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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".