Effect of cannabidiol on apoptosis and cellular interferon and interferon-stimulated gene responses to the SARS-CoV-2 genes ORF8, ORF10 and M protein
Bibliographic record
Abstract
AIMS: To study effects on cellular innate immune responses to ORF8, ORF10, and Membrane protein (M protein) from the Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that causes COVID-19, in combination with cannabidiol (CBD). MAIN METHODS: HEK293 cells transfected with plasmids expressing control vector, ORF8, ORF10, or M protein were assayed for cell number and markers of apoptosis at 24 h, and interferon and interferon-stimulated gene expression at 14 h, with or without CBD. Cells transfected with polyinosinic:polycytidylic acid (Poly (I:C)) were also studied as a general model of RNA-type viral infection. KEY FINDINGS: Reduced cell number and increased early and late apoptosis were found when expression of viral genes was combined with 1-2 μM CBD treatment, but not in control-transfected cells treated with CBD, or in cells expressing viral genes but treated only with vehicle. In cells expressing viral genes, CBD augmented expression of IFNγ, IFNλ1 and IFNλ2/3, as well as the 2'-5'-oligoadenylate synthetase (OAS) family members OAS1, OAS2, OAS3, and OASL. CBD also augmented expression of these genes in control cells not expressing viral genes, but without enhancing apoptosis. CBD similarly enhanced the cellular anti-viral response to Poly (I:C). SIGNIFICANCE: Our results demonstrate a poor ability of HEK293 cells to respond to SARS-CoV-2 genes alone, but an augmented innate anti-viral response to these genes in the presence of CBD. Thus, CBD may prime components of the innate immune system, increasing readiness to respond to RNA-type viral infection without activating apoptosis, and could be studied for potential in prophylaxis.
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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 | 0.000 | 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".