The Alzheimer’s disease (AD)-associated breakdown of the blood–brain barrier (BBB) promotes the accumulation of beta-amyloid peptide (Aβ) in the brain as the BBB cells provide Aβ transport from the brain parenchyma to the blood, and vice versa. The breakdown of the BBB during AD may be caused by the emergence of blood-borne Aβ pathogenic forms, such as structurally and chemically modified Aβ species; their effect on the BBB cells has not yet been studied. Here, we report that the effects of Aβ42, Aβ42, containing isomerized Asp7 residue (iso-Aβ42) or phosphorylated Ser8 residue (p-Aβ42) on the mitochondrial potential and respiration are closely related to the redox status changes in the mouse brain endothelial cells bEnd.3. Aβ42 and iso-Aβ42 cause a significant increase in nitric oxide, reactive oxygen species, glutathione, cytosolic calcium and the mitochondrial potential after 4 h of incubation. P-Aβ42 either does not affect or its effect develops after 24 h of incubation. Aβ42 and iso-Aβ42 activate mitochondrial respiration compared to p-Aβ42. The isomerized form promotes a greater cytotoxicity and mitochondrial dysfunction, causing maximum oxidative stress. Thus, Aβ42, p-Aβ42 and iso-Aβ42 isoforms differently affect the BBBs’ cell redox parameters, significantly modulating the functioning of the mitochondria. The changes in the level of modified Aβ forms can contribute to the BBBs’ breakdown during AD.
We aimed to replicate a published effect of transcranial direct-current stimulation (tDCS)-induced recognition enhancement over the human ventrolateral prefrontal cortex (VLPFC) and analyse the data with machine learning. We investigated effects over an adjacent region, the dorsolateral prefrontal cortex (DLPFC). In total, we analyzed data from 97 participants after exclusions. We found weak or absent effects over the VLPFC and DLPFC. We conducted machine learning studies to examine the effects of semantic and phonetic features on memorization, which revealed no effect of VLPFC tDCS on the original dataset or the current data. The highest contributing factor to memory performance was individual differences in memory not explained by word features, tDCS group, or sample size, while semantic, phonetic, and orthographic word characteristics did not contribute significantly. To our knowledge, this is the first tDCS study to investigate cognitive effects with machine learning, and future studies may benefit from studying physiological as well as cognitive effects with data-driven approaches and computational models.
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