ArXiv最新文献

{"title":"Ordinal Characterization of Similarity Judgments.","authors":"Jonathan D Victor, Guillermo Aguilar, Suniyya A Waraich","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Characterizing judgments of similarity within a perceptual or semantic domain, and making inferences about the underlying structure of this domain from these judgments, has an increasingly important role in cognitive and systems neuroscience. We present a new framework for this purpose that makes limited assumptions about how perceptual distances are converted into similarity judgments. The approach starts from a dataset of empirical judgments of relative similarities: the fraction of times that a subject chooses one of two comparison stimuli to be more similar to a reference stimulus. These empirical judgments provide Bayesian estimates of underling choice probabilities. From these estimates, we derive indices that characterize the set of judgments in three ways: compatibility with a symmetric dis-similarity, compatibility with an ultrametric space, and compatibility with an additive tree. Each of the indices is derived from rank-order relationships among the choice probabilities that, as we show, are necessary and sufficient for local consistency with the three respective characteristics. We illustrate this approach with simulations and example psychophysical datasets of dis-similarity judgments in several visual domains and provide code that implements the analyses at https://github.com/jvlab/simrank.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10593068/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49695007","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":"Global Deep Forecasting with Patient-Specific Pharmacokinetics.","authors":"Willa Potosnak, Cristian Challu, Kin G Olivares, Keith A Dufendach, Artur Dubrawski","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Forecasting healthcare time series data is vital for early detection of adverse outcomes and patient monitoring. However, it can be challenging in practice due to variable medication administration and unique pharmacokinetic (PK) properties of each patient. To address these challenges, we propose a novel hybrid global-local architecture and a PK encoder that informs deep learning models of patient-specific treatment effects. We showcase the efficacy of our approach in achieving significant accuracy gains in a blood glucose forecasting task using both realistically simulated and real-world data. Our PK encoder surpasses baselines by up to 16.4% on simulated data and 4.9% on real-world data for individual patients during critical events of severely high and low glucose levels. Furthermore, our proposed hybrid global-local architecture outperforms patient-specific PK models by 15.8%, on average.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10635292/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"107593093","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":"An affordable, wearable, fiber-free pulsed-mode diffuse speckle contrast flowmetry (PM-DSCF) sensor for noninvasive measurements of deep cerebral blood flow.","authors":"Chaebeom Yeo, Xuhui Liu, Mehrana Mohtasebi, Faezeh Akbari, Faraneh Fathi, Guoqiang Yu","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Significance: </strong>Measuring cerebral blood flow (CBF) is crucial for diagnosing various cerebral diseases. An affordable, wearable, and fiber-free continuous-wave speckle contrast flowmetry (CW-DSCF) technique has been developed for continuous monitoring of CBF variations. However, its application in adult humans is limited by shallow tissue penetration.</p><p><strong>Aim: </strong>To develop an innovative pulse-mode DSCF (PM-DSCF) system for continuous monitoring of CBF variations in adult humans.</p><p><strong>Approach: </strong>The PM-DSCF utilizes an 808 nm laser diode and a small NanEye camera to capture diffuse laser speckle fluctuations caused by red blood cell movement in the brain (i.e., CBF). Operating in short-pulse mode (duty cycle < 5%), the system maximizes peak pulse light power for deeper tissue penetration, while ensuring that the average power density remains within ANSI safety standards for skin exposure. The PM-DSCF was evaluated on tissue-simulating phantoms and in adult humans.</p><p><strong>Results: </strong>The maximum effective source-detector distance increased from 15 mm (CW-DSCF) to 35 mm (PM-DSCF). The PM-DSCF successfully detected CBF variations in adult brains during head-up-tilting experiments, consistent with physiological expectations.</p><p><strong>Conclusions: </strong>Switching from CW mode to PM mode significantly increases the maximum tissue penetration depth from ~7.5 mm (CW-DSCF) to ~17.5 mm (PM-DSCF), enabling successful CBF measurements in adult humans.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11844631/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143484862","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":"Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data.","authors":"Luoting Zhuang, Stephen H Park, Steven J Skates, Ashley E Prosper, Denise R Aberle, William Hsu","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Cancer evolves continuously over time through a complex interplay of genetic, epigenetic, microenvironmental, and phenotypic changes. This dynamic behavior drives uncontrolled cell growth, metastasis, immune evasion, and therapy resistance, posing challenges for effective monitoring and treatment. However, today's data-driven research in oncology has primarily focused on cross-sectional analysis using data from a single modality, limiting the ability to fully characterize and interpret the disease's dynamic heterogeneity. Advances in multiscale data collection and computational methods now enable the discovery of longitudinal multimodal biomarkers for precision oncology. Longitudinal data reveal patterns of disease progression and treatment response that are not evident from single-timepoint data, enabling timely abnormality detection and dynamic treatment adaptation. Multimodal data integration offers complementary information from diverse sources for more precise risk assessment and targeting of cancer therapy. In this review, we survey methods of longitudinal and multimodal modeling, highlighting their synergy in providing multifaceted insights for personalized care tailored to the unique characteristics of a patient's cancer. We summarize the current challenges and future directions of longitudinal multimodal analysis in advancing precision oncology.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11844620/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143484813","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":"ADEPT: A Noninvasive Method for Determining Elastic Parameters of Valve Tissue.","authors":"Wensi Wu, Mitchell Daneker, Christian Herz, Hannah Dewey, Jeffrey A Weiss, Alison M Pouch, Lu Lu, Matthew A Jolley","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Computer simulation of \"virtual interventions\" may inform optimal valve repair for a given patient prior to intervention. However, the paucity of noninvasive methods to determine <i>in vivo</i> mechanical parameters of valves limits the accuracy of computer prediction and their clinical application. To address this, we propose ADEPT: <b>A</b> noninvasive method for <b>D</b>etermining <b>E</b>lastic <b>P</b>arameters of valve <b>T</b>issue. In this work, we demonstrated its application to the tricuspid valve of a child. We first tracked valve displacements from open to closed frames within a 3D echocardiogram time sequence using image registration. Physics-informed neural networks were subsequently applied to estimate the nonlinear mechanical properties from first principles and reference displacements. The simulated model using these patient-specific parameters closely aligned with the reference image segmentation, achieving a mean symmetric distance of less than 1 mm. Our approach doubled the accuracy of the simulated model compared to the generic parameters reported in the literature.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11844617/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143484808","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":"A state-space framework for causal detection of hippocampal ripple-replay events.","authors":"Sirui Zeng, Uri T Eden","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Hippocampal ripple-replay events are typically identified using a two-step process that at each time point uses past and future data to determine whether an event is occurring. This prevents researchers from identifying these events in real time for closed-loop experiments. It also prevents the identification of periods of nonlocal representation that are not accompanied by large changes in the spectral content of the local field potentials (LFPs). In this work, we present a new state-space model framework that is able to detect concurrent changes in the rhythmic structure of LFPs with nonlocal activity in place cells to identify ripple-replay events in a causal manner. The model combines latent factors related to neural oscillations, represented space, and switches between coding properties to explain simultaneously the spiking activity from multiple units and the rhythmic content of LFPs recorded from multiple sources. The model is temporally causal, meaning that estimates of the switching state can be made at each instant using only past information from the spike and LFP signals, or can be combined with future data to refine those estimates. We applied this model framework to simulated and real hippocampal data to demonstrate its performance in identifying ripple-replay events.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11844619/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143484777","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":"CausalGeD: Blending Causality and Diffusion for Spatial Gene Expression Generation.","authors":"Rabeya Tus Sadia, Md Atik Ahamed, Qiang Cheng","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>The integration of single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) data is crucial for understanding gene expression in spatial context. Existing methods for such integration have limited performance, with structural similarity often below 60%, We attribute this limitation to the failure to consider causal relationships between genes. We present CausalGeD, which combines diffusion and autoregressive processes to leverage these relationships. By generalizing the Causal Attention Transformer from image generation to gene expression data, our model captures regulatory mechanisms without predefined relationships. Across 10 tissue datasets, CausalGeD outperformed state-of-the-art baselines by 5- 32% in key metrics, including Pearson's correlation and structural similarity, advancing both technical and biological insights.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11844633/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143484872","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":"GitHub is an effective platform for collaborative and reproducible laboratory research.","authors":"Katharine Y Chen, Maria Toro-Moreno, Arvind Rasi Subramaniam","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Laboratory research is a complex, collaborative process that involves several stages, including hypothesis formulation, experimental design, data generation and analysis, and manuscript writing. Although reproducibility and data sharing are increasingly prioritized at the publication stage, integrating these principles at earlier stages of laboratory research has been hampered by the lack of broadly applicable solutions. Here, we propose that the workflow used in modern software development offers a robust framework for enhancing reproducibility and collaboration in laboratory research. In particular, we show that GitHub, a platform widely used for collaborative software projects, can be effectively adapted to organize and document all aspects of a research project's lifecycle in a molecular biology laboratory. We outline a three-step approach for incorporating the GitHub ecosystem into laboratory research workflows: 1. designing and organizing experiments using issues and project boards, 2. documenting experiments and data analyses with a version control system, and 3. ensuring reproducible software environments for data analyses and writing tasks with containerized packages. The versatility, scalability, and affordability of this approach make it suitable for various scenarios, ranging from small research groups to large, cross-institutional collaborations. Adopting this framework from a project's outset can increase the efficiency and fidelity of knowledge transfer within and across research laboratories. An example GitHub repository based on this approach is available at https://github.com/rasilab/github_demo and a template repository that can be copied is available at https://github.com/rasilab/github_template.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11844616/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143484883","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":"Efficient Spatial Estimation of Perceptual Thresholds for Retinal Implants via Gaussian Process Regression.","authors":"Roksana Sadeghi, Michael Beyeler","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Retinal prostheses restore vision by electrically stimulating surviving neurons, but calibrating perceptual thresholds-the minimum stimulus intensity required for perception-remains a time-intensive challenge, especially for high-electrode-count devices. Since neighboring electrodes exhibit spatial correlations, we propose a Gaussian Process Regression (GPR) framework to predict thresholds at unsampled locations while leveraging uncertainty estimates to guide adaptive sampling. Using perceptual threshold data from four Argus II users, we show that GPR with a Matérn kernel provides more accurate threshold predictions than a Radial Basis Function (RBF) kernel (<i>p</i> < .001, Wilcoxon signed-rank test). In addition, spatially optimized sampling yielded lower prediction error than uniform random sampling for Participants 1 and 3 (<i>p</i> < .05). While adaptive sampling dynamically selects electrodes based on model uncertainty, its accuracy gains over spatial sampling were not statistically significant (<i>p</i> > .05), though it approached significance for Participant 1 (<i>p</i> = .074). These findings establish GPR with spatial sampling as a scalable, efficient approach to retinal prosthesis calibration, minimizing patient burden while maintaining predictive accuracy. More broadly, this framework offers a generalizable solution for adaptive calibration in neuroprosthetic devices with spatially structured stimulation thresholds.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11844632/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143484846","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":"<i>KMT2B</i>-related disorders: expansion of the phenotypic spectrum and long-term efficacy of deep brain stimulation.","authors":"Laura Cif, Diane Demailly, Jean-Pierre Lin, Katy E Barwick, Mario Sa, Lucia Abela, Sony Malhotra, Wui K Chong, Dora Steel, Alba Sanchis-Juan, Adeline Ngoh, Natalie Trump, Esther Meyer, Xavier Vasques, Julia Rankin, Meredith W Allain, Carolyn D Applegate, Sanaz Attaripour Isfahani, Julien Baleine, Bettina Balint, Jennifer A Bassetti, Emma L Baple, Kailash P Bhatia, Catherine Blanchet, Lydie Burglen, Gilles Cambonie, Emilie Chan Seng, Sandra Chantot Bastaraud, Fabienne Cyprien, Christine Coubes, Vincent d'Hardemare, Asif Doja, Nathalie Dorison, Diane Doummar, Marisela E Dy-Hollins, Ellyn Farrelly, David R Fitzpatrick, Conor Fearon, Elizabeth L Fieg, Brent L Fogel, Eva B Forman, Rachel G Fox, William A Gahl, Serena Galosi, Victoria Gonzalez, Tracey D Graves, Allison Gregory, Mark Hallett, Harutomo Hasegawa, Susan J Hayflick, Ada Hamosh, Marie Hully, Sandra Jansen, Suh Young Jeong, Joel B Krier, Sidney Krystal, Kishore R Kumar, Chloé Laurencin, Hane Lee, Gaetan Lesca, Laurence Lion François, Timothy Lynch, Neil Mahant, Julian A Martinez-Agosto, Christophe Milesi, Kelly A Mills, Michel Mondain, Hugo Morales-Briceno, John R Ostergaard, Swasti Pal, Juan C Pallais, Frédérique Pavillard, Pierre-Francois Perrigault, Andrea K Petersen, Gustavo Polo, Gaetan Poulen, Tuula Rinne, Thomas Roujeau, Caleb Rogers, Agathe Roubertie, Michelle Sahagian, Elise Schaefer, Laila Selim, Richard Selway, Nutan Sharma, Rebecca Signer, Ariane G Soldatos, David A Stevenson, Fiona Stewart, Michel Tchan, Ishwar C Verma, Bert B A de Vries, Jenny L Wilson, Derek A Wong, Raghda Zaitoun, Dolly Zhen, Anna Znaczko, Russell C Dale, Claudio M de Gusmão, Jennifer Friedman, Victor S C Fung, Mary D King, Shekeeb S Mohammad, Luis Rohena, Jeff L Waugh, Camilo Toro, F Lucy Raymond, Maya Topf, Philippe Coubes, Kathleen M Gorman, Manju A Kurian","doi":"","DOIUrl":"","url":null,"abstract":"&lt;p&gt;&lt;p&gt;Heterozygous mutations in &lt;i&gt;KMT2B&lt;/i&gt; are associated with an early-onset, progressive and often complex dystonia (DYT28). Key characteristics of typical disease include focal motor features at disease presentation, evolving through a caudocranial pattern into generalized dystonia, with prominent oromandibular, laryngeal and cervical involvement. Although &lt;i&gt;KMT2B&lt;/i&gt;-related disease is emerging as one of the most common causes of early-onset genetic dystonia, much remains to be understood about the full spectrum of the disease. We describe a cohort of 53 patients with &lt;i&gt;KMT2B&lt;/i&gt; mutations, with detailed delineation of their clinical phenotype and molecular genetic features. We report new disease presentations, including atypical patterns of dystonia evolution and a subgroup of patients with a non-dystonic neurodevelopmental phenotype. In addition to the previously reported systemic features, our study has identified co-morbidities, including the risk of status dystonicus, intrauterine growth retardation, and endocrinopathies. Analysis of this study cohort (&lt;i&gt;n&lt;/i&gt; = 53) in tandem with published cases (&lt;i&gt;n&lt;/i&gt; = 80) revealed that patients with chromosomal deletions and protein truncating variants had a significantly higher burden of systemic disease (with earlier onset of dystonia) than those with missense variants. Eighteen individuals had detailed longitudinal data available after insertion of deep brain stimulation for medically refractory dystonia. Median age at deep brain stimulation was 11.5 years (range: 4.5-37.0 years). Follow-up after deep brain stimulation ranged from 0.25 to 22 years. Significant improvement of motor function and disability (as assessed by the Burke Fahn Marsden's Dystonia Rating Scales, BFMDRS-M and BFMDRS-D) was evident at ł months, 1 year and last follow-up (motor, &lt;i&gt;P&lt;/i&gt; = 0.001, &lt;i&gt;P&lt;/i&gt; = 0.004, and &lt;i&gt;P&lt;/i&gt; = 0.012; disability, &lt;i&gt;P&lt;/i&gt; = 0.009, &lt;i&gt;P&lt;/i&gt; = 0.002 and &lt;i&gt;P&lt;/i&gt; = 0.012). At 1 year post-deep brain stimulation, &gt;50% of subjects showed BFMDRS-M and BFMDRS-D improvements of &gt;30%. In the long-term deep brain stimulation cohort (deep brain stimulation inserted for &gt;5 years, &lt;i&gt;n&lt;/i&gt; = 8), improvement of &gt;30% was maintained in 5/8 and 3/8 subjects for the BFMDRS-M and BFMDRS-D, respectively. The greatest BFMDRS-M improvements were observed for trunk (53.2%) and cervical (50.5%) dystonia, with less clinical impact on laryngeal dystonia. Improvements in gait dystonia decreased from 20.9% at 1 year to 1ł.2% at last assessment; no patient maintained a fully independent gait. Reduction of BFMDRS-D was maintained for swallowing (52.9%). Five patients developed mild parkinsonism following deep brain stimulation. &lt;i&gt;KMT2B-&lt;/i&gt;related disease comprises an expanding continuum from infancy to adulthood, with early evidence of genotype-phenotype correlations. Except for laryngeal dysphonia, deep brain stimulation provides a significant improvement in quality of life and function with sustained clinical be","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11844621/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143484921","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}