Cholinergic white matter pathways along the Alzheimer's disease continuum

Nemy et al. investigate cholinergic white matter projections along the Alzheimer's disease continuum. They show that alterations are already present in individuals with subjective cognitive decline, preceding the more widespread alterations seen in mild cognitive impairment and Alzheimer's disease dementia. Previous studies have shown that the cholinergic nucleus basalis of Meynert and its white matter projections are affected in Alzheimer's disease dementia and mild cognitive impairment. However, it is still unknown whether these alterations can be found in individuals with subjective cognitive decline, and whether they are more pronounced than changes found in conventional brain volumetric measurements. To address these questions, we investigated microstructural alterations of two major cholinergic pathways in individuals along the Alzheimer's disease continuum using an in vivo model of the human cholinergic system based on neuroimaging. We included 402 participants (52 Alzheimer's disease, 66 mild cognitive impairment, 172 subjective cognitive decline and 112 healthy controls) from the Deutsches Zentrum für Neurodegenerative Erkrankungen Longitudinal Cognitive Impairment and Dementia Study. We modelled the cholinergic white matter pathways with an enhanced diffusion neuroimaging pipeline that included probabilistic fibre-tracking methods and prior anatomical knowledge. The integrity of the cholinergic white matter pathways was compared between stages of the Alzheimer's disease continuum, in the whole cohort and in a CSF amyloid-beta stratified subsample. The discriminative power of the integrity of the pathways was compared to the conventional volumetric measures of hippocampus and nucleus basalis of Meynert, using a receiver operating characteristics analysis. A multivariate model was used to investigate the role of these pathways in relation to cognitive performance. We found that the integrity of the cholinergic white matter pathways was significantly reduced in all stages of the Alzheimer's disease continuum, including individuals with subjective cognitive decline. The differences involved posterior cholinergic white matter in the subjective cognitive decline stage and extended to anterior frontal white matter in mild cognitive impairment and Alzheimer's disease dementia stages. Both cholinergic pathways and conventional volumetric measures showed higher predictive power in the more advanced stages of the disease, i.e. mild cognitive impairment and Alzheimer's disease dementia. In contrast, the integrity of cholinergic pathways was more informative in distinguishing subjective cognitive decline from healthy controls, as compared with the volumetric measures. The multivariate model revealed a moderate contribution of the cholinergic white matter pathways but not of volumetric measures towards memory tests in the subjective cognitive decline and mild cognitive impairment stages. In conclusion, we demonstrated that cholinergic white matter pathways are altered already in subjective cognitive decline individuals, preceding the more widespread alterations found in mild cognitive impairment and Alzheimer's disease. The integrity of the cholinergic pathways identified the early stages of Alzheimer's disease better than conventional volumetric measures such as hippocampal volume or volume of cholinergic nucleus basalis of Meynert

Tissue specific electrochemical fingerprinting.

BACKGROUND: Proteomics and metalloproteomics are rapidly developing interdisciplinary fields providing enormous amounts of data to be classified, evaluated and interpreted. Approaches offered by bioinformatics and also by biostatistical data analysis and treatment are therefore of extreme interest. Numerous methods are now available as commercial or open source tools for data processing and modelling ready to support the analysis of various datasets. The analysis of scientific data remains a big challenge, because each new task sets its specific requirements and constraints that call for the design of a targeted data pre-processing approach. METHODOLOGY/PRINCIPAL FINDINGS: This study proposes a mathematical approach for evaluating and classifying datasets obtained by electrochemical analysis of metallothionein in rat 9 tissues (brain, heart, kidney, eye, spleen, gonad, blood, liver and femoral muscle). Tissue extracts were heated and then analysed using the differential pulse voltammetry Brdicka reaction. The voltammograms were subsequently processed. Classification models were designed making separate use of two groups of attributes, namely attributes describing local extremes, and derived attributes resulting from the level=5 wavelet transform. CONCLUSIONS/SIGNIFICANCE: On the basis of our results, we were able to construct a decision tree that makes it possible to distinguish among electrochemical analysis data resulting from measurements of all the considered tissues. In other words, we found a way to classify an unknown rat tissue based on electrochemical analysis of the metallothionein in this tissue

Suggested Tissue Electrochemical Fingerprinting is based on sampling a tissue, which is then extracted and heat treated.

<p>The denatured sample is further electrochemically analysed using the differential pulse voltammetry Brdicka reaction. The data that is obtained is processed using an optimized protocol, and a tissue is identified.</p

Electrochemical sensors and biosensors for identification of viruses: a critical review.

Due to their life cycle, viruses can disrupt the metabolism of their hosts, causing diseases. If we want to disrupt their life cycle, it is necessary to identify their presence. For this purpose, it is possible to use several molecular-biological and bioanalytical methods. The reference selection was performed based on electronic databases (2020–2023). This review focused on electrochemical methods with high sensitivity and selectivity (53% voltammetry/amperometry, 33% impedance, and 12% other methods) which showed their great potential for detecting various viruses. Moreover, the aforementioned electrochemical methods have considerable potential to be applicable for care-point use as they are portable due to their miniaturizability and fast speed analysis (minutes to hours), and are relatively easy to interpret. A total of 2011 articles were found, of which 86 original papers were subsequently evaluated (the majority of which are focused on human pathogens, whereas articles dealing with plant pathogens are in the minority). Thirty-two species of viruses were included in the evaluation. It was found that most of the examined research studies (77%) used nanotechnological modifications. Other ones performed immunological (52%) or genetic analyses (43%) for virus detection. 5% of the reports used peptides to increase the method’s sensitivity. When evaluable, 65% of the research studies had LOD values in the order of ng or nM. The vast majority (79%) of the studies represent proof of concept and possibilities with low application potential and a high need of further research experimental work

Detail of intersections of Brdicka curves for all considered body tissues.

<p>Detail of intersections of Brdicka curves for all considered body tissues.</p

Brdicka reaction of metallothionein.

<p>(A) Presumable scheme of the sequence of electrochemical reactions at the mercury electrode when the Brdicka reaction is applied for MT analysis. (B) A typical DPV voltammogram of MT measured in the presence of a supporting electrolyte containing 1 mM Co(NH₃)₆Cl₃ and 1 M NH₃(aq)+NH₄Cl, pH = 9.6; dotted line: voltammogram of a supporting electrolyte without MT. During MT analysis four peaks, Co1, RS₂Co, Cat1 and Cat2 that correspond to the MT level can be observed. Heights of (C) Cat2, (D) RS₂Co and (E) Cat1 peaks measured in extracts of various rat tissues. (C) Metallothionein level in single tissues. The highest level is in liver and kidney, i.e. organs providing detoxification, and in brain. In comparison, the level of MT in muscle and in heart is almost 50% lower.</p

Decision tree of all samples from the cleaned dataset.

<p>Decision tree of all samples from the cleaned dataset.</p

Confusion matrix for decision trees constructed from attributes obtained by a wavelet transform of level 5.

<p>Confusion matrix for decision trees constructed from attributes obtained by a wavelet transform of level 5.</p

A screenshot of the SW tool that was used, depicting all Brdicka curves measured for all the body parts of a single subject, namely P3.

<p>A screenshot of the SW tool that was used, depicting all Brdicka curves measured for all the body parts of a single subject, namely P3.</p

Transformation of DP voltammograms.

<p>(A) Haar’s Simple Wavelet transformation – brain. (B) Haar’s Simple Wavelet transformation – eye.</p