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The isoforms of the APOE gene are of profound importance regarding the onset of Alzheimer's disease (AD), with APOE2 conferring resistance, APOE3 conferring neutral susceptibility, and APOE4 conferring proneness to AD. L-cysteine is an amino acid that has several anti-AD properties, among which are its ability to sequester iron and form glutathione (GSH), a powerful antioxidant. In our experiment, we fed Mus musculus (mice) homozygous for APOE2, APOE3, and APOE4 either a control diet or a diet high in L-cysteine. Using Western blotting analysis, we quantified total APOE proteins extracted from post-mortem brains of APOE2, APOE3, and APOE4 homozygous mice, total Amyloid β (Aβ) protein, and total hyper-phosphorylated Tau (HP-Tau) from mice at 3-, 6-, 9-, and 12-month ages. We found that administration of L-cysteine trends toward lowering levels of Aβ in the APOE3 cohort, but this effect is statistically insignificant. On the other hand, L-cysteine caused a significant increase in APOE4 abundance, but a significant decrease in APOE3 abundance regarding diet [F(6,42) = 5.61, p = 0.01]. Furthermore, administration of L-cysteine revealed trends toward lowering HP-Tau deposition in the APOE2 and APOE3 cohorts, but increasing deposition in the APOE4 cohort, although these effects are statistically insignificant. Moreover, immunohistochemistry analyses on the hippocampus and midsagittal brain revealed no effects of L-cysteine on Aβ. Results also showed a decrease in HP-Tau without regard to APOE genotype, but this was not statistically significant (p = 0.18). Taken together, these data suggest that L-cysteine may serve as a promising intervention for AD pathology, although future studies necessitate increasing statistical power to confirm the effect of diet on Aβ and HP-Tau deposition.

We propose that Alzheimer's disease (AD) progression is largely caused by excess reactive oxygen species (ROS) or free radicals created by iron dysregulation. An AD brain is struggling with damage control creating harmful tau tangles and amyloid plaques to deal with the dysregulated iron. We hypothesized that transgenic APP/PS1 (Amyloid precursor protein/ Presenilin-1) and Tau mice would exhibit higher levels of deposits in the brain which can be detected through MRI as well as decreased behavioral performance in radial arm maze tasks. We bred APP/PS1 transgenic mice overexpressing chimeric mouse/human APP-695 with mutations and human PSEN1 carrying the exon-9-deleted variant (PSEN1dE9), and Tau mice overexpressing all six isoforms of hyper-phosphorylated human MAPT (Microtubule associated protein Tau), which were compared with age controlled wild type mice. Mice received a diet of either regular or methionine rich chow as an oxidative stressor. Subgroups received a rescue treatment of either zinc, metformin or clioquinol chow. MRI (Magnetic Resonance Imaging) scans were performed using a Siemens 3 Tesla scanner. Behavioral data was collected using a radial arm maze (RAM) for 2 weeks at each point. Data collection time points were: 1 (baseline), 3, 6 and 9 months. Mean T2 TSE signals from scans on these mice revealed significant signal loss in bilateral hippocampi when compared by age. We also found a significant main effect of genotype and a trend toward significance for genotype and treatment interaction in the mean time mice spent in the RAM. Pairwise comparison showed a significant difference between the time male and female mice spent in the RAM. There was, however, no effect of signal loss or behavior deficit when comparing rescue treatments with or without oxidative insults. The decrease in signal and RAM performance is due to plaque increase and accompanying iron, which offers a possibility to refine the imaging techniques in pursuit of a noninvasive diagnostic biomarker.

Purpose of reviewAssistive (nonautonomous) artificial intelligence (AI) models designed to support (rather than function independently of) clinicians have received increasing attention in medicine. This review aims to highlight several recent developments in these models over the past year and their ophthalmic implications.Recent findingsArtificial intelligence models with a diverse range of applications in ophthalmology have been reported in the literature over the past year. Many of these systems have reported high performance in detection, classification, prognostication, and/or monitoring of retinal, glaucomatous, anterior segment, and other ocular pathologies.SummaryOver the past year, developments in AI have been made that have implications affecting ophthalmic surgical training and refractive outcomes after cataract surgery, therapeutic monitoring of disease, disease classification, and prognostication. Many of these recently developed models have obtained encouraging results and have the potential to serve as powerful clinical decision-making tools pending further external validation and evaluation of their generalizability.

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