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Unraveling the persistent renal impact of intrauterine growth restriction and catch-up growth: integrating morphological insights with metabolomic profiling
The study aimed to investigate the long-term effects of IUGR and consequent catch-up growth on metabolic health by using a comprehensive approach that included histopathological, immunohistochemical, biochemical, and metabolomics analyses. Sprague–Dawley pregnant rats either undergo bilateral uterine artery ligation or a sham surgery on the 19th day of gestation. The offspring reached catch-up growth, kidney samples were collected at postnatal weeks 2, 4, and 8 for analysis. IUGR rats exhibited a spectrum of changes including reduced glomeruli number, proliferating cell number, altered oxidative stress markers, various enzymes involved in Krebs cycle, mitochondrial dynamics, and energy metabolism. Examination of the 8-week-old cohort identified a broader spectrum of metabolic alterations, notably in the biosynthesis of phenylalanine, tyrosine, and tryptophan, phenylalanine, tyrosine, glyoxylate, dicarboxylate, pyruvate, alanine, aspartate, and glutamate metabolism, glycolysis/gluconeogenesis and citrate (TCA) cycle. Our metabolomics analysis provides insights into the potential disease susceptibility of individuals born with IUGR, including obesity, diabetes, hypertriglyceridemia, cardiovascular diseases, and mental retardation. These findings underscore the intricate interplay between intrauterine conditions and long-term metabolic health outcomes, highlighting the need for further investigation into preventive and therapeutic strategies to mitigate the risk of metabolic diseases in individuals with a history of IUGR.
Enhancing imputation accuracy for catch-all missing data mechanisms with DFBETAS and leverage
This paper addresses the challenge of missing data in scientific research. It specifically examines the case of missing data arising from a “catch-all” missing not at ran (MNAR) mechanism, where missing values are disproportionately from one category, such as income or ethnicity in surveys. The study introduces the use of the regression diagnostic DFBETAS along with Leverage to improve the imputation of categorical data under such conditions. DFBETAS, a measure of influence in regression, is adapted to capture the intrinsic information of missing values, thereby enhancing the imputation process within a Bayesian multiple imputation (MI) framework. We validate the proposed approach through Monte Carlo simulations with data generating mechanisms based on probability distributions. The results show that incorporating DFBETAS and Leverage significantly improves the accuracy of imputations, optimizes the balance between its sensitivity and specificity reduces bias, and enhances confidence interval coverage of imputed estimates, especially as the strength of the catch-all mechanism increases. The study demonstrates that MI with DFBETAS and Leverage outperforms standard MI methods, offering a robust solution for handling categorical data with catch-all MNAR mechanisms. This advancement in imputation methodology provides a more accurate and efficient means of dealing with missing data in various research fields.