Increased arachidonic acid‐induced thromboxane generation impairs skeletal muscle arteriolar dilation with genetic dyslipidemia
Bibliographic record
Abstract
This study determined if arachidonic acid (AA)‐induced skeletal muscle arteriolar dilation is altered with hypercholesterolemia in ApoE and LDLR gene deletion mice fed normal diet compared to control C57/Bl/6J (C57) mice, and potential contributing mechanisms underlying demonstrated differences. Gracilis muscle arterioles were isolated, with mechanical responses assessed following challenge with AA under control conditions and after elements of AA metabolism pathways were inhibited. Conduit arteries from each strain were used to assess AA‐induced production of PGI 2 and TxA 2 . Arterioles from ApoE and LDLR exhibited a blunted dilation to AA versus C57. While responses were cyclooxygenase‐dependent in all strains, inhibition of thromboxane synthase or blockade of PGH 2 /TxA 2 receptors improved dilation in ApoE and LDLR only. AA‐induced generation of PGI 2 was comparable across strains, although TxA 2 generation was increased in ApoE and LDLR. Arteriolar reactivity to PGI 2 and TxA 2 was comparable across strains. Treatment with TEMPOL improved dilation and reduced TxA 2 production with AA in ApoE and LDLR. These results suggest that AA‐induced arteriolar dilation is constrained in ApoE and LDLR via an increased production of TxA 2 . While partially due to elevated oxidant stress, additional mechanisms contribute which are independent of acute alterations in oxidant tone. (NIH R01 DK64668, AHA EIA 0740129N)
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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) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".