Glutamate Scavenging as a Neuroreparative Strategy in Ischemic Stroke
Bibliographic record
Abstract
Stroke is the second highest reason of death in the world and the leading cause of disability. The ischemic stroke makes up the majority of stroke cases that occur due to the blockage of blood vessels. Therapeutic applications for ischemic stroke include thrombolytic treatments that are in limited usage and only applicable to less than 10% of the total stroke patients, but there are promising new approaches. The main cause of ischemic neuronal death is glutamate excitotoxicity. There have been multiple studies focusing on neuroprotection via reduction of glutamate both in ischemic stroke and other neurodegenerative diseases that ultimately failed due to the obstacles in delivery. At that point, systemic glutamate grabbing, or scavenging is an ingenious way of decreasing glutamate levels upon ischemic stroke. The main advantage of this new therapeutic method is the scavengers working in the circulating blood so that there is no interference with the natural brain neurophysiology. In this review, we explain the molecular mechanisms of ischemic stroke, provide brief information about existing drugs and approaches, and present novel systemic glutamate scavenging methods. This review hopefully will elucidate the potential usage of the introduced therapeutic approaches 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".