Knockdown of Ant2 Reduces Adipocyte Hypoxia And Improves Insulin Resistance in Obesity.

Decreased adipose tissue oxygen tension and increased HIF-1α expression can trigger adipose tissue inflammation and dysfunction in obesity. Our current understanding of obesity-associated decreased adipose tissue oxygen tension is mainly focused on changes in oxygen supply and angiogenesis. Here, we demonstrate that increased adipocyte O2 demand, mediated by ANT2 activity, is the dominant cause of adipocyte hypoxia. Deletion of adipocyte Ant2 improves obesity-induced intracellular adipocyte hypoxia by decreasing obesity-induced adipocyte oxygen demand, without effects on mitochondrial number or mass, or oligomycin-sensitive respiration. This led to decreased adipose tissue HIF-1α expression and inflammation with improved glucose tolerance and insulin resistance in both a preventative or therapeutic setting. Our results suggest that ANT2 may be a target for the development of insulin sensitizing drugs and that ANT2 inhibition might have clinical utility

Renal DCE-MRI model selection using Bayesian probability theory

The goal of this work was to demonstrate the utility of Bayesian probability theory-based model selection for choosing the optimal mathematical model from among 4 competing models of renal dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data. DCE-MRI data were collected on 21 mice with high (n = 7), low (n = 7), or normal (n = 7) renal blood flow (RBF). Model parameters and posterior probabilities of 4 renal DCE-MRI models were estimated using Bayesian-based methods. Models investigated included (1) an empirical model that contained a monoexponential decay (washout) term and a constant offset, (2) an empirical model with a biexponential decay term (empirical/biexponential model), (3) the Patlak–Rutland model, and (4) the 2-compartment kidney model. Joint Bayesian model selection/parameter estimation demonstrated that the empirical/biexponential model was strongly favored for all 3 cohorts, the modeled DCE signals that characterized each of the 3 cohorts were distinctly different, and individual empirical/biexponential model parameter values clearly distinguished cohorts of low and high RBF from one another. The Bayesian methods can be readily extended to a variety of model analyses, making it a versatile and valuable tool for model selection and parameter estimation.</jats:p

Brain retraction and thickness of cerebral neocortex: an automated technique for detecting retraction-induced anatomic changes using magnetic resonance imaging.

BACKGROUND: Treating deep-seated cerebral lesions often requires retracting the brain. Retraction, however, causes clinically significant postoperative neurological deficits in 3% to 9% of intracranial cases. OBJECTIVE: This pilot study used automated analysis of postoperative magnetic resonance images (MRIs) to determine whether brain retraction caused local anatomic changes to the cerebral neocortex and whether such changes represented sensitive markers for detecting brain retraction injury. METHODS: Pre- and postoperative maps of whole-brain cortical thickness were generated from 3-dimensional MRIs of 6 patients who underwent selective amygdalohippocampectomy for temporal lobe epilepsy (5 left hemispheres, 1 right hemisphere). Mean cortical thickness was determined in the inferior temporal gyrus (ITG test), where a retractor was placed during surgery, and in 2 control gyri-the posterior portion of the inferior temporal gyrus (ITG control) and motor cortex control. Regions of cortical thinning were also compared with signs of retraction injury on early postoperative MRIs. RESULTS: Postoperative maps of cortical thickness showed thinning in the inferior temporal gyrus where the retractor was placed in 5 patients. Postoperatively, mean cortical thickness declined from 4.1 +/- 0.4 mm to 2.9 +/- 0.9 mm in ITG test (P = .03) and was unchanged in the control regions. Anatomically, the region of neocortical thinning correlated with postoperative edema on MRIs obtained within 48 hours of surgery. CONCLUSION: Postoperative MRIs can be successfully interrogated for information on cortical thickness. Brain retraction is associated with chronic local thinning of the neocortex. This automated technique may be sensitive enough to detect regions at risk for functional impairment during craniotomy that cannot be easily detected on postoperative structural imaging

Delayed visuospatial memory test and motor skill task.

A. Participants completed the Rey-Osterrieth Complex Figure Delayed Recall test (measures delayed visuospatial memory). An example drawing from one of the participants is shown. B. Participants used their nondominant hand to perform the motor task that mimicked the upper extremity movements required to feed oneself. This image is adapted from the “Dexterity and Reaching Motor Tasks” by MRL Laboratory that is licensed under CC BY 2.0.</p

Diffusion tenor model parameter maps.

Fractional anisotropy (top row), radial (second row), mean (third row), and axial (bottom row) diffusivity maps for an example participant that demonstrates the diffusion tensor model fit the data as expected. (TIF)</p

Whole-brain fractional anisotropy and radial diffusivity results.

Fractional anisotropy results are shown in Panel A; the first row illustrates the large positive cluster in the right SLF (orange), the second row illustrates the negative cluster in the left CST/ATR/SLF, and the third row illustrates the positive cluster in bilateral CST in the brainstem. Radial diffusivity results are shown in Panel B; the first row shows the negative cluster in the left SLF, the second row shows the positive cluster in the ATR/CST, and the third row illustrates the negative cluster in the bilateral CST in the brainstem. The last row shows the negative cluster in the right ATR.</p

Renal DCE-MRI Model Selection Using Bayesian Probability Theory

The goal of this work was to demonstrate the utility of Bayesian probability theory-based model selection for choosing the optimal mathematical model from among 4 competing models of renal dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data. DCE-MRI data were collected on 21 mice with high (n = 7), low (n = 7), or normal (n = 7) renal blood flow (RBF). Model parameters and posterior probabilities of 4 renal DCE-MRI models were estimated using Bayesian-based methods. Models investigated included (1) an empirical model that contained a monoexponential decay (washout) term and a constant offset, (2) an empirical model with a biexponential decay term (empirical/biexponential model), (3) the Patlak–Rutland model, and (4) the 2-compartment kidney model. Joint Bayesian model selection/parameter estimation demonstrated that the empirical/biexponential model was strongly favored for all 3 cohorts, the modeled DCE signals that characterized each of the 3 cohorts were distinctly different, and individual empirical/biexponential model parameter values clearly distinguished cohorts of low and high RBF from one another. The Bayesian methods can be readily extended to a variety of model analyses, making it a versatile and valuable tool for model selection and parameter estimation

Whole-brain fractional anisotropy and radial diffusivity results.

Whole-brain fractional anisotropy and radial diffusivity results.</p

Participant characteristics.

Participant characteristics.</p

Principal component values and one-week skill retention and delayed visuospatial memory performance.

Principal component values (y-axis) for each participant was highly correlated with their one-week skill retention and Delayed Recall scores (x-axes), demonstrating that it was indeed a good representation of the shared variance of both behaviors. (TIF)</p