High-CBD cannabis extracts inhibit the expression of proinflammatory factors via miRNA-mediated silencing in human small intestinal epithelial cells
Bibliographic record
Abstract
The incidence of chronic inflammatory disorders and autoimmune diseases is rapidly growing. To date, the COVID-19 pandemic caused by SARS-CoV-2 has killed over 6,209,000 people globally, while no drug has been proven effective for the disease. Screening natural anti-inflammatory compounds for clinical application has drawn much attention. In this study, we showed that high-CBD cannabis extracts #1, #5, #7, #169, and #317 suppressed the levels of expression of proinflammatory cyclooxygenase 2 (COX2) and increased the expression of the anti-inflammatory suppressor of cytokine signaling 3 (SOCS3) in human small intestinal epithelial cells (HSIEC) in TNFα/IFNγ-triggered inflammation. We revealed that these extracts, with the exception of extract #169, also profoundly attenuated induction of proinflammatory cytokines interleukin-6 (IL-6) and/or IL-8 proteins through miR-760- and miR-302c-3p-mediated silencing. The prevalent components in extracts #1 and #7 influenced the levels of IL-8 both individually as well as in combination with each other. However, the high-dose cannabis extracts displayed an inhibitory effect in the growth of HSIEC cells. These results show that our high-CBD cannabis extracts decrease the levels of proinflammatory molecules COX2, IL-6, and IL-8 via transcriptional suppression or miRNA-mediated silencing, highlighting their potential against COVID-19-associated cytokine storm syndrome.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.001 | 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 teacher head, 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".