Argon neuroprotection in a non-human primate model of transient endovascular ischemic stroke
Bibliographic record
Abstract
ABSTRACT Background Previous studies have demonstrated the efficacy of argon neuroprotection in rodent models of cerebral ischemia. The objective of the present study was to confirm a potential neuroprotective effect of argon in a non-human primate model of endovascular ischemic stroke as an essential step before considering the use of argon as a neuroprotective agent in humans. Methods Thirteen adult monkeys ( Macaca mulatta ) were allocated to two groups: a control group (n=8) without neuroprotection and an argon group (n=5) in which argon inhalation (90 min) was initiated 30 minutes after onset of ischemia. Animals in both groups underwent brain MRI (pre-ischemic) at least 7 days before the intervention. The monkeys were subjected to focal cerebral ischemia induced by a transient (90 min) middle cerebral artery occlusion (tMCAO). After tMCAO, MRI was performed 1 hour after cerebral reperfusion. The ischemic core volume was defined by the apparent diffusion coefficient (aDC) and edema in fluid attenuated inversion recovery (FLAIR) acquisitions. MRI masks were applied to distinguish between cortical and subcortical abnormalities. In addition, a modified version of the Rankin scale was used to neurologically assess post-tMCAO. Results Despite variability in the ischemic core and edema volumes in the control group, argon significantly reduced ischemic core volume after ischemia compared to the control group (1.1±1.6 cm 3 vs. 8.5±8.1 cm 3 ; p =0.03). This effect was limited to cortical structures (0.6±1.1 cm 3 vs. 7.4±7.2 cm 3 ; p =0.03). No significant differences were observed in the edema volumes. Measures of neurological clinical outcome suggested a better prognosis in argon-treated animals. Conclusions In the tMCAO macaque model, argon induced effective neuroprotective effects, leading to a reduced ischemic core in cortical areas. These results support the potential use of this therapeutic approach for future clinical studies in stroke patients.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".