B‐type Natriuretic Peptide and EET Mediated Cardioprotection
Bibliographic record
Abstract
B‐type natriuretic peptide (BNP) can attenuate ischemic‐reperfusion injury through natriuretic peptide receptor type‐A (NPR‐A). We investigated the role of BNP in epoxyeicosatrienoic acids(EET) mediated cardioprotection. Hearts from sEH null (KO), littermate controls(WT) and C57 mice were perfused in a Langendorff apparatus and subjected to 20/30min of ischemia followed by 40min of reperfusion. RT‐PCR analysis for preproBNP (Nppb) mRNA expression was performed in both naïve and isolated perfused hearts. Relative to naïve hearts, KO mice had a significant increase in Nppb mRNA expression following both preischemic perfusion (KO 5‐fold; WT 2‐fold) and postischemic reperfusion (KO 19‐fold; WT 7‐fold), correlating with improved postischemic left ventricular (LV) function (KO 52±3; WT 23.1±3%). K ATP channel blocker, glibenclamide(1μM), and an EET antagonist, 14,15‐EEZE (1μM), significantly reduced the expression of mRNA in KO hearts compared to vehicle controls. Addition of exogenous BNP or EETs resulted in significant increases in LV functional recovery (BNP 59.2±13, EET 54.4±21, control 23.1±3%). BNP‐mediated protection was abolished with co‐administration of 14,15‐EEZE or A71915 (NPR‐A antagonist) (14,15‐EEZE 18.3±10, A71915 39.7±19%). However, EET‐mediated effects were not affected by A71915 (48.5±25%). Our results suggest a correlation between BNP and EET‐mediated cardioprotection.
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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 | 0.000 | 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) | 0.002 | 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".