Anti‐inflammatory effects of cannabidiol against lipopolysaccharides in cardiac sodium channels
Bibliographic record
Abstract
Background Sepsis, caused by a dysregulated response to infections, can lead to cardiac arrhythmias. However, the mechanisms underlying sepsis‐induced inflammation, and how inflammation provokes cardiac arrhythmias, are not well understood. We hypothesized that cannabidiol (CBD) may ameliorate lipopolysaccharide (LPS)‐induced cardiotoxicity, via Toll‐like receptors (TLR4) and cardiac sodium channels (NaV1.5). Methods and results We incubated human immune cells (THP‐1 macrophages) with LPS for 24 h, then extracted the THP‐1 incubation media. ELISA assays showed that LPS (1 or 5 μg·ml−1), in a concentration‐dependent manner, or MPLA (TLR4 agonist, 5 μg·ml−1) stimulated the THP‐1 cells to release inflammatory cytokines (TNF‐α and IL‐6). Prior incubation (4 h) with CBD (5 μM) or C34 (TLR4 antagonist: 5 μg·ml−1) inhibited LPS and MPLA‐induced release of both IL‐6 and TNF‐α. Human‐induced pluripotent stem cell‐derived cardiomyocytes (hiPSC‐CM) were subsequently incubated for 24 h in the media extracted from THP‐1 cells incubated with LPS, MPLA alone, or in combination with CBD or C34. Voltage‐clamp experiments showed a right shift in the voltage dependence of NaV1.5 activation, steady state fast inactivation (SSFI), increased persistent current and prolonged in silico action potential duration in hiSPC‐CMs incubated in the LPS or MPLA‐THP‐1 media. Co‐incubation with CBD or C34 rescued the biophysical dysfunction caused by LPS and MPLA. Conclusion Our results suggest that CBD may protect against sepsis‐induced inflammation and subsequent arrhythmias through (i) inhibition of the release of inflammatory cytokines, antioxidant and anti‐apoptotic effects and/or (ii) a direct effect on NaV1.5.
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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".