A beta 1-40, a major component of Alzheimer's disease cerebral amyloid, is present in the cerebrospinal fluid and remains relatively soluble at high concentrations (less than or equal to 3.7 mM). Thus, physiological factors which induce A beta amyloid formation could provide clues to the pathogenesis of the disease. It has been shown that human A beta specifically and saturably binds zinc. Here, concentrations of zinc above 300 nM rapidly destabilized human A beta 1-40 solutions, inducing tinctorial amyloid formation. However, rat A beta 1-40 binds zinc less avidly and is immune to these effects, perhaps explaining the scarcity with which these animals form cerebral A beta amyloid. These data suggest a role for cerebral zinc metabolism in the neuropathogenesis of Alzheimer's disease.

Familial Alzheimer's disease (FAD) is a genetically heterogeneous disorder that includes a rare early-onset form linked to mutations in the amyloid b protein precursor (APP) gene. Clues to the function of APP derive from the recent finding that it is a member of a highly conserved protein family that includes the mammalian amyloid precursor-like protein (APLP1) gene which maps to the same general region of human chromosome 19 linked to late-onset FAD. Here we report the isolation of the human APLP2 gene. We show that APLP2 is a close relative of APP and exhibits a very similar pattern of expression in the brain and throughout the body. Like APP, APLP2 contains a cytoplasmic domain predicted to couple with the GTP-binding protein G(o) indicating that it may be an additional cell surface activator of this G protein.

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Background: The number of cases from the coronavirus disease 2019 (COVID-19) global pandemic has overwhelmed existing medical facilities and forced clinicians, patients, and families to make pivotal decisions with limited time and information. Main body: While machine learning (ML) methods have been previously used to augment clinical decisions, there is now a demand for "Emergency ML." Throughout the patient care pathway, there are opportunities for MLsupported decisions based on collected vitals, laboratory results, medication orders, and comorbidities. With rapidly growing datasets, there also remain important considerations when developing and validating ML models. Conclusion: This perspective highlights the utility of evidence-based prediction tools in a number of clinical settings, and how similar models can be deployed during the COVID-19 pandemic to guide hospital frontlines and healthcare administrators to make informed decisions about patient care and managing hospital volume.

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Patients with coronavirus disease 2019 (COVID-19) can have increased risk of mortality shortly after intubation. The aim of this study is to develop a model using predictors of early mortality after intubation from COVID-19. A retrospective study of 1945 intubated patients with COVID-19 admitted to 12 Northwell hospitals in the greater New York City area was performed. Logistic regression model using backward selection was applied. This study evaluated predictors of 14-day mortality after intubation for COVID-19 patients. The predictors of mortality within 14 days after intubation included older age, history of chronic kidney disease, lower mean arterial pressure or increased dose of required vasopressors, higher urea nitrogen level, higher ferritin, higher oxygen index, and abnormal pH levels. We developed and externally validated an intubated COVID-19 predictive score (ICOP). The area under the receiver operating characteristic curve was 0.75 (95% CI 0.73–0.78) in the derivation cohort and 0.71 (95% CI 0.67–0.75) in the validation cohort; both were significantly greater than corresponding values for sequential organ failure assessment (SOFA) or CURB-65 scores. The externally validated predictive score may help clinicians estimate early mortality risk after intubation and provide guidance for deciding the most effective patient therapies.

Inherited Alzheimer's disease is a genetically heterogeneous disorder that involves gene defects on at least five chromosomal loci. Three of these loci have been found by genetic linkage studies to reside on chromosomes 21, 19, and 14. On chromosomes 21, the gene encoding the precursor protein of Alzheimer-associated amyloid (APP) has been shown to contain several mutations in exons 16 and 17 which account for roughly 2-3% of familial Alzheimer's disease (FAD). The other loci include what appears to be a susceptibility gene on chromosome 19 associated with late-onset (> 65 years) FAD, and a major early-onset FAD gene defect on the long arm of chromosome 14. In other early- and late-onset FAD kindreds, the gene defects involved do not appear to be linked to any of these three loci, indicating the existence of additional and as of yet unlocalized FAD genes. This review provides a historical perspective of the search for FAD gene defects and summarizes the progress made in world-wide attempts to isolate and characterize the genes responsible for this disorder.

An early hurdle in the optimization of small-molecule chemical probes and drug discovery entities is the attainment of sufficient exposure in the mouse via oral administration of the compound. While computational approaches have attempted to predict molecular properties related to the mouse pharmacokinetic (PK) profile, we present herein a machine learning approach to specifically predict the oral exposure of a compound as measured in the mouse snapshot PK assay. A random forest workflow was found to produce the best cross-validation and external test set statistics after processing of the input data set and optimization of model features. The modeling approach should be useful to the chemical biology and drug discovery communities to predict this key molecular property and afford chemical entities of translational significance.