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The emerging role of mesenchymal stem cell-derived extracellular vesicles to ameliorate hippocampal NLRP3 inflammation induced by binge-like ethanol treatment in adolescence

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Neural Regeneration Research 2025年第4期20卷 1153-1163页

作者： Susana Mellado María JoséMorillo-Bargues Carla Perpiñá-Clérigues Francisco García-García Victoria Moreno-Manzano Consuelo Guerri María Pascual Department of Physiology School of Medicine and DentistryUniversity of ValenciaValenciaSpain Bioinformatics and Biostatistics Unit Príncipe Felipe Research CenterValenciaSpain Neuronal and Tissue Regeneration Laboratory Príncipe Felipe Research CenterValenciaSpain

Our previous studies have reported that activation of the NLRP3(NOD-,LRR-and pyrin domain-containing protein 3)-inflammasome complex in ethanol-treated astrocytes and chronic alcohol-fed mice could be associated with neuroinflammation and brain *** stem cell-derived extracellular vesicles(MSC-EVs)have been shown to restore the neuroinflammatory response,along with myelin and synaptic structural alterations in the prefrontal cortex,and alleviate cognitive and memory dysfunctions induced by binge-like ethanol treatment in adolescent *** the therapeutic role of the molecules contained in mesenchymal stem cell-derived extracellular vesicles,the present study analyzed whether the administration of mesenchymal stem cell-derived extracellular vesicles isolated from adipose tissue,which inhibited the activation of the NLRP3 inflammasome,was capable of reducing hippocampal neuroinflammation in adolescent mice treated with binge *** demonstrated that the administration of mesenchymal stem cell-derived extracellular vesicles ameliorated the activation of the hippocampal NLRP3 inflammasome complex and other NLRs inflammasomes(e.g.,pyrin domain-containing 1,caspase recruitment domain-containing 4,and absent in melanoma 2,as well as the alterations in inflammatory genes(interleukin-1β,interleukin-18,inducible nitric oxide synthase,nuclear factor-kappa B,monocyte chemoattractant protein-1,and C–X3–C motif chemokine ligand 1)and miRNAs(miR-21a-5p,miR-146a-5p,and miR-141-5p)induced by binge-like ethanol treatment in adolescent *** analysis further revealed the involvement of miR-21a-5p and miR-146a-5p with inflammatory target genes and NOD-like receptor signaling *** together,these findings provide novel evidence of the therapeutic potential of MSC-derived EVs to ameliorate the hippocampal neuroinflammatory response associated with NLRP3 inflammasome activation induced by binge drinking in adolescence.

关键词： adolescence binge-like ethanol treatment extracellular vesicles hippocampus mesenchymal stem cells neuroinflammation NOD-,LRR-and pyrin domain-containing protein 3(NLRP3)

Fast Algorithm for Calculating Probability of Chess Winning Streaks

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arXiv 2025年

作者： Diao, Guoqing Department of Biostatistics and Bioinformatics George Washington University United States

Motivated by the controversy in the chess community, where Hikaru Nakamura, a renowned grandmaster, has posted multiple impressive winning streaks over the years on the online platform ***, we derive the probabilities of various types of streaks in online chess and/or other sports. Specifically, given the winning/drawing/losing probabilities of individual games, we derive the probabilities of"pure" winning streaks, non-losing streaks, and"in-between" streaks involving at most one draw over the course of games played in a period. The performance of the developed algorithms is examined through numerical studies. © 2025, CC BY-NC-ND.

关键词： Monte Carlo methods

Screening and function analysis of key genes and signaling pathways in Alzheimer’s disease via next generation sequencing data analysis and bioinformatics approaches

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Genome Instability & Disease 2025年第1期6卷 42-59页

作者： Vastrad, Basavaraj Vastrad, Chanabasayya Department of Pharmaceutical Chemistry KLE College of Pharmacy Gadag India Biostatistics and Bioinformatics Dharwad India

Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by extracellular senile plaques. The pathogenesis of AD remains unclear. This study aims to explore the molecular mechanism of AD through bioinformatics and next generation sequencing (NGS) data analysis. NGS dataset GSE125583 was downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified using the “limma” R bioconductor package. Gene Ontology (GO) and REACTOME enrichment analysis were conducted. Analysis and visualization of protein–protein interaction network (PPI) and modules were carried out with HIPPIE and Cytoscape. Subsequently, miRNA-hub gene regulatory network and TF-hub gene regulatory network were built to predict the underlying microRNAs (miRNAs) and transcription factors (TFs) associated with hub genes. Finally, receiver operating characteristic (ROC) curve to assess the diagnostic efficacy of hub genes, which have been validated with training and validation datasets. Totally 956 DEGs (479 up regulated DEGs and 477 down regulated DEGs) were identified from GSE125583. GO and REACTOME pathway enrichment analysis indicated that DEGS were mainly enriched in regulation of biological quality, cell junction, channel activity, cell communication, plasma membrane, signaling receptor activity, neuronal system and GPCR ligand binding. 10 hub genes were selected, including PAK1, ELAVL2, NSF, HTR2C, TERT, UBD, MKI67, HSPB1, PYHIN1 and TES. MiRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed and analyzed successfully. Hsa-mir-4517, hsa-mir-3652, EGR1 and ZNF354C were predicted as possible key miRNAs and TFs for progression of AD. In conclusion, the results in this study provided reliable key genes and signaling pathways for AD, which will be useful for AD molecular mechanisms, diagnosis, prognosis and candidate targeted treatment.

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FUNCTIONAL CLUSTERING FOR LONGITUDINAL ASSOCIATIONS BETWEEN SOCIAL DETERMINANTS OF HEALTH AND STROKE MORTALITY IN THE U.S.

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Annals of Applied Statistics 2025年第1期19卷 798-820页

作者： Luo, Fangzhi Tan, Jianbin Zhang, Donglan Huang, Hui Shen, Ye Department of Epidemiology and Biostatistics College of Public Health University of Georgia United States Department of Biostatistics and Bioinformatics School of Medicine Duke University United States Department of Foundations of Medicine NYU Grossman Long Island School of Medicine United States Center for Applied Statistics and School of Statistics Renmin University of China China

Understanding the longitudinally changing associations between Social Determinants of Health (SDOH) and stroke mortality is essential for effective stroke management. Previous studies have uncovered significant regional disparities in the associations between SDOH and stroke mortality. However, existing studies have not utilized longitudinal associations to develop datadriven methods for regional division in stroke control. To fill this gap, we propose a novel clustering method to analyze SDOH-stroke mortality associations across U.S. counties. To enhance the interpretability of the clustering outcomes, we introduce a novel regularized expectation-maximization algorithm equipped with sparsity-and smoothness-pursued penalties, aiming at simultaneous clustering and variable selection in longitudinal associations. As a result, we can identify key SDOH that contributes to longitudinal changes in stroke mortality. This facilitates the clustering of U.S. counties based on the associations between these SDOH and stroke mortality. The effectiveness of our proposed method is demonstrated through extensive numerical studies. By applying our method to longitudinal data on SDOH and stroke mortality at the county level, we identify 18 important SDOH for stroke mortality and divide the U.S. counties into two clusters based on these selected SDOH. Our findings unveil complex regional heterogeneity in the longitudinal associations between SDOH and stroke mortality, providing valuable insights into region-specific SDOH adjustments for mitigating stroke mortality. © Institute of Mathematical Statistics, 2025.

关键词： functional regression model Model-based clustering regularized expectation-maximization algorithm stroke mortality variable selection

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In Reply to Li

International Journal of Radiation Oncology Biology Physics 2025年第2期122卷 513-514页

作者： Eschrich, Steven A. Torres-Roca, Javier F. Department of Bioinformatics Biostatistics and Radiation Oncology Moffitt Cancer Center TampaFlorida United States

A non-parametric U-statistic testing approach for multi-arm clinical trials with multivariate longitudinal data

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Journal of Multivariate Analysis 2025年 209卷 105447页

作者： Ghosh, Dhrubajyoti Luo, Sheng Department of Biostatistics & Bioinformatics Duke University United States

Randomized clinical trials (RCTs) often involve multiple longitudinal primary outcomes to comprehensively assess treatment efficacy. The Longitudinal Rank-Sum Test (LRST) Xu et al. (2025), a robust U-statistics-based, non-parametric, rank-based method, effectively controls Type I error and enhances statistical power by leveraging the temporal structure of the data without relying on distributional assumptions. However, the LRST is limited to two-arm comparisons. To address the need for comparing multiple doses against a control group in many RCTs, we extend the LRST to a multi-arm setting. This novel multi-arm LRST provides a flexible and powerful approach for evaluating treatment efficacy across multiple arms and outcomes, with a strong capability for detecting the most effective dose in multi-arm trials. Extensive simulations demonstrate that this method maintains excellent Type I error control while providing greater power compared to the two-arm LRST with multiplicity adjustments. Application to the Bapineuzumab (Bapi) 301 trial further validates the multi-arm LRST's practical utility and robustness, confirming its efficacy in complex clinical trial analyses. © 2025 Elsevier Inc.

关键词： Longitudinal data Rank-Sum Test U-statistics

Power and sample size calculation for multivariate longitudinal trials using the longitudinal rank sum test

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arXiv 2025年

作者： Ghosh, Dhrubajyoti Xu, Xiaoming Luo, Sheng Department of Biostatistics and Bioinformatics Duke University DurhamNC27606 United States

Neurodegenerative diseases such as Alzheimer’s and Parkinson’s often exhibit complex, multivariate longitudinal outcomes that require advanced statistical methods to comprehensively evaluate treatment efficacy. The Longitudinal Rank Sum Test (LRST) offers a nonparametric framework to assess global treatment effects across multiple longitudinal endpoints without requiring multiplicity corrections. This study develops a robust methodology for power and sample size estimation specific to the LRST, integrating theoretical derivations, asymptotic properties, and practical estimation techniques. Validation through numerical simulations demonstrates the accuracy of the proposed methods, while real-world applications to clinical trials in Alzheimer’s and Parkinson’s disease highlight their practical significance. This framework facilitates the design of efficient, well-powered trials, advancing the evaluation of treatments for complex diseases with multivariate longitudinal outcomes. © 2025, CC BY.

关键词： Neurodegenerative diseases

Inverse sampling intensity weighting for preferential sampling adjustment

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arXiv 2025年

作者： Hsiao, Thomas W. Waller, Lance A. Department of Biostatistics and Bioinformatics Rollins School of Public Health Emory University United States

Traditional geostatistical methods assume independence between observation locations and the spatial process of interest. Violations of this independence assumption are referred to as preferential sampling (PS). Standard methods to address PS rely on estimating complex shared latent variable models and can be difficult to apply in practice. We study the use of inverse sampling intensity weighting (ISIW) for PS adjustment in model-based geostatistics. ISIW is a two-stage approach wherein we estimate the sampling intensity of the observation locations then define intensity-based weights within a weighted likelihood adjustment. Prediction follows by substituting the adjusted parameter estimates within a kriging framework. A primary contribution was to implement ISIW by means of the Vecchia approximation, which provides large computational gains and improvements in predictive accuracy. Interestingly, we found that accurate parameter estimation had little correlation with predictive performance, raising questions about the conditions and parameter choices driving optimal implementation of kriging-based predictors under PS. Our work highlights the potential of ISIW to adjust for PS in an intuitive, fast, and effective manner. © 2025, CC BY.

关键词： Inverse problems

The Hidden Toll of COVID-19 on Opioid Mortality in Georgia: A Bayesian Excess Opioid Mortality Analysis

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arXiv 2025年

作者： Peterkin, Cyen J. Waller, Lance A. Peterson, Emily N. Department of Biostatistics and Bioinformatics Emory Rollins School of Public Health AtlantaGA United States

COVID-19 has had a large scale negative impact on the health of opioid users exacerbating the health of an already vulnerable population. Critical information on the total impact of COVID-19 on opioid users is unknown due to a lack of comprehensive data on COVID-19 cases, inaccurate diagnostic coding, and lack of data coverage. To assess the impact of COVID-19 on small-area opioid mortality, we developed a Bayesian hierarchical excess opioid mortality modeling approach. We incorporate spatio-temporal autocorrelation structures to allow for sharing of information across small areas and time to reduce uncertainty in small area estimates. Excess mortality is defined as the difference between observed trends after a crisis and expected trends based on observed historical trends, which captures the total increase in observed mortality rates compared to what was expected prior to the crisis. We illustrate the application of our approach to assess excess opioid mortality risk estimates for 159 counties in GA. Using our proposed approach will help inform interventions in opioid-related public health responses, policies, and resource allocation. The application of this work also provides a general framework for improving the estimation and mapping of health indicators during crisis periods for the opioid user population. Copyright © 2025, The Authors. All rights reserved.

关键词： COVID-19