5,818 research outputs found
Dapagliflozin stimulates glucagon secretion at high glucose: experiments and mathematical simulations of human A-cells.
Glucagon is one of the main regulators of blood glucose levels and dysfunctional stimulus secretion coupling in pancreatic A-cells is believed to be an important factor during development of diabetes. However, regulation of glucagon secretion is poorly understood. Recently it has been shown that Na(+)/glucose co-transporter (SGLT) inhibitors used for the treatment of diabetes increase glucagon levels in man. Here, we show experimentally that the SGLT2 inhibitor dapagliflozin increases glucagon secretion at high glucose levels both in human and mouse islets, but has little effect at low glucose concentrations. Because glucagon secretion is regulated by electrical activity we developed a mathematical model of A-cell electrical activity based on published data from human A-cells. With operating SGLT2, simulated glucose application leads to cell depolarization and inactivation of the voltage-gated ion channels carrying the action potential, and hence to reduce action potential height. According to our model, inhibition of SGLT2 reduces glucose-induced depolarization via electrical mechanisms. We suggest that blocking SGLTs partly relieves glucose suppression of glucagon secretion by allowing full-scale action potentials to develop. Based on our simulations we propose that SGLT2 is a glucose sensor and actively contributes to regulation of glucagon levels in humans which has clinical implications
Role of multiorbital effects in the magnetic phase diagram of iron-pnictides
We elucidate the pivotal role of the bandstructure's orbital content in
deciding the type of commensurate magnetic order stabilized within the
itinerant scenario of iron-pnictides. Recent experimental findings in the
tetragonal magnetic phase attest to the existence of the so-called charge and
spin ordered density wave over the spin-vortex crystal phase, the latter of
which tends to be favored in simplified band models of itinerant magnetism.
Here we show that employing a multiorbital itinerant Landau approach based on
realistic bandstructures can account for the experimentally observed magnetic
phase, and thus shed light on the importance of the orbital content in deciding
the magnetic order. In addition, we remark that the presence of a hole pocket
centered at the Brillouin zone's -point favors a magnetic stripe
rather than a tetragonal magnetic phase. For inferring the symmetry properties
of the different magnetic phases, we formulate our theory in terms of magnetic
order parameters transforming according to irreducible representations of the
ensuing D point group. The latter method not only provides
transparent understanding of the symmetry breaking schemes but also reveals
that the leading instabilities always belong to the subset
of irreducible representations, independent of their C or C nature.Comment: 11 pages, 6 figure
Predicting time to graduation at a large enrollment American university
The time it takes a student to graduate with a university degree is mitigated
by a variety of factors such as their background, the academic performance at
university, and their integration into the social communities of the university
they attend. Different universities have different populations, student
services, instruction styles, and degree programs, however, they all collect
institutional data. This study presents data for 160,933 students attending a
large American research university. The data includes performance, enrollment,
demographics, and preparation features. Discrete time hazard models for the
time-to-graduation are presented in the context of Tinto's Theory of Drop Out.
Additionally, a novel machine learning method: gradient boosted trees, is
applied and compared to the typical maximum likelihood method. We demonstrate
that enrollment factors (such as changing a major) lead to greater increases in
model predictive performance of when a student graduates than performance
factors (such as grades) or preparation (such as high school GPA).Comment: 28 pages, 11 figure
Predicting the dissolution kinetics of silicate glasses using machine learning
Predicting the dissolution rates of silicate glasses in aqueous conditions is
a complex task as the underlying mechanism(s) remain poorly understood and the
dissolution kinetics can depend on a large number of intrinsic and extrinsic
factors. Here, we assess the potential of data-driven models based on machine
learning to predict the dissolution rates of various aluminosilicate glasses
exposed to a wide range of solution pH values, from acidic to caustic
conditions. Four classes of machine learning methods are investigated, namely,
linear regression, support vector machine regression, random forest, and
artificial neural network. We observe that, although linear methods all fail to
describe the dissolution kinetics, the artificial neural network approach
offers excellent predictions, thanks to its inherent ability to handle
non-linear data. Overall, we suggest that a more extensive use of machine
learning approaches could significantly accelerate the design of novel glasses
with tailored properties
- …