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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)

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Spectral estimation for high-dimensional linear processes

arXiv

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arXiv 2025年

作者： Namdari, Jamshid Aue, Alexander Paul, Debashis Department of Biostatistics & Bioinformatics Emory University United States Department of Statistics University of California Davis United States Applied Statistics Unit Indian Statistical Institute Kolkata India

We propose a novel estimation procedure for certain spectral distributions associated with a class of high dimensional linear time series. The processes under consideration are of the form Xt = Í∞=0 AZt− with iid innovations (Zt). The key structural assumption is that the coefficient matrices and the variance of the innovations are simultaneously diagonalizable in a common orthonormal basis. We develop a strategy for estimating the joint spectral distribution of the coefficient matrices and the innovation variance by making use of the asymptotic behavior of the eigenvalues of appropriately weighted integrals of the sample periodogram. Throughout we work under the asymptotic regime p, n → ∞, such that p/n → c ∈ (0, ∞), where p is the dimension and n is the sample size. Under this setting, we first establish a weak limit for the empirical distribution of eigenvalues of the aforementioned integrated sample periodograms. This result is proved using techniques from random matrix theory, in particular the characterization of weak convergence by means of the Stieltjes transform of relevant distributions. We utilize this result to develop an estimator of the joint spectral distribution of the coefficient matrices, by minimizing an Lk discrepancy measure, for k ≥ 1, between the empirical and limiting Stieltjes transforms of the integrated sample periodograms. This is accomplished by assuming that the joint spectral distribution is a discrete mixture of point masses. We also prove consistency of the estimator corresponding to the L2 discrepancy measure. We illustrate the methodology through simulations and an application to stock price data from the S&P 500 series. © 2025, CC BY.

关键词： Asymptotic analysis

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Cross-cultural Adaptation and Validation of the Italian Version of the Revised Perceived Perioperative Competence Scale

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Journal of Perianesthesia Nursing 2025年

作者： Giammaria, Chiara Panattoni, Nicolò Terrenato, Irene Spano, Alessandro De Leo, Aurora Tomei, Bernardino Di Simone, Emanuele Petrone, Fabrizio IRCCS Istituti Fisioterapici Ospitalieri DITRAR Research Unit IFO Rome Italy IRCCS - Regina Elena National Cancer Institute Clinical Trial Center Biostatistics and Bioinformatics Unit Rome Italy Nursing Department ASL Roma 6 Rome Italy

Purpose: The nurse's role in the operating room requires essential competencies throughout the care process, such as theoretical knowledge, technical skills, and problem-solving abilities assessed by the Revised Perceived Perioperative Competence Scale (PPCS-R). The study aims to translate, cross-culturally adapt, and validate the Italian version of the scale. Design: A cross-cultural adaptation and validation of the Italian version of PPCS-R. Methods: The revised English version of the 40-item scale PPCS-R was translated into Italian according to Beaton's guidelines. The scale's internal consistency was assessed using Cronbach's α coefficient, ensuring the instrument's reliability. An exploratory factor analysis was conducted, preceded by the Kaiser-Meyer-Olkin test and Bartlett's sphericity test. Statistical analyses were performed using SPSS version 29.1 (IBM Corp.). Findings: The exploratory factor analysis of the Revised Perceived Perioperative Competence Scale—Italian Version (PPCS-R ITA) (N = 509) confirmed the 6 factors served in the original version. The final Italian version of PPCS-R comprises 40 items grouped into 6 latent factors. Each latent component showed a high consistency per Cronbach's α. The statistical analyses of the study confirm the reliability and good consistency of the tool. Conclusions: The statistical analyses of the study confirm the reliability and consistency of the tool (Revised Perceived Perioperative Competence Scale—Italian Version). By investigating the perception of Italian professionals working in the operating room through a translated and validated scale, specific training and professional development needs of a specific context can be better understood to identify appropriate learning and management strategies. © 2025 The American Society of PeriAnesthesia Nurses

关键词： competence cross-cultural adaptation nurse operating room validation

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Artificial Intelligence-based Decision Support Systems for Precision and Digital Health

arXiv

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arXiv 2024年

作者： Deliu, Nina Chakraborty, Bibhas MEMOTEF Department Sapienza University of Rome Italy MRC - Biostatistics Unit University of Cambridge United Kingdom Centre for Quantitative Medicine and the Program in Health Services and Systems Research Duke-NUS Medical School Singapore Department of Statistics and Data Science National University of Singapore Singapore Department of Biostatistics and Bioinformatics Duke University United States

Precision health, increasingly supported by digital technologies, is a domain of research that broadens the paradigm of precision medicine, advancing everyday healthcare. This vision goes hand in hand with the groundbreaking advent of artificial intelligence (AI), which is reshaping the way we diagnose, treat, and monitor both clinical subjects and the general population. AI tools powered by machine learning have shown considerable improvements in a variety of healthcare domains. In particular, reinforcement learning (RL) holds great promise for sequential and dynamic problems such as dynamic treatment regimes and just-in-time adaptive interventions in digital health. In this work, we discuss the opportunity offered by AI, more specifically RL, to current trends in healthcare, providing a methodological survey of RL methods in the context of precision and digital health. Focusing on the area of adaptive interventions, we expand the methodological survey with illustrative case studies that used RL in real practice. © 2024, CC BY-NC-SA.

关键词： Electronic health record

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Farming Features of African Bonytongue Fish Heterotis niloticus in Cameroon, Central Africa

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Open Journal of Animal Sciences 2023年第2期13卷 232-248页

作者： Jeanne Wikondi Ema Patrick Jolly Ngono Algrient Towa Nana Felix Meutchieye Minette Eyango Tabi Tomedi Department of Genetics and Biostatistics School of Veterinary Medicine and Sciences The University of Ngaoundere Ngaoundere Cameroon Applied Hydrobiology and Ichthyology Research Unit The University of Dschang Dschang Cameroon Department of Animal Science Biotechnology and Bioinformatics Research Unit The University of Dschang Dschang Cameroon Institute of Fisheries and Aquatic Sciences of the University of Douala Douala-Bassa Cameroon

The characterization of African bonytongue Heterotis niloticus fish farming was conducted in the Bi-modal agro-ecological zone of Cameroon from January to May 2021, Thirty-two fish farmers were interviewed and SPSS 6.2 Version was used to analyse the data. The results showed that the majority of fish farmers (81%) are men above 51 years, all of whom are Christians with more than fifty percent of them married. About 43% of them have university level education, and more than 6% of them considered the farming of Heterotis niloticus as their main income-earning activity. The main objective of Heterotis niloticus farming is sales (50%) and the uses (56%) of waged labour. A majority of the farmers produced reared African bonytongue in a polyculture system and 65% of them raised Heterotis niloticus in derivation ponds.

关键词： Aquaculture Characterization African Bonytongue Cameroon Central Africa

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Computational screening of potential drugs against COVID-19 disease: the Neuropilin-1 receptor as molecular target

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VirusDisease 2022年第1期33卷 23-31页

作者： Charoute, Hicham Elkarhat, Zouhair Elkhattabi, Lamiae El Fahime, Elmostafa Oukkache, Naoual Rouba, Hassan Barakat, Abdelhamid Research Unit of Epidemiology Biostatistics and Bioinformatics 1 Place Louis Pasteur Institut Pasteur du Maroc Casablanca 20360 Morocco Laboratory of Genomics and Human Genetics Institut Pasteur du Maroc Casablanca Morocco Molecular Biology and Functional Genomics Platform National Center for Scientific and Technical Research Rabat Morocco Laboratory of Venoms and Toxins Institut Pasteur du Maroc Casablanca Morocco

The transmembrane receptor Neuropilin-1 (NRP-1) was reported to serve as a host cell entry factor for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causal agent of COVID-19 disease. Therefore, molecular compounds interfering with SARS-CoV-2 binding to NRP-1 seem to be potential candidates as new antiviral drugs. In this study, NRP-1 receptor was targeted using a library of 1167 compounds previously analyzed in COVID-19 related studies. The results show the effectiveness of Nafamostat, Y96, Selinexor, Ebastine and UGS, in binding to NRP-1 receptor, with docking scores lower than − 8.2 kcal/mol. These molecules interact with NRP-1 receptor key residues, which makes them promising drugs to pursue further biological assays to explore their potential use in the treatment of COVID-19. © 2021, The Author(s), under exclusive licence to Indian Virological Society.

关键词： Antivirals COVID-19 Molecular docking Neuropilin-1 SARS-CoV-2 Virtual screening

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Integration between Genomic and Computational Statistical Surveys for the Screening of SNP Genetic Variants in Inflammatory Bowel Disease (IBD) Pediatric Patients*

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Computational Molecular Bioscience 2024年第3期14卷 146-191页

作者： Dago Dougba Noel Koffi N’Guessan Bénédicte Sonia Dagnogo Olefongo Daramcoum Wentoin Alimata Marie-Pierre Mauro Giacomelli Dagnogo Dramane Eboulé Ago Eliane Rebecca Yao Saraka Didier Martial Diarrassouba Nafan Giovanni Malerba Raffaele Badolato Department of Clinical and Experimental Sciences University of Brescia Brescia Italy Angelo Nocivelli Institute of Molecular Medicine Childrens Hospital ASST Spedali Civili Brescia Italy Unit of Biostatistics and Biomathematics & Unit of Bioinformatics Department of Molecular and Translational Medicine University of Brescia Brescia Italy Biological Sciences Training and Research Unit Department of Genetic and Biochemistry Peleforo Gon Coulibaly University Korhogo Cte dIvoire Department of Neurosciences Biomedicine and Movement Sciences Section of Biology and Genetics University of Verona Verona Italy Biosciences Training and Research Unit Biology and Health Laboratory Felix Houphouet-Boigny University Abidjan Cte dIvoire

Inflammatory bowel diseases (IBD) are complex multifactorial disorders that include Crohn’s disease (CD) and ulcerative colitis (UC). Considering that IBD is a genetic and multifactorial disease, we screened for the distribution dynamism of IBD pathogenic genetic variants (single nucleotide polymorphisms;SNPs) and risk factors in four (4) IBD pediatric patients, by integrating both clinical exome sequencing and computational statistical approaches, aiming to categorize IBD patients in CD and UC phenotype. To this end, we first aligned genomic read sequences of these IBD patients to hg19 human genome by using bowtie 2 package. Next, we performed genetic variant calling analysis in terms of single nucleotide polymorphism (SNP) for genes covered by at least 20 read genomic sequences. Finally, we checked for biological and genomic functions of genes exhibiting statistically significant genetic variant (SNPs) by introducing Fitcon genomic parameter. Findings showed Fitcon parameter as normalizing IBD patient’s population variability, as well as inducing a relative good clustering between IBD patients in terms of CD and UC phenotypes. Genomic analysis revealed a random distribution of risk factors and as well pathogenic SNPs genetic variants in the four IBD patient’s genome, claiming to be involved in: i) Metabolic disorders, ii) Autoimmune deficiencies;iii) Crohn’s disease pathways. Integration of genomic and computational statistical analysis supported a relative genetic variability regarding IBD patient population by processing IBD pathogenic SNP genetic variants as opposite to IBD risk factor variants. Interestingly, findings clearly allowed categorizing IBD patients in CD and UC phenotypes by applying Fitcon parameter in selecting IBD pathogenic genetic variants. Considering as a whole, the study suggested the efficiency of integrating clinical exome sequencing and computational statistical tools as a right approach in discriminating IBD phenotypes as well as improvi

关键词： Inflammatory Bowel Disease (IBD) Crohn Disease (CD) Ulcerative Colitis (UC) Clinical Exome Analysis Computational Statistic SNP Genetic Variants

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Reinforcement Learning in Modern biostatistics: Constructing Optimal Adaptive Interventions

arXiv

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arXiv 2022年

作者： Deliu, Nina Williams, Joseph Jay Chakraborty, Bibhas MEMOTEF Department Sapienza University of Rome Italy MRC - Biostatistics Unit University of Cambridge United Kingdom Department of Computer Science University of Toronto Canada Centre for Quantitative Medicine Duke-NUS Medical School Singapore Singapore Department of Biostatistics and Bioinformatics Duke University United States

In recent years, reinforcement learning (RL) has acquired a prominent position in health-related sequential decision-making problems, gaining traction as a valuable tool for delivering adaptive interventions (AIs). However, in part due to a poor synergy between the methodological and the applied communities, its real-life application is still limited and its potential is still to be realized. To address this gap, our work provides the first unified technical survey on RL methods, complemented with case studies, for constructing various types of AIs in healthcare. In particular, using the common methodological umbrella of RL, we bridge two seemingly different AI domains, dynamic treatment regimes and just-in-time adaptive interventions in mobile health, highlighting similarities and differences between them and discussing the implications of using RL. Open problems and considerations for future research directions are outlined. Finally, we leverage our experience in designing case studies in both areas to showcase the significant collaborative opportunities between statistical, RL, and healthcare researchers in advancing AIs. © 2022, CC BY-NC-ND.

关键词： Reinforcement learning

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An Rf-Cnn Pipeline for Predicting Pm2.5 Concentration in Sri Lanka

SSRN

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SSRN 2025年

作者： Attanayake, Gimhan Senarathna, Mahesh Bergin, Mike Carlson, David Bhave, Prakash V. Bowatte, Gayan Harischandra, Nalin Department of Basic Sciences Faculty of Allied Health Sciences University of Peradeniya Peradeniya20400 Sri Lanka Department of Civil and Environmental Engineering Duke University DurhamNC27708 United States Department of Biostatistics and Bioinformatics Duke University DurhamNC27708 United States Allergy and Lung Health Unit Centre for Epidemiology and Biostatistics School of Population and Global Health University of Melbourne MelbourneVIC3053 Australia Department of Electrical & Electronic Engineering Faculty of Engineering University of Peradeniya Peradeniya20400 Sri Lanka

Air pollution is a considerable global public health threat, requiring efficient monitoring and forecasting to guide decision-making. This study introduces a cascaded model of enhanced Random Forest with Convolutional Neural Network (RF-CNN) that predicts spatiotemporal fluctuations in PM2.5 concentrations throughout Sri Lanka. The K-Nearest Neighbors method is employed to impute missing data, and the model utilizes data from 24 low-cost PM2.5 sensors that are distributed throughout the country. The Convolutional Neural Network (CNN) derives spatial features from four-band PlanetScope satellite images (3m/pixel resolution, 1km2 spatial coverage), while the Random Forest (RF) component models the relationship between PM2.5 levels and four meteorological parameters. These features, combined with meteorological, spatial, and temporal inputs, produce the final forecasting results. The dataset comprises 1934 satellite images that were collected between December 2022 and February 2024, with an average PM2.5 concentration of approximately 15 μg/m3. The RF-CNN model exhibited robust performance metrics across a variety of climate zones, including a normalized root mean square error of approximately 32.4%, a mean absolute percentage error of approximately 25.7%, a normalized mean absolute error of approximately 22.8%, a Spearman r of 0.871, and a Pearson r of 0.873. Two metrics: Input Data Quality Score (IDQS) and Testing Data Quality Score (TDQS) were implemented to evaluate the effects of imputation. Performance was minimally impacted by imputation within acceptable ranges, while exceeding limits resulted in increased uncertainty. This research emphasizes the efficacy of the RF-CNN approach, which integrates satellite imagery and low-cost sensor data, as a scalable solution for predicting spatiotemporal PM2.5 variations. It provides valuable insights for regions that lack extensive monitoring. © 2025, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Investigating Fairness with FanFAIR: is Pre-processing Useful Only for Performances?

Investigating Fairness with FanFAIR: is Pre-processing Usefu...

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2025 IEEE Symposium on Computational Intelligence in Health and Medicine, CIHM 2025

作者： Rispoli, Michele Nobile, Marco S. Manzoni, Luca D’Onofrio, Alberto Confalonieri, Marco Salton, Francesco Confalonieri, Paola Ruaro, Barbara Gallese, Chiara Department of Mathematics Informatics and Geosciences University of Trieste Italy Department of Environmental Sciences Informatics and Statistics Ca’ Foscari University of Venice Italy Pulmonology Unit University Hospital of Cattinara Trieste Italy Department of Medical Surgical and Health Sciences University of Trieste Italy Department of Law University of Turin Italy Bicocca Bioinformatics Biostatistics and Bioimaging research center Monza Italy

ISBN: (纸本)9798331508333

Artificial Intelligence, and Machine Learning systems in general, are becoming pervasive in our society, from the industry to the public administration. AI can often provide a very efficient means to support decision-making, but it can represent a danger for high-risk applications such as bio-medicine and healthcare. In particular, biased datasets might lead to inaccurate or discriminatory ML systems, undermining the accuracy of their predictions and putting patients’ health at risk. FanFAIR is a python tool that provides the community with a semiautomatic tool for datasets’ fairness assessment. FanFAIR is designed to integrate qualitative considerations – such as ethics, human rights assessment, and data protection – with quantitative indicators of dataset’s fairness, such as balance, the presence of invalid entries, or outliers. In this work, we extend FanFAIR to deal with categorical data, and introduce a new algorithm for outlier detection in the presence of missing values. We then provide a case study on the data collected from COVID patients admitted to pneumology departments in Italy. We show how the successive steps of data cleaning and variable selection improve the indicators provided by FanFAIR. This shows that data cleaning procedures are not only necessary to improve the performance of the machine learning algorithm using the data for learning, but are also a way to improve (a measure of) fairness. Hence, the proposed case study provides an example in which performance and fairness are not in contrast, like it is commonly believed to be, but they improve together. © 2025 IEEE.

关键词： Health risks

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