Effects of Nerinetide on Behavioural Recovery of Experimental Stroke in the Rat
Bibliographic record
Abstract
Stroke is the leading cause of long-term disability in Canada and a significant financial burden on the healthcare system, yet no pharmaceutical intervention is available that has been proven to improve recovery. N-methyl-D- aspartate receptors (NMDARs) are known to play a substantive role in neural circuit rebuilding post-injury. Studies from acute stroke research suggested that interfering with the toxic GluN2B-subunit of NMDAR downstream signalling or enhancing the pro-survival GluN2A-subunit one is beneficial in promoting neuroprotection. Nerinetide has been shown to promote neuroprotection during acute ischemia, by interfering with nitric oxide (NO) production and the pro-death signalling cascade mediated by the GluN2B-subunit of the NMDARs. The purpose of this dissertation was to evaluate the role of nerinetide in promoting functional recovery and molecular changes when administered during the recovery phase after stroke. In this thesis, the effect of nerinetide in promoting functional recovery was tested in two different stroke models in rats. Results show that nerinetide promoted functional recovery only after cortical motor injury and was correlated with the upregulation of selected key pro-survival proteins (GluN2A, PSD-95, PI3K, AKT, ERK1/2, CREB, and S6). In particular, CREB has been found phosphorylated at 6 and 24 hours following the first dose of nerinetide in the cortical peri-infarct region and it is believed to play a key role in promoting functional recovery. This thesis has contributed to the field of stroke research by applying nerinetide for the first time in a recovery setting and by highlighting a set of protein candidates that may have contributed to these recovery mechanisms. Future studies should investigate the role of CREB in connection with recovery mechanisms post-stroke and enhancement by the application of specific pharmacotherapies.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".