Reduced excitatory neurotransmission in the hippocampus after inflammation and sevoflurane anaesthesia
Bibliographic record
Abstract
Background: Inflammation and general anaesthesia likely contribute to perioperative neurocognitive disorders, possibly by causing a neuronal imbalance of excitation and inhibition. We showed previously that treatment with lipopolysaccharide (LPS) and sevoflurane causes a sustained increase in a tonic inhibitory conductance in the hippocampus; however, whether excitatory neurotransmission is also altered remains unknown. The goal of this study was to examine excitatory synaptic currents in the hippocampus after treatment with LPS and sevoflurane. Synaptic plasticity in the hippocampus, a cellular correlate of learning and memory, was also studied. Methods: i.p.), and after 24 h they were then exposed to vehicle or sevoflurane (2.3%; 2 h). Hippocampal slices were prepared 48 h later. Excitatory synaptic currents were recorded from pyramidal neurones. Long-term potentiation (LTP) and long-term depression (LTD) were studied in the Schaffer collateral-cornu ammonis 1 pathway. Results: <0.001), whereas that of spontaneous EPSCs was unaltered, as evidenced by cumulative distribution plots. The frequency, area, and kinetics of both miniature and spontaneous EPSCs were unchanged, as were LTP and LTD. Conclusions: The reduced amplitude of miniature EPSCs, coupled with the previously reported increase in tonic inhibition, indicates that the combination of LPS and sevoflurane markedly disrupts the balance of excitation and inhibition. Restoring this balance by pharmacologically enhancing excitatory neurotransmission and inhibiting the tonic current may represent an effective therapeutic option for perioperative neurocognitive disorders.
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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) | 0.001 | 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".