The overlooked aspect of excitotoxicity: Glutamate‐independent excitotoxicity in traumatic brain injuries
Bibliographic record
Abstract
Traumatic brain injury (TBI) is a leading major cause of morbidity and mortality in youth and individuals under 45 year age. A wide variety of cellular and molecular mechanisms have been identified contributing to the pathogenesis of TBI. A better understanding of the pathophysiology behind TBI is essential for providing more effective treatment. Excitotoxicity as one of the secondary molecular events is a major contributing factor in apoptosis and neuronal death following the initial injury in TBI. Excitotoxicity is the rapid overload and influx of calcium into the cell cytoplasm, activating a series of deleterious signaling cascades causing the cell to undergo apoptosis. Conventional understanding is that the rapid influx of calcium is initiated through glutamate release. However, there are overlooked glutamate-independent mechanisms that cause the rapid calcium influx into the neuronal cytoplasm, evoking or contributing to excitotoxicity. Therefore, the focus of this review will be on the role of the glutamate-independent excitotoxic mechanisms of the mechanosensitive response of NMDA receptors, mechanoporation of the cell membrane, ischemia, and the release of calcium from intracellular stores. In conclusion, the shear and stretch forces during a TBI event may result in the mechanosensitive activation of NMDA receptors which contribute to glutamate-independent excitotoxicity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".