Protein kinases mediate anti-inflammatory effects of cannabidiol and estradiol against high glucose in cardiac sodium channels
Bibliographic record
Abstract
Background and purpose. Cardiovascular anomalies are predisposing factors for diabetes-induced morbidity and mortality. Recently, we showed that high glucose induces changes in the biophysical properties of Nav1.5 that could be strongly correlated to diabetes-induced arrhythmia. However, the mechanisms underlying hyperglycemia-induced inflammation, and how inflammation provokes cardiac arrhythmia, are not well understood. We hypothesized that inflammation could mediate the high glucose-induced biophyscial changes on Nav1.5 through protein phosphorylation by protein kinases A and C. We also hypothesized that this signaling pathway is, at least partly, involved in the cardiprotective effects of CBD and E2. Experimental approach. To test these ideas, we used Chinese hamster ovarian (CHO) cells transiently co-transfected with cDNA encoding human Nav1.5 α-subunit under control, a cocktail of inflammatory mediators or 100 mM glucose conditions (for 24 hours). We used electrophysiological experiments and action potential modelling. Key Results. Inflammatory mediators, similar to 100 mM glucose, right shifted the voltage dependence of conductance and steady state fast inactivation and increased persistent current leading to computational prolongation of action potential (hyperexcitability) which could result in long QT3 arrhythmia. In addition, activators of PK-A or PK-C replicated the inflammation-induced gating changes of Nav1.5. Inhibitors of PK-A or PK-C, CBD or E2 mitigated all the potentially deleterious effects provoked by high glucose/inflammation. Conclusions and implications. These findings suggest that PK-A and PK-C may mediate the anti-inflammatory effects of CBD and E2 against high glucose-induced arrhythmia. CBD, via Nav1.5, may be a cardioprotective therapeutic approach in diabetic postmenopausal population.
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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 | 0.000 |
| 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".