Publications by authors named "M B PATTERSON"

Background: Fermentation of dietary fiber by the gut microbiota leads to the production of metabolites called short-chain fatty acids, which lower blood pressure and exert cardioprotective effects. Short-chain fatty acids activate host signaling responses via the functionally redundant receptors GPR41 and GPR43, which are highly expressed by immune cells. Whether and how these receptors protect against hypertension or mediate the cardioprotective effects of dietary fiber remains unknown.

View Article and Find Full Text PDF

Unlabelled: Despite the prevalence and severity of enterococcal bacteremia (EcB), the mechanisms underlying systemic host responses to the disease remain unclear. Here, we present an extensive study that profiles molecular differences in plasma from EcB patients using an unbiased multi-omics approach. We performed shotgun proteomics and metabolomics on 105 plasma samples, including those from EcB patients and healthy volunteers.

View Article and Find Full Text PDF

Non-contact anterior cruciate ligament (ACL) rupture is a common serious orthopaedic disease in humans and dogs. Familial risk has been recognized in both species but interactions between genetic effects and environmental risk are not understood. We investigated ACL rupture heritability, genetic architecture, selection pressure, sharing of risk genes and biological pathways, and polygenic risk score (PRS) prediction of disease risk.

View Article and Find Full Text PDF

Purpose: In patients with relapsed or refractory (R/R) diffuse large B-cell lymphoma (DLBCL), brentuximab vedotin (BV) as monotherapy or combined with either lenalidomide (Len) or rituximab (R) has demonstrated efficacy with acceptable safety. We evaluated the efficacy and safety of BV + Len + R versus placebo + Len + R in patients with R/R DLBCL.

Methods: ECHELON-3 is a randomized, double-blind, placebo-controlled, multicenter, phase 3 trial comparing BV + Len + R with placebo + Len + R in patients with R/R DLBCL.

View Article and Find Full Text PDF

Wearable accelerometers are widely used as an ecologically valid and scalable solution for long-term at-home sleep monitoring in both clinical research and care. In this study, we applied a deep learning domain adversarial convolutional neural network (DACNN) model to this task and demonstrated that this new model outperformed existing sleep algorithms in classifying sleep-wake and estimating sleep outcomes based on wrist-worn accelerometry. This model generalized well to another dataset based on different wearable devices and activity counts, achieving an accuracy of 80.

View Article and Find Full Text PDF