Effects of sevoflurane and its metabolite hexafluoroisopropanol on hypoxia/reoxygenation-induced injury and mitochondrial bioenergetics in murine cardiomyocytes
Bibliographic record
Abstract
Background: The volatile anaesthetic sevoflurane protects cardiac tissue from reoxygenation/reperfusion. Mitochondria play an essential role in conditioning. We aimed to investigate how sevoflurane and its primary metabolite hexafluoroisopropanol (HFIP) affect necrosis, apoptosis, and reactive oxygen species formation in cardiomyocytes upon hypoxia/reoxygenation injury. Moreover, we aimed to describe the similarities in the mode of action in a mitochondrial bioenergetics analysis. Methods: for 2 h) in the presence or absence sevoflurane 2.2% or HFIP 4 mM. Lactate dehydrogenase (LDH) release (necrosis), caspase activation (apoptosis), reactive oxygen species, mitochondrial membrane potential, and mitochondrial function (Seahorse XF analyser) were measured. Results: <0.001). Reoxygenation in the presence of sevoflurane 2.2% or HFIP 4 mM increased LDH release only by +18% (+6 to +30%) and 20% (+7 to +32%), respectively. Apoptosis and reactive oxygen species formation were attenuated by sevoflurane and HFIP. Mitochondrial bioenergetics analysis of the two substances was profoundly different. Sevoflurane did not influence oxygen consumption rate (OCR) or extracellular acidification rate (ECAR), whereas HFIP reduced OCR and increased ECAR, an effect similar to oligomycin, an adenosine triphosphate (ATP) synthase inhibitor. When blocking the metabolism of sevoflurane into HFIP, protective effects of sevoflurane - but not of HFIP - on LDH release and caspase were mitigated. Conclusion: Together, our data suggest that sevoflurane metabolism into HFIP plays an essential role in cardiomyocyte postconditioning after hypoxia/reoxygenation injury.
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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) | 0.001 | 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".