New AKT-dependent mechanisms of anti-COVID-19 action of high-CBD Cannabis sativa extracts
Bibliographic record
Abstract
COVID-19 is caused by the SARS-CoV-2 virus, which enters target cells via interactions with ACE2 and TMPRSS2. Here, we show AKT serine/threonine kinase-dependent epigenetic control of ACE2 and TMPRSS2 expression by high-cannabidiol (CBD) cannabis extracts and their individual components. CBD alone and extracts #1, #5, #7, and #129 downregulated ACE2 and TMPRSS2 in lung fibroblast WI-38 cells through AKT-mediated inhibition. miR-200c-3p and let-7a-5p were two contributing miRNAs in CBD-mediated suppression of ACE2 and TMPRSS2. CBD and terpene PTWT2.2 profoundly inhibited ACE2 and TMPRSS2 expression, both individually and in combination. Extracts #1, #5, #7, and #169 suppressed COX2 expression and remarkably attenuated TNFα/IFNγ-triggered induction of proinflammatory factors IL-6 and IL-8 by AKT pathway. The most abundant molecules present in extracts #1 and #7 modulated the expression of COX2, IL-6, and IL-8 both individually and in combination. These results reveal that high-CBD cannabis extracts attenuated ACE2 and TMPRSS2 expression and the induction of inflammatory mediators COX2, IL-6, and IL-8 via the AKT pathway, highlighting their potential anti-COVID-19 features.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".