Xenon-helium gas mixture at equimolar concentration of 37.5% protects against oxygen and glucose deprivation-induced injury and inhibits tissue plasminogen activator
Bibliographic record
Abstract
Xenon (Xe) is considered to be the golden standard neuroprotective gas. However, Xe has a higher molecular weight and lower thermal conductivity and specific heat than those of nitrogen, the main diluent of oxygen in air. These physical characteristics could impair or at least reduce the intrinsic neuroprotective action of Xe by increasing the patient's respiratory workload and body temperature. In contrast, helium (He) is a cost-efficient gas with a lower molecular weight and higher thermal conductivity and specific heat than those of nitrogen, but is far less potent than Xe. In this study, we hypothesized that mixing Xe and He could allow obtaining a neuroprotective gas mixture with advantageously reduced molecular weight and increased thermal conductivity. We found that Xe and He at the equimolar concentration of 37.5% reduced oxygen-glucose deprivation-induced increase in lactate dehydrogenase in brain slices, an ex vivo model of acute ischemic stroke. These results together with the effects of Xe-He on the thrombolytic efficiency of tissue plasminogen activator are discussed.
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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".