Insulin, Not the Preservative m-cresol, Instigates Loss of Infusion Site Patency Over Extended Durations of CSII in Diabetic Swine.

Insulin infusion sets worn for more than 4-5 days have been associated with a greater risk of unexplained hyperglycemia, a phenomenon that has been hypothesized to be caused by an inflammatory response to preservatives such as m-cresol and phenol. In this cross-over study in diabetic swine, we examined the role of the preservative m-cresol in inflammation and changes in infusion site patency. Insulin pharmacokinetics (PK) and glucose pharmacodynamics (PD) were measured on delivery of a bolus of regular human insulin U-100 (U-100R), formulated with or without 2.

Synthesis and Pharmacological Characterization of C4-Amide-Substituted 2-Aminobicyclo[3.1.0]hexane-2,6-dicarboxylates. Identification of (1 S,2 S,4 S,5 R,6 S)-2-Amino-4-[(3-methoxybenzoyl)amino]bicyclo[3.1.0]hexane-2,6-dicarboxylic Acid (LY2794193), a Highly Potent and Selective mGlu Receptor Agonist.

Multiple therapeutic opportunities have been suggested for compounds capable of selective activation of metabotropic glutamate 3 (mGlu) receptors, but small molecule tools are lacking. As part of our ongoing efforts to identify potent, selective, and systemically bioavailable agonists for mGlu and mGlu receptor subtypes, a series of C4-N-linked variants of (1 S,2 S,5 R,6 S)-2-amino-bicyclo[3.1.

Risk-stratified imputation in survival analysis.

Background: Censoring that is dependent on covariates associated with survival can arise in randomized trials due to changes in recruitment and eligibility criteria to minimize withdrawals, potentially leading to biased treatment effect estimates. Imputation approaches have been proposed to address censoring in survival analysis; while these approaches may provide unbiased estimates of treatment effects, imputation of a large number of outcomes may over- or underestimate the associated variance based on the imputation pool selected.

Purpose: We propose an improved method, risk-stratified imputation, as an alternative to address withdrawal related to the risk of events in the context of time-to-event analyses.

Sufficient dimension reduction and prediction in regression.

Dimension reduction for regression is a prominent issue today because technological advances now allow scientists to routinely formulate regressions in which the number of predictors is considerably larger than in the past. While several methods have been proposed to deal with such regressions, principal components (PCs) still seem to be the most widely used across the applied sciences. We give a broad overview of ideas underlying a particular class of methods for dimension reduction that includes PCs, along with an introduction to the corresponding methodology.