Characterization of a novel multifunctional resveratrol derivative for the treatment of atrial fibrillation
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Atrial fibrillation (AF) is the most common cardiac arrhythmia and is associated with an increased risk for stroke, heart failure and cardiovascular-related mortality. Candidate targets for anti-AF drugs include a potassium channel K(v)1.5, and the ionic currents I(KACh) and late I(Na), along with increased oxidative stress and activation of NFAT-mediated gene transcription. As pharmacological management of AF is currently suboptimal, we have designed and characterized a multifunctional small molecule, compound 1 (C1), to target these ion channels and pathways. EXPERIMENTAL APPROACH: We made whole-cell patch-clamp recordings of recombinant ion channels, human atrial I(Kur), rat atrial I(KACh), cellular recordings of contractility and calcium transient measurements in tsA201 cells, human atrial samples and rat myocytes. We also used a model of inducible AF in dogs. KEY RESULTS: C1 inhibited human peak and late K(v)1.5 currents, frequency-dependently, with IC₅₀ of 0.36 and 0.11 μmol·L(-1) respectively. C1 inhibited I(KACh)(IC₅₀ of 1.9 μmol·L(-1)) and the Na(v)1.5 sodium channel current (IC₅₀s of 3 and 1 μmol·L(-1) for peak and late components respectively). C1 (1 μmol·L(-1)) significantly delayed contractile and calcium dysfunction in rat ventricular myocytes treated with 3 nmol·L(-1) sea anemone toxin (ATX-II). C1 weakly inhibited the hERG channel and maintained antioxidant and NFAT-inhibitory properties comparable to the parent molecule, resveratrol. In a model of inducible AF in conscious dogs, C1 (1 mg·kg(-1)) reduced the average and total AF duration. CONCLUSION AND IMPLICATIONS: C1 behaved as a promising multifunctional small molecule targeting a number of key pathways involved in AF.
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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".