Host-diet-gut microbiome interactions influence human energy balance: a randomized clinical trial.

The gut microbiome is emerging as a key modulator of human energy balance. Prior studies in humans lacked the environmental and dietary controls and precision required to quantitatively evaluate the contributions of the gut microbiome. Using a Microbiome Enhancer Diet (MBD) designed to deliver more dietary substrates to the colon and therefore modulate the gut microbiome, we quantified microbial and host contributions to human energy balance in a controlled feeding study with a randomized crossover design in young, healthy, weight stable males and females (NCT02939703).

Reprogramming the Human Gut Microbiome Reduces Dietary Energy Harvest.

The gut microbiome is emerging as a key modulator of host energy balance1. We conducted a quantitative bioenergetics study aimed at understanding microbial and host factors contributing to energy balance. We used a Microbiome Enhancer Diet (MBD) to reprogram the gut microbiome by delivering more dietary substrates to the colon and randomized healthy participants into a within-subject crossover study with a Western Diet (WD) as a comparator.

Developing a model for estimating the activity of colonic microbes after intestinal surgeries.

Background: The large intestine provides a compensatory role in energy recovery when surgical interventions such as extensive small intestinal resections or bypass operations lower the efficiency of nutrient absorption in the upper gastrointestinal (GI) tract. While microorganisms in the colon are known to play vital roles in recovering energy, their contributions remain to be qualified and quantified in the small intestine resection.

Objective: We develop a mathematical model that links nutrient absorption in the upper and lower GI tract in two steps.

Chemical Oxygen Demand Can Be Converted to Gross Energy for Food Items Using a Linear Regression Model.

Background: Human and microbial metabolism are distinct disciplines. Terminology, metrics, and methodologies have been developed separately. Therefore, combining the 2 fields to study energetic processes simultaneously is difficult.

Learning Implicit Brain MRI Manifolds with Deep Learning.

An important task in image processing and neuroimaging is to extract quantitative information from the acquired images in order to make observations about the presence of disease or markers of development in populations. Having a low-dimensional manifold of an image allows for easier statistical comparisons between groups and the synthesis of group representatives. Previous studies have sought to identify the best mapping of brain MRI to a low-dimensional manifold, but have been limited by assumptions of explicit similarity measures.

Impact of vessel wall lesions and vascular stenoses on cerebrovascular reactivity in patients with intracranial stenotic disease.

Purpose: To compare cerebrovascular reactivity (CVR) and CVR lagtimes in flow territories perfused by vessels with vs. without proximal arterial wall disease and/or stenosis, separately in patients with atherosclerotic and nonatherosclerotic (moyamoya) intracranial stenosis.

Materials And Methods: Atherosclerotic and moyamoya patients with >50% intracranial stenosis and <70% cervical stenosis underwent angiography, vessel wall imaging (VWI), and CVR-weighted imaging (n = 36; vessel segments evaluated = 396).