Search NASA⌕ Search

SEARCH · Search NASA

Results for “diabetes”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Longitudinal proteomics analysis in the immediate microenvironment of islet allografts during progression of rejection

The applicability and benefits of pancreatic islet transplantation are limited due to various issues including the need to avoid immune-mediated rejection. In the present study, we used our experimental platform of allogeneic islet transplant in the anterior chamber of the eye (ACE-platform) to longitudinally monitor the progress of rejection in mice and obtain aqueous humor samples representative of the microenvironment of the graft for accurately-timed proteomic analyses. LC-MS/MS-based proteomics performed on such mass-limited samples (~5 μL) identified a total of 1296 proteins. Various analyses revealed distinct protein patterns associated with the mounting of the inflammatory and immune responses and their evolution with the progression of the rejection. Pathway analyses indicated predominant changes in cytotoxic functions, cell movement, and innate and adaptive immune responses. Furthermore, network prediction analyses revealed transition from humoral to cellular immune response and exacerbation of pro-inflammatory signaling. One of the proteins identified by this localized proteomics as a candidate biomarker of islet rejection, Cystatin 3, was further validated by ELISA in the aqueous humor. This study provides (1) experimental evidence demonstrating the feasibility of longitudinal localized proteomics using small aqueous humor samples and (2) proof-of-concept for the discovery of biomarkers of impending immune attack from the immediate local microenvironment of ACE-transplanted islets.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Hepatic SerpinA1 improves energy and glucose metabolism through regulation of preadipocyte proliferation and UCP1 expression

Lipodystrophy and obesity are associated with insulin resistance and metabolic syndrome accompanied by fat tissue dysregulation. Here, we show that serine protease inhibitor A1 (SerpinA1) expression in the liver is increased during recovery from lipodystrophy caused by the adipocyte-specific loss of insulin signaling in mice. SerpinA1 induces the proliferation of white and brown preadipocytes and increases the expression of uncoupling protein 1 (UCP1) to promote mitochondrial activation in mature white and brown adipocytes. Liver-specific SerpinA1 transgenic mice exhibit increased browning of adipose tissues, leading to increased energy expenditure, reduced adiposity and improved glucose tolerance. Conversely, SerpinA1 knockout mice exhibit decreased adipocyte mitochondrial function, impaired thermogenesis, obesity, and systemic insulin resistance. SerpinA1 forms a complex with the Eph receptor B2 and regulates its downstream signaling in adipocytes. These results demonstrate that SerpinA1 is an important hepatokine that improves obesity, energy expenditure and glucose metabolism by promoting preadipocyte proliferation and activating mitochondrial UCP1 expression in adipocytes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

GronOR: Massively Parallel and GPU-Accelerated Non-Orthogonal Configuration Interaction for Large Molecular Systems

GronOR is a program package for non-orthogonal configuration interaction calculations for an electronic wave function built in terms of anti-symmetrized products of multi-configuration molecular fragment wave functions. The two-electron integrals that have to be processed may be expressed in terms of atomic orbitals or in terms of an orbital basis determined from the molecular orbitals of the fragments. The code has been specifically designed for execution on distributed memory massively parallel and Graphics Processing Unit (GPU)-accelerated computer architectures, using an MPI+OpenACC/OpenMP programming approach. The task-based execution model used in the implementation allows for linear scaling with the number of nodes on the largest pre-exascale architectures available, provides hardware fault resiliency, and enables effective execution on systems with distinct central processing unit-only and GPU-accelerated partitions. The code interfaces with existing multi-configuration electronic structure codes that provide optimized molecular fragment orbitals, configuration interaction coefficients, and the required integrals. Algorithm and implementation details, parallel and accelerated performance benchmarks, and an analysis of the sensitivity of the accuracy of results and computational performance to thresholds used in the calculations are presented.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Explainable Artificial Intelligence in Endocrinological Medical Research

Artificial Intelligence (AI) has been a part of the medical community for decades in the form of Clinical Decision Support Systems (CDSS) to aide physicians in diagnosis and categorization of patients (1). Recent years have seen a shift from expert-derived models to the integration of machine learning (ML) algorithms to drive the output of these AI systems due to the ability of ML models to more accurately make predictions by exploiting higher dimensional and often complex data. In many cases ML models gain their advantage in accuracy by capture complex and often non-linear relationships between features being used to make the prediction. However, the hype and excitement around these methods are tempered by the clinical utility of often black-box solutions driven by skepticism of results that are difficult for practitioners to not only interpret but explain to their patients (1-3). This skepticism is not unfounded as multiple examples of black-box solutions identifying incidental correlates as the key predictors have highlighted the potential bias in a training set, or reward system, that a ML model may exploit; for example a model discerning wolves from huskies based on snow in the background rather than features of the dogs (1, 4, 5).

Endocrinological, artificial intelligence, diabete↗