Locus coeruleus at asymptomatic early and middle Braak stages of neurofibrillary tangle pathology.

AIMS: The present study analyses molecular characteristics of the locus coeruleus (LC) and projections to the amygdala and hippocampus at asymptomatic early and middle Braak stages of neurofibrillary tangle (NFT) pathology. METHODS: Immunohistochemistry, whole-transcriptome arrays and RT-qPCR in LC and western blotting in hippocampus and amygdala in a cohort of asymptomatic individuals at stages I-IV of NFT pathology were used. RESULTS: NFTs in the LC increased in parallel with colocalized expression of tau kinases, increased neuroketal adducts and decreased superoxide dismutase 1 in neurons with hyperphosphorylated tau and decreased voltage-dependent anion channel in neurons containing truncated tau were found. These were accompanied by increased microglia and AIF1, CD68, PTGS2, IL1ß, IL6 and TNF-α gene expression. Whole-transcriptome arrays revealed upregulation of genes coding for proteins associated with heat shock protein binding and genes associated with ATP metabolism and downregulation of genes coding for DNA-binding proteins and members of the small nucleolar RNAs family, at stage IV when compared with stage I. Tyrosine hydroxylase (TH) immunoreactivity was preserved in neurons of the LC, but decreased TH and increased α2A adrenergic receptor protein levels were found in the hippocampus and the amygdala. CONCLUSIONS: Complex alteration of several metabolic pathways occurs in the LC accompanying NFT formation at early and middle asymptomatic stages of NFT pathology. Dopaminergic/noradrenergic denervation and increased expression of α2A adrenergic receptor in the hippocampus and amygdala occur at first stage of NFT pathology, suggesting compensatory activation in the face of decreased adrenergic input occurring before clinical evidence of cognitive impairment and depression.

Assessing influence in regression analysis with censored data.

In this paper we show how to evaluate the effect that perturbations to the model, data, or case weights have on maximum likelihood estimates from censored survival data. The ideas and methods also apply to other nonlinear estimation problems. We review the ideas behind using log-likelihood displacement and local influence methods. We describe new interpretations for some local influence statistics and show how these statistics extend and complement traditional case deletion influence statistics for linear least squares. These statistics identify individual and combinations of cases that have important influence on estimates of parameters and functions of these parameters. We illustrate the methods by reanalyzing the Stanford Heart Transplant data with a parametric regression model.