Abstract 25: Promoting Recovery After Stroke by Manipulating NMDA Receptor-Associated Signalling in the Early Post Stroke Phase
Bibliographic record
Abstract
Introduction: According to the World Health Organization (WHO), stroke remains a leading cause of mortality and neurological disability globally. To date, there is no effective pharmacological treatment to promote recovery from stroke. Promoting regenerative repair may improve recovery and it is known that N-methyl-D- aspartate receptors (NMDARs) play important roles in neural circuit rebuilding. Therefore, an appealing strategy to promote stroke recovery is to selectively inhibit cell death mechanisms while promoting pro-survival processes mediated by NMDARs. A promising agent for this purpose is Tat-NR2B9c (NA-1), a clinical-stage agent being tested in Phase 3 trials for acute ischemic stroke. Tat-NR2B9c promotes the activation of pro-survival signaling and is a potent neuroprotector against excitotoxicity. Here we sought to determine whether treatment with Tat-NR2B9c promotes functional recovery in rats subjected to ischemic brain damage during the subacute recovery phase. Methods: Stroke was induced by Pial Vessel Disruption (PVD) in Sprague Dawley rats. Permanent devascularisation was induced by removing all the pia and the attached blood vessels of the left motor cortex. Experimental groups were: PVD + Tat-NR2B9c (n = 12); PVD + Vehicle (n = 11); Sham + Tat-NR2B9c (n = 8); Sham + Vehicle (n = 8). Tat-NR2B9c was injected intravenously (IV) beginning 3 days after stroke and was administered daily for 7 days. A behavioral battery of tests followed, composed of neurological score, cylinder test, horizontal ladder and adhesive removal tests. Brains were stained with H&E and the infarct size was analyzed by ImageJ. Results: Repeated IV injections of Tat-NR2B9c led to a significant improvement in all motor tasks: decreased number in foot slips by 3.6-fold in horizontal ladder (P < 0.0001); improved neurological score by 4.7-fold compared to control (P < 0.0001); improved use of affected limb by 58% in cylinder test (P = 0.0006); reduced adhesive removal test time by 81% (P < 0.0001). There was no difference in infarct sizes between the groups. Conclusion: Treatment with Tat-NR2B9c post stroke improved functional outcome in rats subjected to PVD. Current studies are ongoing to assess the mechanism of action underlying the observed recovery.
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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 | 0.000 |
| 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) | 0.005 | 0.001 |
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".