Excitotoxicity and stroke: Identifying novel targets for neuroprotection
Bibliographic record
Abstract
Excitotoxicity, the specific type of neurotoxicity mediated by glutamate, may be the missing link between ischemia and neuronal death, and intervening the mechanistic steps that lead to excitotoxicity can prevent stroke damage. Interest in excitotoxicity began fifty years ago when monosodium glutamate was found to be neurotoxic. Evidence soon demonstrated that glutamate is not only the primary excitatory neurotransmitter in the adult brain, but also a critical transmitter for signaling neurons to degenerate following stroke. The finding led to a number of clinical trials that tested inhibitors of excitotoxicity in stroke patients. Glutamate exerts its function in large by activating the calcium-permeable ionotropic NMDA receptor (NMDAR), and different subpopulations of the NMDAR may generate different functional outputs, depending on the signaling proteins directly bound or indirectly coupled to its large cytoplasmic tail. Synaptic activity activates the GluN2A subunit-containing NMDAR, leading to activation of the pro-survival signaling proteins Akt, ERK, and CREB. During a brief episode of ischemia, the extracellular glutamate concentration rises abruptly, and stimulation of the GluN2B-containing NMDAR in the extrasynaptic sites triggers excitotoxic neuronal death via PTEN, cdk5, and DAPK1, which are directly bound to the NMDAR, nNOS, which is indirectly coupled to the NMDAR via PSD95, and calpain, p25, STEP, p38, JNK, and SREBP1, which are further downstream. This review aims to provide a comprehensive summary of the literature on excitotoxicity and our perspectives on how the new generation of excitotoxicity inhibitors may succeed despite the failure of the previous generation of drugs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".