A synthetic peptide rescues rat cortical neurons from anesthetic-induced cell death and modulation of growth and synaptic assembly
Bibliographic record
Abstract
Abstract Anesthetics, although necessary for many modern procedures, exert neurotoxic effects in various experimental models; the underlying mechanisms, however, remain unknown. In the absence of this information, any in vivo mitigation strategy would be challenging. To help address these gaps, we sought to determine whether preserving mitochondrial network integrity with a non-toxic, short-life synthetic peptide, P110, could protect cortical neurons against both inhalational and intravenous anesthetic-induced neurotoxicity. This study provides the first direct comparative account of three key general anesthetics (desflurane, propofol, and ketamine) under identical conditions and demonstrates their impact on cellular viability, neurite outgrowth, and synaptic network assembly on neonatal rat cortical neurons. Further, we discovered that the inhibition of Fis1 receptor-mediated fission reverses anesthetic-induced aberrations in an agent-specific manner. Our data thus underscore the importance of a reductionist approach, offering unique opportunities to explore non-toxic, mitochondria-based therapeutic tools for mitigating anesthetic-induced harm to the developing brain.
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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".