A Translational Paradigm for the Preclinical Evaluation of the Stroke Neuroprotectant Tat-NR2B9c in Gyrencephalic Nonhuman Primates
Bibliographic record
Abstract
Over decades, all attempts to translate acute stroke neuroprotectants from discovery in lower-order species to human clinical use have failed. This raised concerns about the predictive validity of preclinical studies in animals for outcomes in human stroke trials. To bridge this translational gap, we used high-order gyrencephalic nonhuman primates subjected to an experimental protocol that mimicked that of a corresponding, separately reported, clinical trial in which the human subjects underwent endovascular cerebral aneurysm repair. Both placebo-controlled studies tested neuroprotection by Tat-NR2B9c, a prospective therapeutic compound, in anesthetized subjects. Embolic strokes were produced by small intra-arterial emboli caused by the endovascular procedure. We show that primates treated with Tat-NR2B9c after the onset of embolic strokes exhibited significantly reduced numbers and volumes of strokes, as visualized by diffusion- and T2-weighted magnetic resonance imaging. These results correctly anticipated the outcome of the corresponding human trial, thus validating this study design as a predictor of neuroprotective efficacy in humans. This strategy may facilitate the evaluation of promising neuroprotectants before undertaking similar studies in human subjects.
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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 | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".