Abstract 10708: Cannabidiol Loaded Nanoparticles Exhibit an Anti-Fibrotic Effect in a Mouse Model of Non-Ischemic Heart Failure
Bibliographic record
Abstract
Introduction: Cannabidiol (CBD) is being studied in a spectrum of ailments including cardiovascular disorders. It is also clinically prescribed for certain diseases. However, the hydrophobicity of CBD has been reported to contribute to low oral bioavailability. Here we use a polymeric nanoparticle formulation of CBD (nCBD) to overcome this limitation and evaluate its potential in heart failure (HF). Methods: HF was induced in 3-month old C57BL/6J mice with drinking water containing 1% NaCl and 0.3 mg/mL of L-NAME. After 1-week, subcutaneous osmotic pumps were implanted to deliver angiotensin-II at a rate of 0.7mg/kg/day for 4 weeks. nCBD was prepared using co-solvent evaporation of poly(ethylene oxide)-poly(α-benzyl carboxylate-ε-caprolactone) (PEO-b-PBCL) and pharmaceutical-grade CBD. nCBD (1 mg/kg) was injected 2 times per week subcutaneously in HF mice and Control mice. At week 5, Masson’s trichrome and H & E staining was performed to evaluate severity of fibrosis and cardiomyocyte (CM) size respectively using Cell Sense Dimension Software from Olympus. Results: PEO-b-PBCL nanoparticles localized in both interstitial and replacement fibrotic regions in the heart of our HF mice. HF mice with nCBD treatment displayed less fibrosis when compared to HF mice without treatment as shown in a representative image of fibrosis staining (Figure A). Histological quantification depicted that fibrosis area was higher in HF mice and was significantly (p<0.0001) reduced in HF mice exposed to nCBD (Figure B). Treatment of HF mice with nCBD also influenced CM size. Normal mice had a mean CM size of 12.33±2.07 μm. This increased to 21.17±2.88 μm in HF mice without treatment. However, nCBD treatment significantly (p<0.0001) decreased CM size to a mean value 14.76±2.71 μm (Figure C). Conclusion: Our results show that nCBD has an anti-fibrotic and anti-hypertrophic effect in a mouse model of non-ischemic heart failure.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".