Erratum: Body composition study by dual-energy x-ray absorptiometry in familial partial lipodystrophy: finding new tools for an objective evaluation.

[This corrects the article DOI: 10.1186/1758-5996-4-40.].

The role of glucagon on type 2 diabetes at a glance.

The opposite effects of insulin and glucagon in fuel homeostasis, the paracrine/endocrine inhibitory effects of insulin on glucagon secretion and the hyperglucagonemia in the pathogenesis of type 2 diabetes (T2D) have long been recognized. Inappropriately increased alpha-cell function importantly contributes to hyperglycemia and reflects the loss of tonic restraint normally exerted by high local concentrations of insulin on alpha-cells, possibly as a result of beta-cell failure and alpha-cell insulin resistance, but additional mechanisms, such as the participation of incretin hormones in this response, have also been suggested. Three classes of drugs already available for clinical use address the abnormalities of glucagon secretion in T2D, namely, the GLP-1 receptor agonists (GLP-1RA), the inhibitors of dipeptidyl peptidase-4 (DPP-4i) and the amylin agonist pramlintide; it has been proposed that the glucagonostatic and insulinotropic effects of GLP-1RA equally contribute to their hypoglycemic efficacy.

Body composition study by dual-energy x-ray absorptiometry in familial partial lipodystrophy: finding new tools for an objective evaluation.

Background: Familial partial lipodystrophies (FPLD) are clinically heterogeneous disorders characterized by selective loss of adipose tissue, insulin resistance and metabolic complications. Until genetic studies become available for clinical practice, clinical suspicion and pattern of fat loss are the only parameters leading clinicians to consider the diagnosis. The objective of this study was to compare body composition by dual energy X-ray absorptiometry (DXA) in patients with FPLD and control subjects, aiming to find objective variables for evaluation of FPLD.

Multilayer neural networks and function reconstruction by using a priori knowledge.

This paper proposes a new methodology to approximate functions by incorporating a priori information. The relationship between the proposed scheme and multilayer neural networks is explored theoretically and numerically. This approach is particularly interesting for the very relevant class of limited spectrum functions.