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Trevino, V.

Publications and source records attributed to Trevino, V..

3 recordsLinked to original sources

Mitochondrial respiratory complexes, excitation-contraction coupling and mTOR signaling transcriptional programs are dysregulated in cardiometabolic HFpEF

Heart failure is a leading cause of death and disability worldwide. Nearly, half of the cases correspond to HF with preserved ejection fraction (HFpEF), which is predicted to become the most prevalent form of HF in the forthcoming years. In this study, we performed a bulk RNA-seq assay to study the transcriptomic profiles of the hearts of HFpEF mice. HFpEF animals consisted in C57BL/6 mic that were fed a high-fat diet and exposed to L-NAME in the drinking water during five or eight weeks. Controls received a standard chow diet. Differential expression analysis yielded 1535 differentially expressed genes in HFpEF hearts relative to controls. 71 out of 78 DEGs contained in the GO-CC "Mitochondrial Inner Membrane (GO:0005743)" were found to be down-regulated, mostly affecting respiratory complexes I, III, IV and V in a time-dependent manner. Overrepresentation analyses also suggested that the mTOR signaling cascade is switched on, while "Diabetic Cardiomyopathy", "Cardiac Muscle Contraction" and "Cytoskeleton in Muscle Cells" KEGG pathways were detected to be affected, probably pointing at excitation-contraction coupling impairment. In summary, the transcriptomic program of the HFpEF hearts consisted in a widespread downregulation of the expression of the subunits of mitochondrial respiratory complexes, upregulation of mTOR signaling and deregulation of the genes related to the excitation-contraction coupling.

physiology↗

Exploring Survival Models Associated with MCI to AD Conversion: A Machine Learning Approach

Several studies have documented that structural MRI findings are associated with the presence of early-stage Alzheimer Disease (AD). However, the association of each MRI feature with the rate of conversion from mild cognitive impairment (MCI) to AD in a multivariate setting has not been studied fully. The objective of this work is the comprehensive exploration of four different machine learning (ML) strategies to build MRI-based multivariate Cox regression models. These models evaluated the association of MRI features with the time of MCI to clinical AD conversion. We used 442 MCI subjects from the Alzheimers disease Neuroimaging Initiative (ADNI) study. Each subject was described by 346 MRI features and time to AD conversion. Cox regression models then estimated the rate of conversion. Models were built using four ML methodologies in a cross-validation (CV) setting. All the ML methods returned successful Cox models with different CV performances. The best model exhibited a concordance index of 0.84 (95% CI: 0.82-0.86). The final analysis described the hazard ratios (HR) of the top ten MRI features associated with MCI to AD conversion. Our results suggest ML exploration is a viable strategy for building and analyzing survival models that predict subjects at risk of AD conversion.

bioinformatics↗

Benchmarking machine learning models for the analysis of genetic data using FRESA.CAD Binary Classification Benchmarking

BackgroundMachine learning models have proven to be useful tools for the analysis of genetic data. However, with the availability of a wide variety of such methods, model selection has become increasingly difficult, both from the human and computational perspective.\n\nResultsWe present the R package FRESA.CAD Binary Classification Benchmarking that performs systematic comparisons between a collection of representative machine learning methods for solving binary classification problems on genetic datasets.\n\nConclusionsFRESA.CAD Binary Benchmarking demonstrates to be a useful tool over a variety of binary classification problems comprising the analysis of genetic data showing both quantitative and qualitative advantages over similar packages.

bioinformatics↗