Dexmedetomidine Pre-Treatment of Neonatal Rats Prevents Sevoflurane-Induced Deficits in Learning and Memory in the Adult Animals
Bibliographic record
Abstract
Anesthetics have been shown to cause cytotoxicity, cell death, affect neuronal growth and connectivity in animal models; however, their effects on learning and memory remain to be fully defined. Here, we examined the effects of the inhalation anesthetic sevoflurane (SEV)-both in vivo by examining learning and memory in freely behaving animals, and in vitro using cultured neurons to assess its impact on viability, mitochondrial structure, and function. We demonstrate here that neonatal exposure to sub-clinically used concentrations of SEV results in significant, albeit subtle and previously unreported, learning and memory deficits in adult animals. These deficits involve neuronal cell death, as observed in cell culture, and are likely mediated through perturbed mitochondrial structure and function. Parenthetically, both behavioural deficits and cell death were prevented when the animals and cultured neurons were pre-treated with the anesthetic adjuvant Dexmedetomidine (DEX). Taken together, our data provide direct evidence for sevoflurane-induced cytotoxic effects at the neuronal level while perturbing learning and memory at the behavioural level. In addition, our data underscore the importance of adjuvant agents such as DEX that could potentially counter the harmful effects of commonly used anesthetic agents for better clinical outcomes.
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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) | 0.001 | 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 | 0.002 |
| 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".