PLoS Computational Biology最新文献

{"title":"A mixture of attention experts-embedded flow-based generative model to create synthetic cells in single-cell RNA-Seq datasets.","authors":"Sultan Sevgi Turgut Ögme, Nizamettin Aydin, Zeyneb Kurt","doi":"10.1371/journal.pcbi.1013525","DOIUrl":"10.1371/journal.pcbi.1013525","url":null,"abstract":"<p><p>Single-cell RNA-seq (scRNAseq) analyses performed at the cellular level aim to understand the cellular landscape of tissue sections, offer insights into rare cell-types, and identify marker genes for annotating distinct cell types. ScRNAseq analyses are widely applied to cancer research to understand tumor heterogeneity, disease progression, and resistance to therapy. Single-cell data processing is a challenging task due to its high-dimensionality, sparsity, and having imbalanced class(cell-type) distributions. An accurate cell-type identification is highly dependent on preprocessing and quality control steps. To address these issues, generative models have been widely used in recent years. Techniques frequently used include Variational Autoencoders (VAE), Generative Adversarial Networks (GANs), Gaussian-based methods, and, more recently, Flow-based (FB) generative models. We developed a Masked Affine Autoregressive transform-embedded FB (MAF-FB) model. Then, to improve MAF-FB further, we incorporated a mixture of experts (MOE) of attention mechanisms on top of it, resulting in our proposed MOE-FB model. We conducted a comparative analysis of fundamental generative models, aiming to serve as a preliminary guidance for developing novel automated scRNAseq data analysis systems. We performed a large-scale analysis by combiningfour datasets derived from pancreatic tissue sections and for further generalizability assessments, we employed Peripheral Blood Mononuclear Cells (PBMC68K and PBMC3K) and Human Cell Atlas Bone Marrow (HCA-BM10K) datasets. We utilized VAE, GAN, Gaussian Copula, and Automated Cell-Type-informed Introspective Variational Autoencoder (ACTIVA), and compared them against our two novel FB models, MAF-FB and MOE-FB for ScRnaseq synthesis. To evaluate the performances of generative models, we used various discrepancy metrics and performed automated cell-type classification tasks. We also identified differentially expressed genes for each cell type, and inferred cell-cell interactions based on ligand-receptor bindings across distinct cell-type pairs. Among the generative models, FB models, especially MOE-FB, consistently outperformed others across all experimental setups in both discrepancy metrics with comparison to the baseline test set and cell-type classification tasks (with an F1-score of 0.90 precision of 0.89 and recall of 0.92 for the integrated pancreatic datasets). MOE-FB produced biologically more relevant synthetic data, and ligand-receptor-based cell-cell interactions inferred from the synthetic cells closely resemble the original data, achieving an RMSE of 0.65 against the corresponding pancreatic test set. These findings highlight the potential and promising use of FB models, especially MOE-FB, in scRNAseq analyses.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 10","pages":"e1013525"},"PeriodicalIF":3.6,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12500167/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145239326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Host responses and viral traits interact to shape the impacts of climate warming on highly pathogenic avian influenza in migratory waterfowl.","authors":"Claire S Teitelbaum, Michael L Casazza, Cory T Overton, Elliott L Matchett, Diann J Prosser","doi":"10.1371/journal.pcbi.1013451","DOIUrl":"10.1371/journal.pcbi.1013451","url":null,"abstract":"<p><p>Emerging infectious diseases pose threats to wildlife populations, as exemplified by recent outbreaks of avian influenza viruses in wild birds. Climate change can affect infection dynamics in wildlife through direct effects on pathogens (e.g., environmental decay rates) and changes to host ecology, including shifting migration patterns. Here, we adapt an existing mechanistic model that couples migration and infection to study how traits of highly pathogenic avian influenza (HPAI) viruses contribute to HPAI outcomes in migratory waterfowl, then apply this model to explore potential impacts of climate change on HPAI dynamics. We find that the simulated impacts of HPAI on the host population under baseline climate conditions varied from no impact to 100% mortality, depending on viral traits. In most cases, traits related to transmission (i.e., contact rates, shedding rates) were more important for HPAI establishment probability, infection prevalence, and mortality than were other viral traits (e.g., environmental temperature sensitivity, cross-protective immunity). We then simulated the effects of climate change (i.e., altered temperature regimes) on HPAI dynamics both via viral environmental decay and via changes in bird migration phenology. In these simulations, we found that a 9-day advancement in spring migration timing increased the duration of HPAI outbreaks by increasing time birds spent at their breeding grounds, leading to higher mortality and fewer infections. In contrast, increased viral decay in warmer years had a smaller, but opposite impact. These patterns depended on the primary transmission mode of HPAI (i.e., direct vs. environmental) and its sensitivity to environmental temperatures. Together, these results suggest that climate change is likely to increase the impacts of HPAI on waterfowl populations if HPAI relies strongly on direct transmission and birds advance their spring migration. Further integrating host-viral co-evolution and other climatic changes (e.g., salinity, humidity) could provide more precise predictions of how HPAI dynamics could change in the future.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 10","pages":"e1013451"},"PeriodicalIF":3.6,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12513652/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145239364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding the high-order network plasticity mechanisms of ultrasound neuromodulation.","authors":"Marilyn Gatica, Cyril Atkinson-Clement, Carlos Coronel-Oliveros, Mohammad Alkhawashki, Pedro A M Mediano, Enzo Tagliazucchi, Fernando E Rosas, Marcus Kaiser, Giovanni Petri","doi":"10.1371/journal.pcbi.1013514","DOIUrl":"10.1371/journal.pcbi.1013514","url":null,"abstract":"<p><p>Transcranial ultrasound stimulation (TUS) is an emerging non-invasive neuromodulation technique, offering a potential alternative to pharmacological treatments for psychiatric and neurological disorders. While functional analysis has been instrumental in characterizing the TUS effects, understanding its indirect influence across the network remains challenging. Here, we developed a whole-brain model to represent functional changes as measured by fMRI, enabling us to investigate how TUS-induced effects propagate throughout the brain with increasing stimulus intensity. We implemented two mechanisms: one based on anatomical distance and another on broadcasting dynamics, to explore plasticity-driven changes in specific brain regions. Finally, we highlighted the role of higher-order functional interactions in localizing spatial effects of off-line TUS at two target areas-the right thalamus and inferior frontal cortex-revealing distinct patterns of functional reorganization. This work lays the foundation for mechanistic insights and predictive models of TUS, advancing its potential clinical applications.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 10","pages":"e1013514"},"PeriodicalIF":3.6,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12500120/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145239357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Bayesian model for repeated cross-sectional epidemic prevalence survey data.","authors":"Nicholas Steyn, Marc Chadeau-Hyam, Paul Elliott, Christl A Donnelly","doi":"10.1371/journal.pcbi.1013515","DOIUrl":"10.1371/journal.pcbi.1013515","url":null,"abstract":"<p><p>Epidemic prevalence surveys monitor the spread of an infectious disease by regularly testing representative samples of a population for infection. State-of-the-art Bayesian approaches for analysing epidemic survey data were constructed independently and under pressure during the COVID-19 pandemic. In this paper, we compare two existing approaches (one leveraging Bayesian P-splines and the other approximate Gaussian processes) with a novel approach (leveraging a random walk and fit using sequential Monte Carlo) for smoothing and performing inference on epidemic survey data. We use our simpler approach to investigate the impact of survey design and underlying epidemic dynamics on the quality of estimates. We then incorporate these considerations into the existing approaches and compare all three on simulated data and on real-world data from the SARS-CoV-2 REACT-1 prevalence study in England. All three approaches, once appropriate considerations are made, produce similar estimates of infection prevalence; however, estimates of the growth rate and instantaneous reproduction number are more sensitive to underlying assumptions. Interactive notebooks applying all three approaches are also provided alongside recommendations on hyperparameter selection and other practical guidance, with some cases resulting in orders-of-magnitude faster runtime.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 10","pages":"e1013515"},"PeriodicalIF":3.6,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12507252/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145225486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multi-layer encoder prediction model for individual sample specific gene combination effect (MLEC-iGeneCombo).","authors":"Yun Shen, Kunjie Fan, Birkan Gökbağ, Nuo Sun, Chen Yang, Lijun Cheng, Lang Li","doi":"10.1371/journal.pcbi.1013547","DOIUrl":"10.1371/journal.pcbi.1013547","url":null,"abstract":"<p><p>Using data from gene combination double knockout (CDKO) experiments, top ranked synthetic lethal (SL) gene pairs were highly inconsistent among different SL scores. This leads to a significant concern that SL prediction models highly depend on SL scores. In this paper, we introduce a new gene combination effect (GCE) measurement, log-fold change of dual-gRNA expression before and after CRISPR-cas9 lentivirus transfection. We show it is a direct and highly consistent measurement of GCE in all CDKO experiments. We therefore develop a multi-layer encoder model for individual sample specific GCE prediction, MLEC-iGeneCombo. Under a deep learning framework, MLEC-iGeneCombo is a systems biology model that contains sample specific multi-omics encoder, network encoder and cell-line encoder. For the first time, MLEC-iGeneCombo predicts GCE for a new cell. Using data from 18 CDKO experiments, MLEC-iGeneCombo achieves an average GCE prediction performance, 71.9%. All three encoders significantly improve the model's prediction performance (p[Formula: see text]), and their combined use yields the best GCE prediction performance. Our source code is available at https://github.com/karenyun/MLEC-iGeneCombo.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 10","pages":"e1013547"},"PeriodicalIF":3.6,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12510635/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145225559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Three types of remapping with linear decoders: A population-geometric perspective.","authors":"Guillermo Martín-Sánchez, Christian K Machens, William F Podlaski","doi":"10.1371/journal.pcbi.1013545","DOIUrl":"10.1371/journal.pcbi.1013545","url":null,"abstract":"<p><p>Hippocampal remapping, in which place cells form distinct activity maps across different environments, is a well-established phenomenon with a range of theoretical interpretations. Some theories propose that remapping helps to minimize interference between competing spatial memories, whereas others link it to shifts in an underlying latent state representation. However, how these interpretations of remapping relate to one another, and what types of activity changes they are compatible with, remains unclear. To unify and elucidate the mechanisms behind remapping, we here adopt a neural coding and population geometry perspective. Assuming that hippocampal population activity can be understood through a linearly-decodable latent space, we show that there are three possible mechanisms to induce remapping: (i) a true change in the mapping between neural and latent space, (ii) modulation of activity due to non-spatial mixed selectivity of place cells, or (iii) neural variability in the null space of the latent space that reflects a redundant code. We simulate and visualize examples of these remapping types in a network model, and relate the resultant remapping behavior to various models and experimental findings in the literature. Overall, our work serves as a unifying framework with which to visualize, understand, and compare the wide array of theories and experimental observations about remapping, and may serve as a testbed for understanding neural response variability under various experimental conditions.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 10","pages":"e1013545"},"PeriodicalIF":3.6,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12510668/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145225862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantifying HiPSC-CM structural organization at scale with deep learning-enhanced SarcGraph.","authors":"Saeed Mohammadzadeh, Emma Lejeune","doi":"10.1371/journal.pcbi.1013436","DOIUrl":"10.1371/journal.pcbi.1013436","url":null,"abstract":"<p><p>In cardiac cells, structural organization is an important indicator of cell maturity and healthy function. Healthy and mature cardiomyocytes exhibit a highly organized structure, characterized by well-aligned almost crystalline morphology with densely packed and organized sarcomeres. Immature and/or diseased cardiomyocytes typically lack this highly organized structure. Critically, human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) offer a valuable model for studying human cardiac cells in a controlled, patient-specific, and minimally invasive manner. However, these cells often exhibit a disorganized and difficult to quantify structure both in their immature form and as disease models. In this work, we extend the SarcGraph computational framework-designed specifically to assess the structural and functional behavior of hiPSC-CMs-to better accommodate the structural features of immature cells. There are two key enhancements: (1) incorporating a deep learning-based z-disc classifier, and (2) introducing a novel ensemble graph-scoring approach. These modification significantly reduced false positive sarcomere detections, particularly in immature cells, and improved the detection of longer myofibrils in mature samples. With this enhanced framework, we analyze an open-source dataset published by the Allen Institute for Cell Science, where, for the first time, we are able to extract key structural features from these data using information from each individually detected sarcomere. Not only are we able to use these structural features to predict expert scores, but we are also able to use these structural features to identify bias in expert scoring and offer an alternative unsupervised learning approach based on explainable clustering. These results demonstrate the efficacy of our modified SarcGraph algorithm in extracting biologically meaningful structural features, enabling a deeper understanding of hiPSC-CM structural integrity. By making our code and tools open-source, we aim to empower the broader cardiac research community and foster further development of computational tools for cardiac tissue analysis.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 10","pages":"e1013436"},"PeriodicalIF":3.6,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12520406/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145225467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modelling plant disease spread and containment: Simulation and approximate Bayesian Computation for Xylella fastidiosa in Puglia, Italy.","authors":"Daniel Chapman, Flavia Occhibove, James M Bullock, Pieter S A Beck, Juan A Navas-Cortes, Steven M White","doi":"10.1371/journal.pcbi.1013539","DOIUrl":"10.1371/journal.pcbi.1013539","url":null,"abstract":"<p><p>Mathematical and computational models play a crucial role in understanding the epidemiology of economically important plant disease outbreaks, and in evaluating the effectiveness of surveillance and disease management measures. A case in point is Xylella fastidiosa, one of the world's most deadly plant pathogens. Since its European discovery in olives in Puglia, Italy in 2013, there remain key knowledge gaps that undermine landscape-scale containment efforts of the outbreak, most notably concerning the year of introduction, the rate of spread, dispersal mechanisms and control efficacy. To address this, we developed a spatially explicit simulation model for the outbreak spreading among olive groves coupled to a simulation of the real surveillance and containment measures. We used Approximate Bayesian Computation to fit the model to surveillance and remote-sensing infection data, comparing the fits for three alternative dispersal mechanisms (isotropic, wind and road). The model accurately explained the rate and spatiotemporal pattern of the outbreak and found weak support for the wind dispersal model over the isotropic model. It suggests that the bacterium may have been introduced as early as 2003 (95% CI [2000, 2009]), earlier than previous estimates and congruent with anecdotal evidence. The isotropic model estimates the pathogen is spreading at 5.7 km y-1 (95% CI [5.4-5.9]) under containment measures, down from 7.2 km y-1 (95% CI [6.9-7.5]) without containment measures. Our estimate of an approximately 10-year lag between introduction and detection highlights the need for stronger biosecurity and surveillance for earlier detection of emerging plant pathogens. The outputs from simulations without any disease management also suggest that while containment measures have caused some slowing of X. fastidiosa spread, stronger measures will be required to contain the outbreak fully.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 10","pages":"e1013539"},"PeriodicalIF":3.6,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12510647/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145225470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The quarantine hospital strategy as a way to reduce both community and nosocomial transmission in the context of a COVID-like epidemic.","authors":"Théo Pinettes, Quentin J Leclerc, Kévin Jean, Laura Temime","doi":"10.1371/journal.pcbi.1013548","DOIUrl":"10.1371/journal.pcbi.1013548","url":null,"abstract":"<p><p>Nosocomial infections of both patients and healthcare workers (HCWs) in hospitals may play an important part in the overall dynamics of a viral pandemic, as evidenced by the recent COVID-19 experience. A strategy to control this risk consists in dedicating some hospitals to the care of infected patients only, with HCWs alternating between shifts of continuous stay within these hospitals and periods of isolation. This strategy has been implemented locally in various settings and generalized in Egypt. Here, using a mathematical model coupling hospitals and community, we assess the impact of this strategy on overall epidemic dynamics. We find that quarantine hospitals may significantly reduce the number of cumulative cases, as well as the peak incidence, when effective control strategies are in place in the community and symptomatic HCWs comply with self-isolation recommendations. Our results, which are robust to variations in assumed biological characteristics of the virus, suggest that the quarantine hospital strategy could be considered in future pandemic contexts to best protect the entire population.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 10","pages":"e1013548"},"PeriodicalIF":3.6,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12507276/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145225661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving policy design and epidemic response using integrated models of economic choice and disease dynamics with behavioral feedback.","authors":"Hongru Du, Matthew V Zahn, Sara L Loo, Tijs W Alleman, Shaun Truelove, Bryan Patenaude, Lauren M Gardner, Nicholas Papageorge, Alison L Hill","doi":"10.1371/journal.pcbi.1013549","DOIUrl":"10.1371/journal.pcbi.1013549","url":null,"abstract":"<p><p>Human behavior plays a crucial role in infectious disease transmission, yet traditional models often overlook or oversimplify this factor, limiting predictions of disease spread and the associated socioeconomic impacts. Here we introduce a feedback-informed epidemiological model that integrates human behavior with disease dynamics in a credible, tractable, and extendable manner. From economics, we incorporate a dynamic decision-making model where individuals assess the trade-off between disease risks and economic consequences, and then link this to a risk-stratified compartmental model of disease spread taken from epidemiology. In the unified framework, heterogeneous individuals make choices based on current and future payoffs, influencing their risk of infection and shaping population-level disease dynamics. As an example, we model disease-decision feedback during the early months of the COVID-19 pandemic, when the decision to participate in paid, in-person work was a major determinant of disease risk. Comparing the impacts of stylized policy options representing mandatory, incentivized/compensated, and voluntary work abstention, we find that accounting for disease-behavior feedback has a significant impact on the relative health and economic impacts of policies. Including two crucial dimensions of heterogeneity-health and economic vulnerability-the results highlight how inequities between risk groups can be exacerbated or alleviated by disease control measures. Importantly, we show that a policy of more stringent workplace testing can potentially slow virus spread and, surprisingly, increase labor supply since individuals otherwise inclined to remain at home to avoid infection perceive a safer workplace. In short, our framework permits the exploration of avenues whereby health and wealth need not always be at odds. This flexible and extendable modeling framework offers a powerful tool for understanding the interplay between human behavior and disease spread.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 10","pages":"e1013549"},"PeriodicalIF":3.6,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12510651/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145225489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}