Ubiquity of the SARS-CoV-2 receptor ACE2 and upregulation in limbic regions of Alzheimer’s disease brain
Bibliographic record
Abstract
The major route of entry for the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) into human host cells is by means of the angiotensin-converting enzyme-2 (ACE2) transmembrane receptor. This zinc-containing carboxypeptidase and membrane-integral surface receptor is ubiquitous and widely expressed in multiple cell types. Hence SARS-CoV-2, an unusually large RNA virus that causes coronavirus disease 2019 (COVID-19) has the remarkable capacity to invade many different types of human host cells simultaneously. Although COVID-19 is generally considered to be primarily an acute respiratory disease SARS-CoV-2 also targets specific anatomical regions of the central nervous system (CNS). In the normal CNS the highest ACE2 levels of expression are found within the medullary respiratory centers of the brainstem and this, in part, may explain the susceptibility of numerous COVID-19 patients to severe respiratory distress. About ~35% of all COVID-19 patients experience neurological and neuropsychiatric symptoms, and a pre-existing diagnosis of Alzheimer's disease (AD) predicts the highest risk of COVID-19 yet identified, with the highest mortality among elderly AD patients. In the current study of multiple anatomical regions of AD brains compared to age-, post-mortem interval- and gender-matched controls (n = 10 regions, n = 32 brains), ACE2 expression was found to be significantly up-regulated in AD in the occipital lobe, temporal lobe neocortex and hippocampal CA1. The temporal lobe and hippocampus of the brain are also targeted by the inflammatory neuropathology that accompanies AD, suggesting a significant mechanistic overlap between COVID-19 and AD, strongly centered on invasion by the neurotropic SARS-CoV-2 virus via the increased presence of ACE2 receptors in limbic regions of the AD-affected brain.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".