Alzheimer's disease pathology:pathways between central norepinephrine activity, memory, and neuropsychiatric symptoms

The locus coeruleus (LC) supplies norepinephrine to the brain, is one of the first sites of tau deposition in Alzheimer's disease (AD) and modulates a variety of behaviors and cognitive functions. Transgenic mouse models showed that norepinephrine dysregulation after LC lesions exacerbates inflammatory responses, blood-brain barrier leakage (BBB), and cognitive deficits. Here, we investigated relationships between central norepinephrine metabolism, tau and beta-amyloid (Aβ), inflammation, BBB-dysfunction, neuropsychiatric problems, and memory in-vivo in a memory clinic population (total n = 111, 60 subjective cognitive decline, 36 mild cognitively impaired, and 19 AD dementia). Cerebrospinal fluid (CSF) and blood samples were collected and analyzed for 3-methoxy-4-hydroxyphenylethyleneglycol (MHPG), CSF/plasma albumin ratio (Q-alb), Aβ, phosphorylated tau, and interleukins. The verbal word learning task and the neuropsychiatric inventory assessed memory functioning and neuropsychiatric symptoms. Structural equation models tested the relationships between all fluid markers, cognition and behavior, corrected for age, education, sex, and clinical dementia rating score. Our results showed that neuropsychiatric symptoms show strong links to both MHPG and p-tau, whereas memory deficits are linked to MHPG via a combination of p-tau and inflammation-driven amyloidosis (30-35% indirect effect contribution). These results suggest that the LC-norepinephrine may be pivotal to understand links between AD pathology and behavioral and cognitive deficits in AD

Switching from serum to plasma: Implementation of BD Vacutainer® Barricor™ Plasma Blood Collection Tubes improves sample quality and laboratory turnaround time

Background: For blood, most 24/7 standard (immuno)chemistry parameters are either measured in serum or in lithium heparin plasma. Standard serum and plasma gel tubes have their shortcomings when timely analysis of high quality results is required. Serum requires clotting time and interference of gel globules in the plasma and adsorption of hydrophobic analytes into the gel layer potentially compromises high quality results from lithium heparin gel tubes. We sought to evaluate the impact of BD Vacutainer® Barricor™ Tube (Barricor™) on laboratory efficiency by measuring its effect on TAT and sample quality, as well as evaluate potential cost opportunities resulting from improved sample quality. Methods: TAT data and remediation activities were extracted and captured during two 6 months phases. Serum was used as the predominant matrix in the first phase and Barricor™ plasma was used in the second phase. Results: Barricor™ significantly reduced the median TAT, especially for routine-priority samples during peak-hours. The TAT key-performance-indicator (percentage of results available within 90 ​min) improved to >90% for STAT as well as routine priority samples. Converting from serum gel, Barricor™ reduced fibrin-related remediation activities from 2.3% to 0.4%. This resulted in remediation-related cost reduction of €6.010,47 over the study period. Conclusions: By implementing Barricor™, we saw a significant reduction in TAT and a reduction in fibrin-related remediation time and costs, when compared to a predominant serum workflow. The improved TAT opens up the possibility of consolidating to one single priority level, eliminating the need for the use of the STAT priority level

Housekeeping genes for quantitative expression studies in the three-spined stickleback Gasterosteus aculeatus

Background During the last years the quantification of immune response under immunological challenges, e.g. parasitation, has been a major focus of research. In this context, the expression of immune response genes in teleost fish has been surveyed for scientific and commercial purposes. Despite the fact that it was shown in teleostei and other taxa that the gene for beta-actin is not the most stably expressed housekeeping gene (HKG), depending on the tissue and experimental treatment, the gene has been us Results To establish a reliable method for the measurement of immune gene expression in Gasterosteus aculeatus, sequences from the now available genome database and an EST library of the same species were used to select oligonucleotide primers for HKG, in order to perform quantitative reverse-transcription (RT) PCR. The expression stability of ten candidate reference genes was evaluated in three different tissues, and in five parasite treatment groups, using the three algorithms BestKeeper, geNorm and N Conclusion As they were the most stably expressed genes in all tissues examined, we suggest using the genes for the L13a ribosomal binding protein and ubiquitin as alternative or additional reference genes in expression analysis in Gasterosteus aculeatus.

Amplification efficiency: linking baseline and bias in the analysis of quantitative PCR data

Despite the central role of quantitative PCR (qPCR) in the quantification of mRNA transcripts, most analyses of qPCR data are still delegated to the software that comes with the qPCR apparatus. This is especially true for the handling of the fluorescence baseline. This article shows that baseline estimation errors are directly reflected in the observed PCR efficiency values and are thus propagated exponentially in the estimated starting concentrations as well as ‘fold-difference’ results. Because of the unknown origin and kinetics of the baseline fluorescence, the fluorescence values monitored in the initial cycles of the PCR reaction cannot be used to estimate a useful baseline value. An algorithm that estimates the baseline by reconstructing the log-linear phase downward from the early plateau phase of the PCR reaction was developed and shown to lead to very reproducible PCR efficiency values. PCR efficiency values were determined per sample by fitting a regression line to a subset of data points in the log-linear phase. The variability, as well as the bias, in qPCR results was significantly reduced when the mean of these PCR efficiencies per amplicon was used in the calculation of an estimate of the starting concentration per sample

An Interpretable Machine Learning Model with Deep Learning-based Imaging Biomarkers for Diagnosis of Alzheimer's Disease

Machine learning methods have shown large potential for the automatic early diagnosis of Alzheimer's Disease (AD). However, some machine learning methods based on imaging data have poor interpretability because it is usually unclear how they make their decisions. Explainable Boosting Machines (EBMs) are interpretable machine learning models based on the statistical framework of generalized additive modeling, but have so far only been used for tabular data. Therefore, we propose a framework that combines the strength of EBM with high-dimensional imaging data using deep learning-based feature extraction. The proposed framework is interpretable because it provides the importance of each feature. We validated the proposed framework on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, achieving accuracy of 0.883 and area-under-the-curve (AUC) of 0.970 on AD and control classification. Furthermore, we validated the proposed framework on an external testing set, achieving accuracy of 0.778 and AUC of 0.887 on AD and subjective cognitive decline (SCD) classification. The proposed framework significantly outperformed an EBM model using volume biomarkers instead of deep learning-based features, as well as an end-to-end convolutional neural network (CNN) with optimized architecture.Comment: 11 pages, 5 figure