A Semantic Similarity Analysis in the brains of Alzheimer's disease and COVID‐19 deceased individuals
Bibliographic record
Abstract
BACKGROUND: Neurological manifestations, such as impaired memory, reduced attention, and cognitive decline, are frequently reported in individuals with COVID-19 and resemble symptoms observed in Alzheimer's disease (AD). Nonetheless, the extent to which these two conditions share molecular patterns remains uncertain. This study investigates transcriptomic parallels in the frontal cortex of patients with COVID-19 and AD. METHOD: Transcriptomic datasets from the frontal cortex of COVID-19 and AD patients were retrieved from the Gene Expression Omnibus. We used the R statistical software to identify differentially expressed genes (DEGs) in COVID-19 and AD brains compared to controls (FDR-adjusted p -value <0.05). Functional enrichment analysis (FEA) was applied to explore biological processes enriched in the shared DEGs. RESULT: A total of 6,533 DEGs were identified in COVID-19 samples and 5,592 in AD samples. Among them, 2,789 DEGs were shared between both conditions. Functional clustering (Table 1) and semantic similarity of the overlapped genes revealed significant convergence in processes related to vesicle transport, exocytosis and regulated secretion (Figure 1). The predominant terms indicate active cytoskeleton-dependent intracellular trafficking, particularly involving the release of neurotransmitters, hormones, and signaling molecules. Additionally, processes involving calcium ion sequestration and membrane lipid distribution suggest integrated mechanisms of signaling and cellular homeostasis. CONCLUSION: These findings point to a functional overlap between the pathophysiological mechanisms of Alzheimer's disease and the effects of COVID-19 in the brain, highlighting vulnerabilities in intercellular communication and regulation of the cellular microenvironment, as an outcome of neuroinflammatory vulnerabilities. These findings could guide clinical approaches for monitoring and managing cognitive impairments in COVID-19 survivors who may be at increased risk of neurodegenerative diseases like AD.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".