Remote myocardial injury: the protective role of fluoxetine
Bibliographic record
Abstract
Aortic cross-clamping-induced ischemia-reperfusion (IR) is an important factor in the development of postoperative acute cardiac injury following abdominal aortic surgery. We investigated the possible anti-oxidant/anti-inflammatory effects of fluoxetine (FLX), which is used widely as a preoperative anxiolytic on cardiac injury induced by IR of the infrarenal abdominal aorta. FLX was administered to IR-performed (60 min of ischemia and 120 min of reperfusion) rats for 3 days, once daily at 20 mg/kg i.p. dosage. Results were compared to control and non-FLX-treated IR-performed rats. Serum creatine kinase (CK) and CK-MB levels, lipid hydroperoxide, thiobarbituric acid reactive substances, and pro-oxidant/anti-oxidant balance levels in the IR group were significantly higher whereas superoxide dismutase activity, glutathione, and ferric reducing/anti-oxidant power levels were lower than for the control. IR also increased myeloperoxidase activity, tumor necrosis factor-α, interleukin-1β, and interleukin-6 and decreased interleukin-10 levels. FLX decreased CK, CK-MB, lipid hydroperoxide, thiobarbituric acid reactive substances, and pro-oxidant/anti-oxidant balance levels while increasing superoxide dismutase activity, glutathione, and ferric reducing/anti-oxidant power levels. FLX also decreased myeloperoxidase activity, tumor necrosis factor-α, interleukin-1β, and interleukin-6 levels and increased interleukin-10 levels compared to IR. FLX attenuated the morphological changes associated with cardiac injury. Our study clearly demonstrates that FLX confers protection against aortic IR-induced cardiac injury, tissue leucocyte infiltration, and cellular integrity via its anti-oxidant/anti-inflammatory effects.
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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".