Project Euclid - mathematics and statistics online

Background: This meta-analysis evaluates the overall effect of the non-pharmacological intervention, aerobic exercise, upon serum liver enzymes levels, glucose metabolism and anthropometric measures amongst patients with metabolic associated fatty liver disease (MAFLD). It also examines whether the effects on these outcomes are moderated by the aerobic training protocol when considered according to the American College of Sports Medicine (ACSM) recommended FITT (frequency, intensity, time, type) principles. Approach and Results: Fifteen randomized control trials were included in the meta-analysis. Compared with usual care, continuous and interval training showed significant efficacy in alanine aminotransferase (ALT) level improvement (MD = −2.4, 95% CI: −4.34 to −0.46 p = 0.015, I² = 9.1%). Interventions based on all types of aerobic exercise protocols showed significant improvement of intrahepatic triglycerides (MD = −4.0557, 95% CI: −5.3711 to −2.7403, p < 0.0001, I² = 0%) and BMI (MD = −0.9774, 95% CI: −1.4086 to −0.5462, p < 0.0001, I² = 0). Meta-regression analysis demonstrated a significant correlation between total intervention time and ALT level (for all aerobic protocols: 6.0056, se = 2.6896, z = 2.2329, p = 0.02; as well as for continuous and interval aerobic protocols: 5.5069, se = 2.7315, z = 2.016, p = 0.04). Conclusions: All types of aerobic exercise protocols are effective at improving intrahepatic triglycerides and lead to a reduction in body mass index. In addition, continuous and interval aerobic exercise may be more effective at improving ALT ≤12 weeks intervention time benefits the management of MAFLD.

The standard Markov chain Monte Carlo method of estimating an expected value is to generate a Markov chain which converges to the target distribution and then compute correlated sample averages. In many applications the quantity of interestθis represented as a product of expected values,θ=µ₁⋯µ_k, and a natural estimator is a product of averages. To increase the confidence level, we can compute a median of independent runs. The goal of this paper is to analyze such an estimator, i.e. an estimator which is a ‘median of products of averages’ (MPA). Sufficient conditions are given forto have fixed relative precision at a given level of confidence, that is, to satisfy. Our main tool is a new bound on the mean-square error, valid also for nonreversible Markov chains on a finite state space.

Recent studies demonstrate that the peptides in the serum of cancer patients that are generated (ex vivo) as a result of tumor protease activity can be used for the detection and classification of cancer. In this paper, we propose the first formal approach to modeling exopeptidase activity from liquid chromatography–mass spectrometry (LC-MS) samples. We design a statistical model of peptidome degradation and a Metropolis-Hastings algorithm for Bayesian inference of model parameters. The model is successfully validated on a real LC-MS dataset. Our findings support the hypotheses about disease-specific exopeptidase activity, which can lead to new diagnostic approach in clinical proteomics.