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We propose a machine learning (ML) model offering a probabilistic prediction adaptable for both community and referral centers, where prevalence varies from 3.7 50%

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Immunoblot and quantification of indicated proteins in primary hepatocytes isolated from mice liver treated with BSA, 200 M palmitic acid (PA), oleic acid (OA), or linoleic acid (LA) for 4 h after overnight serum starvation, and immunoblot quantification of P-eIF4E/eIF4E in the AML12 cells and primary hepatocytes in response to BSA or different fatty acids (n = 3 biological independent samples)

In assumption 1, we extract SNPs that are strongly associated with antioxidant diets at genome-wide significance levels, setting thresholds ( p 10, we show that there is no weak instrumental variable bias