, Itzia Zaleta-Linares, Matthew F Krummel","doi":"10.1101/2023.09.26.559470","DOIUrl":"10.1101/2023.09.26.559470","url":null,"abstract":"<p><p>The anti-tumor function of CD8 T cells is limited through well-established pathways of T cell exhaustion (T_EX). Strategies to capture emergent functional states amongst this dominant trajectory of dysfunction are necessary to find pathways to durable anti-tumor immunity. By leveraging transcriptional reporting (by the fluorescent protein TFP) of the T cell activation marker <i>Cd69,</i> related to upstream AP-1 transcription factors, we define a classifier for potent versus suboptimal CD69+ activation states arising from T cell stimulation. In tumors, this delineation acts an additional functional readout along the T_EX differentiation trajectory, within and across T_EX subsets, marked by enhanced effector cytokine and granzyme B production. The more potent state remains differentially prominent in a T cell-mediated tumor clearance model, where they also show increased engagement in the microenvironment and are superior in tumor cell killing. Employing multimodal CITE-Seq in human head and neck tumors enables a similar strategy to identify Cd69RNA^hiCD69+ cells that also have enhanced functional features in comparison to Cd69RNA^loCD69+ cells, again within and across intratumoral CD8 T cell subsets. Refining the contours of the T cell functional landscape in tumors in this way paves the way for the identification of rare exceptional effectors, with imminent relevance to cancer treatment.</p>","PeriodicalId":72407,"journal":{"name":"bioRxiv : the preprint server for biology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/04/1a/nihpp-2023.09.26.559470v1.PMC10557647.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41157185","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}

{"title":"CellSAM: A Foundation Model for Cell Segmentation.","authors":"Uriah Israel, Markus Marks, Rohit Dilip, Qilin Li, Changhua Yu, Emily Laubscher, Ahamed Iqbal, Elora Pradhan, Ada Ates, Martin Abt, Caitlin Brown, Edward Pao, Shenyi Li, Alexander Pearson-Goulart, Pietro Perona, Georgia Gkioxari, Ross Barnowski, Yisong Yue, David Van Valen","doi":"10.1101/2023.11.17.567630","DOIUrl":"10.1101/2023.11.17.567630","url":null,"abstract":"<p><p>Cells are a fundamental unit of biological organization, and identifying them in imaging data - cell segmentation - is a critical task for various cellular imaging experiments. While deep learning methods have led to substantial progress on this problem, most models are specialist models that work well for specific domains but cannot be applied across domains or scale well with large amounts of data. In this work, we present CellSAM, a universal model for cell segmentation that generalizes across diverse cellular imaging data. CellSAM builds on top of the Segment Anything Model (SAM) by developing a prompt engineering approach for mask generation. We train an object detector, CellFinder, to automatically detect cells and prompt SAM to generate segmentations. We show that this approach allows a single model to achieve human-level performance for segmenting images of mammalian cells, yeast, and bacteria collected across various imaging modalities. We show that CellSAM has strong zero-shot performance and can be improved with a few examples via few-shot learning. Additionally, we demonstrate how CellSAM can be applied across diverse bioimage analysis workflows. A deployed version of CellSAM is available at https://cellsam.deepcell.org/.</p>","PeriodicalId":72407,"journal":{"name":"bioRxiv : the preprint server for biology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10690226/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138479505","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}

{"title":"Changes of cell-type diversity in the polyp-to-medusa metagenesis of the scyphozoan jellyfish <i>Aurelia coerulea</i> (formerly sp.1).","authors":"Oliver Link, Stefan M Jahnel, Kristin Janicek, Johanna Kraus, Juan Daniel Montenegro, Bob Zimmerman, Brittney Wick, Alison G Cole, Ulrich Technau","doi":"10.1101/2023.08.24.554571","DOIUrl":"10.1101/2023.08.24.554571","url":null,"abstract":"<p><p>The life cycle of most medusozoan cnidarians is marked by the metagenesis from the asexually reproducing sessile polyp and the sexually reproducing motile medusa. At present it is unknown to what extent this drastic morphological transformation is accompanied by changes in the cell type composition. Here, we provide a single cell transcriptome atlas of the cosmopolitan scyphozoan <i>Aurelia coerulea</i> focussing on changes in cell-type composition during the transition from polyp to medusa. Notably, this transition marked by an increase in cell type diversity, including an expansion of neural subtypes. We find that two families of neuronal lineages are specified by homologous transcription factors in the sea anemone <i>Nematostella vectensis</i> and <i>Aurelia coerulea</i>, suggesting an origin in the common ancestor of medusozoans and anthozoans about 500 Myr ago. Our analysis suggests that gene duplications might be drivers for the increase of cellular complexity during the evolution of cnidarian neuroglandular lineages. One key medusozoan-specific cell type is the striated muscle in the subumbrella. Analysis of muscle fiber anatomy and gene expression raises the possibility that the striated muscles arise from a population of smooth muscle cells during strobilation. Although smooth and striated muscles are phenotypically distinct, both have a similar contractile complex, in contrast to bilaterian smooth and striated muscles. This suggests that in <i>Aurelia</i>, smooth and striated muscle cells may derive from the same progenitor cells.</p>","PeriodicalId":72407,"journal":{"name":"bioRxiv : the preprint server for biology","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11844373/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84405827","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}