Phenylephrine postconditioning increases myocardial injury: Are alpha-1 sympathomimetic agonist cardioprotective?
Bibliographic record
Abstract
OBJECTIVE: We studied effects of phenylephrine (PHE) on postischemic functional recovery and myocardial injury in an ischemia-reperfusion (I-R) experimental model. MATERIALS AND METHODS: Rat hearts were Langendorff-perfused and subjected to 30 min zero-flow ischemia (I) and 60 min reperfusion (R). During R PHE was added at doses of 1 μM (n = 10) and 50 μM (n = 12). Hearts (n = 14) subjected to 30 and 60 min of I-R served as controls. Contractile function was assessed by left ventricular developed pressure (LVDP) and the rate of increase and decrease of LVDP; apoptosis by fluorescent imaging targeting activated caspase-3, while myocardial injury by lactate dehydrogenase (LDH) released during R. Activation of kinases was measured at 5, 15, and 60 min of R using western blotting. RESULTS: PHE did not improve postischemic contractile function. PHE increased LDH release (IU/g); 102 ± 10.4 (Mean ± standard error of mean) control versus 148 ± 14.8 PHE (1), and 145.3 ± 11 PHE (50) hearts, (P < 0.05). PHE markedly increased apoptosis. Molecular analysis showed no effect of PHE on the activation of proapoptotic c-Jun N-terminal kinase signaling; a differential pattern of p38 mitogen activated protein kinase (MAPK) activation was found depending on the PHE dose used. With 1 μM PHE, p-p38/total-p38 MAPK levels at R were markedly increased, indicating its detrimental effect. With PHE 50 μM, no further changes in p38 MAPK were seen. Activation of Akt kinase was decreased implying involvement of different mechanisms in this response. CONCLUSIONS: PHE administration during reperfusion does not improve postischemic recovery due to exacerbation of myocardial necrosis and apoptosis. This finding may be of clinical and therapeutic relevance.
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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 | 0.001 |
| 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".