Cellular senescence and cognitive impairment following repeated mild traumatic brain injury in mice
Bibliographic record
Abstract
Mild traumatic brain injury (mTBI) is common and considered a Canadian public health issue. While some patients recover shortly after injury, others experience long-term symptoms including memory problems, executive dysfunction, behavioural changes, and mood disorders, along with increased risk of neurodegenerative disease. The precise mechanisms by which mTBI causes these changes remain unclear. We hypothesize that mTBI induces cellular senescence, leading to largescale signaling changes which contribute to neurological dysfunction. Here we have utilized an in vivo experimental model of mTBI in mice. Briefly, sex-balanced groups of adult C57BL/6 mice were anesthetized and exposed to three mTBIs with a 24h inter-injury interval via an electromagnetic driven piston or sham surgeries. Mice underwent behavioural testing for the light-dark task, elevated maze task, and Morris water maze task. These tests indicate that mTBI mice have impaired learning, memory, and executive functioning at 1 week and 6-weeks post-injury compared to shams, with sex-dependent changes in risk-taking behaviour and anxiety at 1 week. Molecular analyses (RT-PCR, Western Blot, histology) show evidence of DNA damage and upregulation of DNA damage induced cellular senescence pathways. Single-cell RNA sequencing of mTBI and sham brains at 1-week post-injury revealed largescale signaling changes consistent with cellular senescence and altered neuronal networks. This study has identified senescence as a mechanism by which mTBI leads to neurobehavioural and cognitive deficits and long-term brain dysfunction.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".