Chronic Sleep Deprivation Activates NLRP3 Inflammasome and Exacerbates Aβ Plaques Deposition in a Mouse Model of Alzheimer’s Disease

Overview

Chronic sleep deprivation (CSD) is a condition resulting from insufficient sleep over a prolonged period of time, which affects one in three adults in the United States. Alzheimer’s disease (AD) is a progressive neurodegenerative disease and the most common form of dementia. Research has shown that people with CSD are more susceptible to developing neurological disorders such as AD in later life. However, the underlying mechanisms responsible for this link remain to be elucidated. The present study is aimed at discovering the underlying mechanism and identifying a potential therapeutic target to reduce the risk of CSD on AD. Here we show that CSD increased NLRP3 inflammasome activation in the brain of a mouse model of AD. Activated NLRP3 inflammasome co-localized with Aβ plaques, a pathological hallmark of AD, indicating that NLRP3 inflammasome may promote Aβ plaques deposition. Consistent with this hypothesis, increased Aβ plaque deposition was also observed after CSD in the brain of the AD model. Importantl y, increased NLRP3 inflammasome activation was detected in the brain of wild -type mice post CSD, suggesting NLRP3 inflammasome can be activated independent of Aβ pathology and therefore upstream of Aβ plaques deposition in the brain of AD mice under CSD co ndition. Together, these findings support the hypothesis that CSD-induced inflammasome activation may exacerbate Aβ pathology. Future studies should investigate if NLRP3 inflammasome inhibitors can reduce Aβ plaque deposition post CSD, which may provide a new direction for the treatment of AD. Puerto Rico Discovery of New Extragalactic Planet Candidates: A Novel End-to-end Machine Learning Pipeline for Efficient Transit Detection in the X-ray Spectrum Emily N. Alemán García CROEC, Ceiba, Puerto Rico The discovery of M51 -ULS-1b, the only known extragalactic planet candidate, introduced a vast new playground in modern astronomy —planetary formation under extreme environments. The finding simultaneously demonstrated that planetary detection techniques com monly employed in optical wavelengths, such as the transit method, can be adapted to the X -ray spectrum given X -ray emissions originating from accretion disks in X -ray binary systems (XRBS). However, despite enabling planetary detection, XRBS observations are sparse and noise -contaminated, thus presenting additional challenges in distinguishing genuine planet signals from system -generated variability. This study introduces the first end-to-end machine learning pipeline designed to automate the identificatio n of eclipses and third -body candidates in XRBS observed by the Chandra X-ray Observatory. The pipeline covers data extraction, pre- processing, and feature engineering through advanced Bayesian block techniques into a random forest model (RFM). Using over 1500 real observations and synthetic examples, the model achieved an outstanding accuracy of 99.5% in identifying transits. Rigorous energy -independence testing was incorporated to ensure the consistency of transit effects across diverse X -ray energy bands. This not only verifies the nature of the transiting body but also enhances the reliability of transit predictions. The RFM's robustness is further substantiated through k-folding cross-validation and a validation set. In validation, the model successfully identified the transit of M51 -ULS-1b and 13 new transits, spanning both extended - duration eclipses and brief -duration third -body candidates within eight distinct sources. This pipeline’s success marks a significant advancement in automating the discovery of extragalactic planets, facilitating the discovery of promising targets for further investigation into planetary formation within complex systems.