Interactions between Nitrous Oxide and Tissue Plasminogen Activator in a Rat Model of Thromboembolic Stroke
Bibliographic record
Abstract
BACKGROUND: Preclinical evidence in rodents has suggested that inert gases, such as xenon or nitrous oxide, may be promising neuroprotective agents for treating acute ischemic stroke. This has led to many thinking that clinical trials could be initiated in the near future. However, a recent study has shown that xenon interacts with tissue-type plasminogen activator (tPA), a well-recognized approved therapy of acute ischemic stroke. Although intraischemic xenon inhibits tPA-induced thrombolysis and subsequent reduction of brain damage, postischemic xenon virtually suppresses both ischemic brain damage and tPA-induced brain hemorrhages and disruption of the blood-brain barrier. The authors investigated whether nitrous oxide could also interact with tPA. METHODS: The authors performed molecular modeling of nitrous oxide binding on tPA, characterized the concentration-dependent effects of nitrous oxide on tPA enzymatic and thrombolytic activity in vitro, and investigated the effects of intraischemic and postischemic nitrous oxide in a rat model of thromboembolic acute ischemic stroke. RESULTS: The authors demonstrate nitrous oxide is a tPA inhibitor, intraischemic nitrous oxide dose-dependently inhibits tPA-induced thrombolysis and subsequent reduction of ischemic brain damage, and postischemic nitrous oxide reduces ischemic brain damage, but in contrast with xenon, it increases brain hemorrhages and disruption of the blood-brain barrier. CONCLUSIONS: In contrast with previous studies using mechanical acute stroke models, these data obtained in a clinically relevant rat model of thromboembolic stroke indicate that nitrous oxide should not be considered a good candidate agent for treating acute ischemic stroke compared with xenon.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".