Brain control of blood glucose levels

In many developed and mechanized countries, the readily access to highly palatable and caloric-dense food far exceeded the need for calories. This change has fostered the current pandemic of obesity and comorbid conditions of type 2 diabetes mellitus (T2DM), which are having negative impacts on public heath globally. Although it is evident that individuals develop central and peripheral insulin resistance upon exposure to an obesogenic environment, many questions are still unanswered. Owing to the sophisticated and technical nature of hyperinsulinemic-euglycemic clamp, laboratories around the world have adopted different procedural practices (eg. Restrained or anesthetized) to perform this canonical gold-standard technique. In particular, chapter 3 of this thesis has highlighted the impact of anesthesia and restrained stress on blood glucose levels in mice. By performing hyperinsulinemic-euglycemic clamp in conscious and free-moving mice, it allows careful analysis of systemic glucose homeostasis controlled by both the brain and peripheral organs in the most physiological setting possible. In addition, it provides important information that ultimately unveil new aspects of glucose regulation. <br>    Strong evidence in the literature have implicated central melanocortin pathways in the regulation of energy and glucose homeostasis. However, melanocortin pathways also exist in the periphery and the role of systemic melanocortins peptides is largely obscure. Essentially, chapter 4 of this thesis has identified a novel endocrine circuit of pituitary melanocortins, specifically -melanocyte stimulating hormone (-MSH) that regulates glucose uptake in skeletal muscle through the activation of a canonical melanocortin-5 receptor and protein kinase A (MC5r-PKA) pathway. <br>    Chapter 5 of this thesis explored the brain-centered glucoregulatory system in the context of obesity. Obesity is associated with reduced physiological responses to leptin and insulin, leading to the concept of obesity-associated hormonal resistance and elevated hepatic glucose production. The findings in chapter 5 have demonstrated that the reduction in insulin signaling in arcuate neurons of diet-induced obese mice is due to constitutive leptin activation of neurons, resulted from hyperleptinemia. Blocking leptin signaling in DIO mice consequently restores insulin signaling in the arcuate neurons. This effect is possibly mediated through the reduced inhibitory action of PTP1B on insulin receptor, thereby restoring the brain capacity to suppress hepatic glucose production in DIO mice. <br>    Noteworthy, obesity also causes ectopic lipid accumulation through hepatic de novo lipogenesis (DNL), which eventually leads to nonalcoholic fatty liver disease (NAFLD) and insulin resistance in peripheral tissues. Contradictory findings exist in the literature regarding the importance of carbohydrate response element-binding protein (ChREBP) expression in the liver and its association with insulin sensitivity. The findings in chapter 6 suggest that liver-specific ChREBP deletion results in hepatic insulin resistance in the absence or presence of excess lipid content in mice. Interestingly, blockade of transforming growth factor (TGF)-β/Smad3 signaling protects mice from obesity and diabetes. Given the functional diversity of Smad2 and Smad3, it is likely that common mediator smad (Co-Smad), Smad4, can function differently despite the fact that it participates in the same TGF-/Smad signaling pathway intracellularly. This necessitates the deletion of common mediator Smad, Smad4, to uncover the role of Smad4 in vivo. In chapter 7, a tamoxifen-inducible Smad4 conditional KO mouse model was generated in order to eliminate the possibility of embryonic compensation. Upon tamoxifen induction, Smad4 deletion enhances insulin sensitivity in lean mice by driving glucose uptake in brown adipose tissue (BAT). In addition, it also ameliorates insulin resistance in obese and insulin resistant mice, suggesting that Smad4 may be a potential target in the treatment of obesity and diabetes. <br>    Collectively, this thesis addresses the significance of both central and peripheral mechanisms in the regulation of glucose homeostasis. These findings have provided novel insights towards the understanding of systemic glucose regulation under normal and pathological conditions, which is vital for the development of therapeutic strategies to treat obesity and diabetes

Simulation of Low-field k-Space Data.

<p>High-field k-space data <i>y</i>_<i>h</i> and pure noise <i>n</i>_<i>h</i> are first acquired and served as input. <i>y</i>_<i>h</i> is then scaled by <i>a</i>² and <i>f</i> to account for signal magnitude change and different relaxation behaviors at different field strengths. <i>f</i> can be determined based on steady state signal equations for different types of sequences (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0154711#sec023" target="_blank">Appendix</a> for details). To simulate low-field data additional noise , as calculated in the text, is added to compensate for the different noise levels.</p

Minimum Field Strength Simulator for Proton Density Weighted MRI

<div><p>Objective</p><p>To develop and evaluate a framework for simulating low-field proton-density weighted MRI acquisitions based on high-field acquisitions, which could be used to predict the minimum B₀ field strength requirements for MRI techniques. This framework would be particularly useful in the evaluation of de-noising and constrained reconstruction techniques.</p><p>Materials and Methods</p><p>Given MRI raw data, lower field MRI acquisitions can be simulated based on the signal and noise scaling with field strength. Certain assumptions are imposed for the simulation and their validity is discussed. A validation experiment was performed using a standard resolution phantom imaged at 0.35 T, 1.5 T, 3 T, and 7 T. This framework was then applied to two sample proton-density weighted MRI applications that demonstrated estimation of minimum field strength requirements: real-time upper airway imaging and liver proton-density fat fraction measurement.</p><p>Results</p><p>The phantom experiment showed good agreement between simulated and measured images. The SNR difference between simulated and measured was ≤ 8% for the 1.5T, 3T, and 7T cases which utilized scanners with the same geometry and from the same vendor. The measured SNR at 0.35T was 1.8- to 2.5-fold less than predicted likely due to unaccounted differences in the RF receive chain. The predicted minimum field strength requirements for the two sample applications were 0.2 T and 0.3 T, respectively.</p><p>Conclusions</p><p>Under certain assumptions, low-field MRI acquisitions can be simulated from high-field MRI data. This enables prediction of the minimum field strength requirements for a broad range of MRI techniques.</p></div

Application to Abdominal Fat-Water Separated Imaging.

<p>a) Fat-water separated images reconstructed from data acquired at 3 T and simulated at low fields. Top row: water only; middle: fat only; bottom: proton-density fat fractions. b) The mean and standard deviation of fat fraction in the ROI at different field strengths. Fifty independent simulations were performed at each field strength.</p

Phantom Validations of Simulated SNR Change.

<p>The acquired 0.35T/1.5T/3T/7T images and simulated images from data acquired at 3T and 7T respectively are listed for comparison. Measured SNR values are shown below each image. For simulated images, the mean and standard deviation of SNR of twenty different simulations were used. Contrast was adjusted for better noise visualization.</p

Application to Upper Airway Compliance Measurement.

<p>a) Gridding reconstruction for data acquired at 3 T & simulated at low field strengths. Two temporal frames are shown: one with the airway partially collapsed (top row) and one with it open (second row). Notice the strong noise that makes the airways gradually unidentifiable as field strength goes down. b) The same frames using CG-SENSE with temporal finite difference sparsity constraint. c) Airways segmented from images using reconstructions in b) are used to calculate the average DICE coefficients over 100 temporal frames (3 breaths) at different field strengths. 3T images are served as references. Fifty independent simulations were performed at each field strength. Error bars correspond to 95% confidence intervals.</p

An approach to developing customized total knee replacement implants

Total knee replacement (TKR) has been performed for patients with end-stage knee joint arthritis to relieve pain and gain functions. Most knee replacement patients can gain satisfactory knee functions; however, the range of motion of the implanted knee is variable. There are many designs of TKR implants; it has been suggested by some researchers that customized implants could offer a better option for patients. Currently, the 3-dimensional knee model of a patient can be created from magnetic resonance imaging (MRI) or computed tomography (CT) data using image processing techniques. The knee models can be used for patient-specific implant design, biomechanical analysis, and creating bone cutting guide blocks. Researchers have developed patient-specific musculoskeletal lower limb model with total knee replacement, and the models can be used to predict muscle forces, joint forces on knee condyles, and wear of tibial polyethylene insert. These available techniques make it feasible to create customized implants for individual patients. Methods and a workflow of creating a customized total knee replacement implant for improving TKR kinematics and functions are discussed and presented in this paper

Subgroup analysis of HSPB1, SFRP1 and TNF based on sample source.

Subgroup analysis of HSPB1, SFRP1 and TNF based on sample source.</p

Subgroup analyses of MMP9, TNF and HSPB1 based on different races.

Subgroup analyses of MMP9, TNF and HSPB1 based on different races.</p

Subgroup analyses of TNF, SFRP1 and LOX based on measurement methods.

Subgroup analyses of TNF, SFRP1 and LOX based on measurement methods.</p