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ABSTRACT Neural stem cell (NSC)‐based therapies offer a promising strategy to promote brain repair by delivering neurotrophic factors, supporting cell replacement, and stimulating endogenous neurogenesis following injury. While numerous studies have highlighted the protective and regenerative potential of NSCs and their extracellular vesicles (EVs), progress toward clinical translation remains hindered by limited molecular characterization of NSC lines and their EV cargo. To address this gap, we characterized two therapeutically relevant human fetal NSC lines, LMNSC01 and LMNSC02, both engineered to express the L‐MYC gene, along with their corresponding EVs. LMNSC01 cells primarily differentiated into neurones with limited glial populations, whereas LMNSC02 cells gave rise to all three major neural lineages: neural, glial and oligodendrocyte progenitor cells (OPCs). scRNA‐seq revealed distinct transcriptional profiles with minimal overlap between the two LMNSC lines. Using single extracellular vesicle nanoscopy, we observed that both lines released predominantly circular EVs, with LMNSC02‐EVs exhibiting higher levels of tetraspanins (CD9, CD63, and CD81) and a larger average diameter than LMNSC01‐EVs. Proteomic profiling revealed that LMNSC01‐EVs are enriched in proteins involved in cell adhesion, migration, junction formation, and neuronal projection development, while LMNSC02‐EVs are enriched in factors related to cytoplasmic translation initiation and biosynthesis. These LMNSC‐EVs (collected from undifferentiated LMNSCs) demonstrated neuroprotective effects in a brain organoid model of methotrexate‐induced toxicity when added to corresponding LMNSC01‐ or LMNSC02‐derived brain organoids. LMNSC01‐ and LMNSC02‐derived EVs restored neuronal and astrocytic populations but failed to rescue OPCs. These findings demonstrate the therapeutic potential of LMNSC‐derived EVs to counter chemotherapy‐induced neurotoxicity by preserving neurones and astrocytes, while highlighting the need for repeated or complementary interventions to restore oligodendrocyte populations.

Visceral adipose tissue plays a critical role in numerous diseases. Although imaging studies often show adipose involvement in abdominal diseases, their outcomes may vary from being a mild self-limited illness to one with systemic inflammation and organ failure. We therefore compared the pattern of visceral adipose injury during acute pancreatitis and acute diverticulitis to determine its role in organ failure. Acute pancreatitis-associated adipose tissue had ongoing lipolysis in the absence of adipocyte triglyceride lipase (ATGL). Pancreatic lipase injected into mouse visceral adipose tissue hydrolyzed adipose triglyceride and generated excess nonesterified fatty acids (NEFAs), which caused organ failure in the absence of acute pancreatitis. Pancreatic triglyceride lipase (PNLIP) increased in adipose tissue during pancreatitis and entered adipocytes by multiple mechanisms, hydrolyzing adipose triglyceride and generating excess NEFAs. During pancreatitis, obese PNLIP-knockout mice, unlike obese adipocyte-specific ATGL knockouts, had lower visceral adipose tissue lipolysis, milder inflammation, less severe organ failure, and improved survival. PNLIP-knockout mice, unlike ATGL knockouts, were protected from adipocyte-induced pancreatic acinar injury without affecting NEFA signaling or acute pancreatitis induction. Therefore, during pancreatitis, unlike diverticulitis, PNLIP leaking into visceral adipose tissue can cause excessive visceral adipose tissue lipolysis independently of adipocyte-autonomous ATGL, and thereby worsen organ failure.

A one-stop guide for public health students and practitioners learning the applications of classical regression models in epidemiology This book is written for public health professionals and students interested in applying regression models in the field of epidemiology. The academic material is usually covered in public health courses including (i) Applied Regression Analysis, (ii) Advanced Epidemiology, and (iii) Statistical Computing. The book is composed of 13 chapters, including an introduction chapter that covers basic concepts of statistics and probability. Among the topics covered are linear regression model, polynomial regression model, weighted least squares, methods for selecting the best regression equation, and generalized linear models and their applications to different epidemiological study designs. An example is provided in each chapter that applies the theoretical aspects presented in that chapter. In addition, exercises are included and the final chapter is devoted to the solutions of these academic exercises with answers in all of the major statistical software packages, including STATA, SAS, SPSS, and R. It is assumed that readers of this book have a basic course in biostatistics, epidemiology, and introductory calculus. The book will be of interest to anyone looking to understand the statistical fundamentals to support quantitative research in public health. In addition, this book: - Is based on the authors' course notes from 20 years teaching regression modeling in public health courses - Provides exercises at the end of each chapter - Contains a solutions chapter with answers in STATA, SAS, SPSS, and R - Provides real-world public health applications of the theoretical aspects contained in the chapters Applications of Regression Models in Epidemiology is a reference for graduate students in public health and public health practitioners. ERICK SUÁREZ is a Professor of the Department of Biostatistics and Epidemiology at the University of Puerto Rico School of Public Health. He received a Ph.D. degree in Medical Statistics from the London School of Hygiene and Tropical Medicine. He has 29 years of experience teaching biostatistics. CYNTHIA M. PÉREZ is a Professor of the Department of Biostatistics and Epidemiology at the University of Puerto Rico School of Public Health. She received an M.S. degree in Statistics and a Ph.D. degree in Epidemiology from Purdue University. She has 22 years of experience teaching epidemiology and biostatistics. ROBERTO RIVERA is an Associate Professor at the College of Business at the University of Puerto Rico at Mayaguez. He received a Ph.D. degree in Statistics from the University of California in Santa Barbara. He has more than five years of experience teaching statistics courses at the undergraduate and graduate levels. MELISSA N. MARTÍNEZ is an Account Supervisor at Havas Media International. She holds an MPH in Biostatistics from the University of Puerto Rico and an MSBA from the National University in San Diego, California. For the past seven years, she has been performing analyses for the biomedical research and media advertising fields.

This chapter discusses the use of weighted least squares (WLS) as a strategy to correct the lack of homoscedasticity in the errors. It presents two methods to achieve homogeneity of variance: the basic assumption for estimating the parameters of a multiple linear regression model using the ordinary least squares (OLS) method. Situations that can cause deviation from variance homogeneity include different number of observations for each value of X, and increase in the variance of Y as X increases. Homogeneity of variance is an assumption required to justify the use of t‐ and F‐tests when performing inference on the coefficients of a linear regression model. In designed experiments with large numbers of replicates, weights can be estimated directly from sample variances of the response variable for each combination of the predictor variables.

This chapter applies several criteria for choosing predictor variables in a multiple linear regression model. There are several criteria that can be used to select the number of independent variables in a regression model. These criteria are based on the principle of parsimony, using a minimum of independent variables and attaining a minimum sum of squared errors (SSE). It is important to keep in mind that in epidemiologic studies, the predictors can be related to the exposure, the confounding variables, and the effect modification variables. The chapter presents stepwise methods for selecting the best model and emphasize the limitations of these methods. Stepwise regression sequentially selects the best independent variables according to certain pre‐established statistical criteria. Moreover, the results obtained by applying different criteria during stepwise regression can produce different models. There are three stepwise methods or algorithms that are commonly used: forward selection (FS), backward elimination (BE), and stepwise selection (SS).

This chapter focuses on the following representations of a generalized linear models (GLM): a classical regression model, a logistic regression model, and a Poisson regression model. It describes the application of GLM in different epidemiological designs. The objective of a GLM is to determine the relationship between η and a set of predictors. The effect of each predictor on η depends on the estimated value of the corresponding β coefficient. The chapter identifies the components of a GLM: random component; systematic component; and link function. Moreover, the link function that is used in a GLM depends on the probability distribution of the dependent variable. When the link function is the identity function, the GLM identifies a linear regression model. If the link function is the natural logarithm, the GLM identifies a Poisson regression model. If the link function is the logit, the GLM identifies a logistic regression model.

Cohort studies are classified as prospective or historical, depending on the time when information about the exposure is collected. This chapter describes the different measures of incidence in a cohort study. Incidence is an epidemiological measure that quantifies the number of new cases of a disease that develop in a population at risk during a specific time period. There are two main measures that are used to quantify incidence: incidence density and cumulative incidence (CI). When the period of observation of each subject in the cohort study is constant, the occurrence of a disease or health‐related event is calculated using the cumulative incidence. The chapter defines the concept of crude and adjusted relative risk (RR) using a Poisson regression model. The procedure to assess the magnitude of the association of interest in a Poisson regression model involves a careful assessment of confounding variables and effect modification.

This chapter presents an extension of linear regression models to obtain predictions using more than one independent or predictor variable. These models are known as multiple linear regression models (MLRM). Matrix notation used to facilitate the presentation of the estimation procedures and the evaluation of the parameters for these models. The chapter introduces the concept of MLRM using matrix theory and polynomial regression models, centering of independent variables, and multicollinearity. It evaluates statistical hypothesis regarding the parameters of a MLRM through analysis of variance (ANOVA). The chapter applies a MLRM to study a public health problem. An important application of a regression model is to use the coefficients as measures to identify differences in the expected value of a continuous random variable between two or more groups of study. That is, a regression model when the predictor is a categorical variable.

This chapter provides the solutions of practical exercises of concepts, linear regression model, polynomial regression model, weighted linear regression, methods for selecting the best regression equation, logistic regression model, and Poisson regression model discussed in this book, with answers in all of the major statistical software packages including STATA, SAS, SPSS, and R. The chapter presents the data that represent the number of parasites (number) found in 1 ml of blood and the age of each infant under study. It uses a scatterplot to display the association between the natural logarithm of the number of parasites (response variable) and age (predictor). The chapter also presents data of concentrations of chlorophyll, phosphorus, and nitrogen taken from several lakes at different times of the day. It predicts the weight in premature children by gestational age.

This chapter describes the case of simple linear regression model (SLRM), which allows people to establish a linear relationship between two quantitative variables. In the simple regression model, one of the variables is identified as the response or dependent variable (X), while the second is called the predictor, explanatory, or independent variable (Y). The first step in establishing a possible linear relationship between two variables is through constructing a scatterplot. The trend line of the association between X and Y through the linear regression model can be displayed as a linear equation, in the simplest relationship. The Pearson correlation coefficient is an index indicating the degree of linear association between two continuous random variables. The chapter aims to interpret the results of a SLRM generated by the statistical software STATA and apply a simple linear regression model to study a problem in public health.