Insulin release from pancreatic islets: Effects of CRF and excess PTH

Insulin release from pancreatic islets: Effects of CRF and excess PTH. Insulin secretion may be impaired in chronic renal failure (CRF) and available data suggest that this abnormality may be related to the state of secondary hyperparathyroidism of renal failure. We directly measured insulin release from isolated islets of Langerhans obtained from normal rats, CRF-control and CRF-PTX (parathyroidectomized) rats, and parathyroid hormone (PTH)-treated animals. Both early and total glucose-induced insulin release from islets of CRF-control were markedly and significantly (P < 0.01) lower than from islets of normal rats. Insulin release from islets of CRF-PTX rats was significantly (P < 0.01) higher than that from islets of CRF-control rats, and not different from insulin release from islets of normal rats. Forskolin and IBMX, which cause a rise in cAMP, significantly stimulated glucose-induced insulin release from islets of normal, CRF-control and CRF-PTX rats, but the increments from baseline were not significantly different between the three groups. Both early and total insulin release from islets obtained from PTH-treated rats with normal renal function were markedly and significantly (P < 0.01) lower than values obtained from normal rats. Calcium contents of the pancreas of CRF-control and PTH-treated rats were significantly (P < 0.01) higher than that in pancreas of normal rats and CRF-PTX animals, and values in the latter two groups of animals were not significantly different. The results show that: 1) CRF impairs insulin release from pancreatic islets; 2) this abnormality is reversed by prior parathyroidectomy; and 3) hyperparathyroidism induced by PTH-treatment in normal rats impairs insulin release from pancreatic islets. The data provide a direct evidence for the role of secondary hyperparathyroidism in the genesis of abnormal carbohydrate metabolism in CRF. This effect of excess PTH is not related to alterations in cAMP production but may potentially be due to calcium accumulation in the pancreas

Climate controls over ecosystem metabolism: insights from a fifteen-year inductive artificial neural network synthesis for a subalpine forest

Eddy covariance (EC) datasets have provided insight into climate determinants of net ecosystem productivity (NEP) and evapotranspiration (ET) in natural ecosystems for decades, but most EC studies were published in serial fashion such that one study's result became the following study's hypothesis. This approach reflects the hypothetico-deductive process by focusing on previously derived hypotheses. A synthesis of this type of sequential inference reiterates subjective biases and may amplify past assumptions about the role, and relative importance, of controls over ecosystem metabolism. Long-term EC datasets facilitate an alternative approach to synthesis: the use of inductive data-based analyses to re-examine past deductive studies of the same ecosystem. Here we examined the seasonal climate determinants of NEP and ET by analyzing a 15-year EC time-series from a subalpine forest using an ensemble of Artificial Neural Networks (ANNs) at the half-day (daytime/nighttime) time-step. We extracted relative rankings of climate drivers and driver-response relationships directly from the dataset with minimal a priori assumptions. The ANN analysis revealed temperature variables as primary climate drivers of NEP and daytime ET, when all seasons are considered, consistent with the assembly of past studies. New relations uncovered by the ANN approach include the role of soil moisture in driving daytime NEP during the snowmelt period, the nonlinear response of NEP to temperature across seasons, and the low relevance of summer rainfall for NEP or ET at the same daytime/nighttime time step. These new results offer a more complete perspective of climate-ecosystem interactions at this site than traditional deductive analyses alone