Alzheimer’s Disease and COVID-19 Pathogenic Overlap: Implications for Drug Repurposing
Bibliographic record
Abstract
As COVID-19 continues, a safe, cost-effective treatment strategy demands continued inquiry. Chronic neuroinflammatory disorders may appear to be of little relevance in this regard; often indolent and progressive disorders characterized by neuroinflammation (such as Alzheimer's disease (AD)) are fundamentally dissimilar in etiology and symptomology to COVID-19's rapid infectivity and pathology. However, the two disorders share extensive pathognomonic features, including at membrane, cytoplasmic, and extracellular levels, culminating in analogous immunogenic destruction of their respective organ parenchyma. We hypothesize that these mechanistic similarities may extent to therapeutic targets, namely that it is conceivable an agent against AD's immunopathy may have efficacy against COVID-19 and vice versa. It is notable that while extensively investigated, no agent has yet demonstrated significant therapeutic efficacy against AD's cognitive and memory declines. Yet this very failure has driven the development of numerous agents with strong mechanistic potential and clinical characteristics. Having already approved for clinical trials, these agents may be an expedient starting point in the urgent search for an effective COVID-19 therapy. Herein, we review the overlapping Alzheimer's/ COVID-19 targets and theorize several initial platforms.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".