Modulation by the Noble Gas Helium of Tissue Plasminogen Activator: Effects in a Rat Model of Thromboembolic Stroke*
Bibliographic record
Abstract
INTERVENTIONS: Helium has been shown to provide neuroprotection in mechanical model of acute ischemic stroke by inducing hypothermia, a condition shown by itself to reduce the thrombolytic and proteolytic properties of tissue plasminogen activator. However, whether or not helium interacts with the thrombolytic drug tissue plasminogen activator, the only approved therapy of acute ischemic stroke still remains unknown. This point is not trivial since previous data have shown the critical importance of the time at which the neuroprotective noble gases xenon and argon should be administered, during or after ischemia, in order not to block tissue plasminogen activator-induced thrombolysis and to obtain neuroprotection and inhibition of tissue plasminogen activator-induced brain hemorrhages. MEASUREMENTS AND MAIN RESULTS: We show that helium of 25-75 vol% inhibits in a concentration-dependent fashion the catalytic and thrombolytic activity of tissue plasminogen activator in vitro and ex vivo. In vivo, in rats subjected to thromboembolic brain ischemia, we found that intraischemic helium at 75 vol% inhibits tissue plasminogen activator-induced thrombolysis and subsequent reduction of ischemic brain damage and that postischemic helium at 75 vol% reduces ischemic brain damage and brain hemorrhages. CONCLUSIONS: In a clinical perspective for the treatment of acute ischemic stroke, these data suggest that helium 1) should not be administered before or together with tissue plasminogen activator therapy due to the risk of inhibiting the benefit of tissue plasminogen activator-induced thrombolysis; and 2) could be an efficient neuroprotective agent if given after tissue plasminogen activator-induced reperfusion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".