Basic Science and Pathogenesis.

Background: In Alzheimer's disease (AD), histone acetylation is disrupted, suggesting loss of transcriptional control. Moreover, converging evidence suggests an age- and AD-dependent loss of transcription controlled by all-trans-retinoic acid (ATRA), the bioactive metabolite of vitamin A (VA). Antioxidant depletion causes oxidative stress (OS).

Basic Science and Pathogenesis.

Background: Disrupted balance between amyloidogenic and non-amyloidogenic pathways leads to cognitive decline in Alzheimer's disease (AD). Evidence suggests vitamin A (VA) supplementation favors the non-amyloidogenic pathway through upregulation of α-secretase. Originally used to map embryonic retinoic acid (RA) signaling, RARE-LacZ mice possess multiple LacZ genes controlled by retinoic acid response elements (RAREs).

Basic Science and Pathogenesis.

Background: Glycosylation is the most common post-translational modification in the brain. Aberrant glycosylation patterns are present in cerebrospinal fluid and brain tissue from Alzheimer's disease (AD) patients. Specifically, dysregulation of a particular form of terminal glycoconjugate modification, sialylation, has been identified in AD.

Basic Science and Pathogenesis.

Background: The glymphatic system has been suggested as an important clearance mechanism for amyloid-β (Aβ) during sleep. Animal and cellular models have suggested this clearance mechanism involves the water-channel protein, Aquaporin-4 (encoded by the AQP4 gene), located primarily in the astrocytic end-feet. We have previously reported on the interaction between genetic variants within AQP4, sleep and cross-sectional cortical amyloid-β (Aβ) burden.

Unsupervised machine learning analysis to identify patterns of ICU medication use for fluid overload prediction.

Background: Fluid overload (FO) in the intensive care unit (ICU) is common, serious, and may be preventable. Intravenous medications (including administered volume) are a primary cause for FO but are challenging to evaluate as a FO predictor given the high frequency and time-dependency of their use and other factors affecting FO. We sought to employ unsupervised machine learning methods to uncover medication administration patterns correlating with FO.