Cannabidiol Suppresses Cytokine Storm and Protects Against Cardiac and Renal Injury Associated with Sepsis
Bibliographic record
Abstract
Background: Cytokine release syndrome, also termed “cytokine storm,” is the leading cause of morbidity and mortality among patients with various conditions such as sepsis. While cytokine storm is associated with multiple organ damage, acute cardiac and renal injury represents a hallmark of cytokine storm. Since recent reports have suggested that cannabidiol (CBD) may assist in the treatment of inflammatory diseases, our objective was to examine the effect of CBD on cytokine storm-induced cardiac and renal injury using the lipopolysaccharide (LPS)-induced sepsis mouse model. Materials and Methods: At 8 weeks of age, mice were randomly assigned to receive CBD (15 mg/kg) or vehicle one hour before a single injection of either phosphate-buffered saline or LPS (10 mg/kg) for an additional 24 h. Results: Our results show that CBD improves cardiac function and reduces renal injury in a mouse model of cytokine storm. Moreover, our data indicate that CBD significantly reduces systemic and renal inflammation to contribute to the improvements observed in a cytokine storm-model of cardiac and renal injury. Conclusions: Overall, the findings of this study suggest that CBD could be repurposed to reduce morbidity in patients with cytokine storm particularly in severe infections such as sepsis.
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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".