A Novel and Translational Rat Model of Concussion Combining Force and Rotation with In Vivo Cerebral Microdialysis
Bibliographic record
Abstract
Persistent cognitive and motor symptoms are known consequences of concussions/mild traumatic brain injury (mTBIs) that can be partly attributable to altered neurotransmission. Indeed, cerebral microdialysis studies in rodents have demonstrated an excessive extracellular glutamate release in the hippocampus within the first 10 min following trauma. Microdialysis offers the clear advantage of in vivo neurotransmitter continuous sampling while not having to sacrifice the animal. In addition to the aforementioned technique, a closed head injury model that exerts rapid acceleration and deceleration of the head and torso is needed, as such a factor is not available in many other animal models. The Wayne State weight-drop model mimics this essential component of human craniocerebral trauma, allowing the induction of an impact on the head of an unrestrained rodent with a falling weight. Our novel and translational rat model combines cerebral microdialysis with the Wayne State weight-drop model to study, in lightly anesthetized and unrestrained adult rats, the acute changes in extracellular neurotransmitter levels following concussion. In this protocol, the microdialysis probe was inserted inside the hippocampus as region of interest, and was left inserted in the brain at impact. There is a high density of terminals and receptors in the hippocampus, making it a relevant region to document altered neurotransmission following concussion. When applied to adult Sprague-Dawley rats, our combined model induced increases in hippocampal extracellular glutamate concentrations within the first 10 min, consistent with the previously reported post-concussion symptomology. This combined weight-drop model provides a reliable tool for researchers to study early therapeutic responses to concussions in addition to repetitive brain injury, since this protocol induces a closed-head mild trauma.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".