Triacsin C Suppresses Non‐Esterified Fatty Acids (NEFA) and Increases Nitric Oxide (NO) Synthesis
Bibliographic record
Abstract
Endothelial dysfunction is characterized by decreased vascular NO availability. Elevated NEFA decreases eNOS activity. In cultured cells, we found that fatty acyl CoA synthase (FACS) inhibitor Triacsin C (TC) interrupted eNOS palmitoylation, increasing eNOS activity but not changing vascular‐active eicosanoids. Hypothesis: TC mitigates endothelial dysfunction by increasing NO and decreasing NEFA. In this study, intravascular NO synthesis was measured by electrochemistry in the ischemic hind limb with heparinized rat model in a time course design. The post‐ischemic NO was significantly elevated in TC (100 μg/kg)‐treated animals than controls. NOS inhibitor treatments in this model implicate a role of eNOS, but not iNOS. Acute hyperlipidemia was induced by a bolus dose of Intralipid ® (1 ml) to increase NEFA. Pre‐treatment with TC had no effect on total plasma lipids or triglycerides, either before or after Intralipid ® . TC reduced baseline plasma NEFA from 240 ± 26.8 to 147 ± 14.3 μg/ml (p=0.038). Fifteen minutes after Intralipid ® , NEFA rose to 1461 ± 130 μg/ml (control), which was blunted by TC to 393 ± 2.55 μg/ml (p=0.0079). By 85 min., the difference had subsided (194 ± 10.2 control vs . 281 ± 74.8 μg/ml TC; p=0.74). The TC effect was not uniform across all fatty acid species measured. These data show that TC increases post‐ischemic eNOS activity and blunted plasma NEFA induced by acute hyperlipidemia.
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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) | 0.001 | 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) | 0.001 | 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".