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Context: Existing researches confirmed that β amyloid (Aβ) has a high affinity for the α7 nicotinic acetylcholine receptor (α7nAChR), associating closely to Alzheimer's disease. The majority of related studies focused on the experimental reports on the neuroprotective role of Aβ fragment (Aβx), however, with a lack of investigation into the most suitable binding region and mechanism of action between Aβ fragment and α7nAChR. In the study, we employed four Aβ1–42 fragments Aβx, Aβ1–16, Aβ10–16, Aβ12–28, and Aβ30–42, of which the first three were confirmed to play neuroprotective roles upon directly binding, to interact with α7nAChR. Methods: The protein–ligand docking server of CABS-DOCK was employed to obtain the α7nAChR-Aβx complexes. Only the top α7nAChR-Aβx complexes were used to perform all-atom GROMACS dynamics simulation in combination with Charmm36 force field, by which α7nAChR-Aβx interactions' dynamic behavior and specific locations of these different Aβx fragments were identified. MM-PBSA calculations were also done to estimate the binding free energies and the different contributions from the residues in the Aβx. Two distinct results for the first three and fourth Aβx fragments in binding site, strength, key residue, and orientation, account for why the fourth fails to play a neuroprotective role at the molecular level. [ABSTRACT FROM AUTHOR]

The alterations in electroencephalogram (EEG) signals are the complex outputs of functional factors, such as normal physiological aging, pathological process, which results in further cognitive decline. It is not clear that when brain aging initiates, but elderly people are vulnerable to be incipient of neurodegenerative diseases such as Alzheimer's disease. The EEG signals were recorded from 20 healthy middle age and 20 healthy elderly subjects while performing a working memory task. Higuchi's fractal dimension (HFD), Katz's fractal dimension (KFD), sample entropy and three Hjorth parameters were extracted to analyse the complexity of EEG signals. Four machine learning classifiers, multilayer perceptron (MLP), support vector machine (SVM), K-nearest neighbour (KNN), and logistic model tree (LMT) were employed to distinguish the EEG signals of middle age and elderly age groups. HFD, KFD and Hjorth complexity were found significantly correlated with age. MLP achieved the highest overall accuracy of 93.75%. For posterior region, the maximum accuracy of 92.50% was achieved using MLP. Since fractal dimension associated with the complexity of EEG signals, HFD, KFD and Hjorth complexity demonstrated the decreased complexity from middle age to elderly groups. The complexity features appear to be more appropriate indicators of monitoring EEG signal complexity in healthy aging. [ABSTRACT FROM AUTHOR]

Amyloid beta (Aβ) proteins accumulate along neuronal circuits in Alzheimer's disease. Elucidating the seeding region and related context as well as their association can provide insight into the origins of misfolding, aggregation, and fibril growth and can subsequently increase our understanding of the mechanisms of pathology progression. To this end, we investigated the folding dynamics of Aβ42 monomer at pH 4.0–7.5 with/without physiological salt and predicted, for the first time, the subtle equilibrium between the seeding region (including charge and polarity of residues) of Aβ42 and the interaction context (pH and Na+/Cl− ions). The solution acidity, number of positive/negative ions, and distribution of charged residues in each Aβ42 region jointly impact the seeding region and seeding rate. The association of these factors well reconciles the controversy regarding the optimal pH (5.0–5.5 or 6.0?) for Aβ42 to trigger seeding and whether the first seeding region is β1 or β2. [ABSTRACT FROM AUTHOR]