Serum Free Zinc as A Predictor for Excessive Function of Pancreatic Beta-Cells in Central-Obese Men

BACKGROUND: Central obesity is known as a risk factor for type 2 diabetes mellitus (T2DM). Its development is influenced by many factors such as a progressive failure of pancreatic beta cell function. The beta cells increase their function to secret insulin along T2DM development to compensate before it becomes exhausted. Zinc (Zn) plays a crucial role in beta cell function and insulin secretion. The majority of Zn in serum are bound to protein which is not readily available interact with cells. The free Zn in serum has been suggested as being more representative than total Zn in beta cell function. This research aimed to investigate the correlation between serum free Zn and homeostasis model assessment for beta cell function (HOMA-B) and to predict the pancreatic beta cell function in the development of T2DM.METHODS: This study was designed as an observational with a cross-sectional approach. The subjects were centrally obese men aged 30-50 years and who had met the inclusion and exclusion criteria from the screening tests. Control subjects were lean men without T2DM. Serum free Zn and serum total Zn were measured by using inductively coupled plasma-mass spectrometry (ICP-MS).RESULTS: There was positive correlation between serum free Zn and HOMA-B (R=0.361, p-value<0.001) but there was no correlation between serum total Zn and HOMA-B (R=-0.062, p-value=0.563). This study found that if the concentration of serum free Zn >1.7 ug/dL in central obese men was suggested as an excessive function of pancreatic beta cell and as an early warning before its exhausted.CONCLUSION: This study suggested that serum free Zn had a correlation with beta cell function and had a predictive ability for beta cell excessive function before its exhausted.KEYWORDS: Type 2 diabetes mellitus, HOMA-B, serum free zinc, central obesity

Reasons and mechanisms of organ remodeling in kidney, cardiovascular diseases and bronchialasthma

Organ and system remodeling is a very common process and is observed in some kidney, cardiovascular, GI, liver diseases and bronchial asthma. Main mechanism is tissue fibrosis in organs with excessive function.

TIME-DEPENDENT DIRICHLET FORMS AND RELATED STOCHASTIC CALCULUS

Analytic and probabilistic properties of symmetric or non-symmetric Dirichlet forms are well studied. But the processes with parabolic generators are out of the framework of symmetric Dirichlet forms. To cover these cases, we have introduced the time-dependent Dirichlet forms and studied their properties so far. In this expository article, we intend to explain in detail the analytic and probabilistic properties for time-dependent Dirichlet forms parallel to the symmetric Dirichlet forms. New results on a characterization of the minimal α-excessive function dominating a quasi-continuous function as well as the correspondence between additive functionals and smooth mesures are given. In particular, we emphasized the existence of the nontrivial semipolar sets under our settings.

Energy of Markov chains

After preliminaries on Markov chains, supermartingales and potential theory (Section 1), the energy of a potential supermartingale generated by an increasing process is defined. The paper examines some properties of the energy of potentials of the form U t = p(X t ) where p is a purely excessive function (which is also a potential of a charge) for a Markov chain (X t ). Also, the mutual energy of two potentials associated with the same Markov chain is discussed. Finally, several applications and examples are worked out in detail (Section 3).

Energy of Markov chains

After preliminaries on Markov chains, supermartingales and potential theory (Section 1), the energy of a potential supermartingale generated by an increasing process is defined. The paper examines some properties of the energy of potentials of the formUt=p(Xt) wherepis a purely excessive function (which is also a potential of a charge) for a Markov chain (Xt). Also, the mutual energy of two potentials associated with the same Markov chain is discussed. Finally, several applications and examples are worked out in detail (Section 3).