Cellular Senescence and Mild Traumatic Brain Injury: Evidence from Sex-balanced Human and Mouse Studies
Bibliographic record
Abstract
Mild traumatic brain injury (mTBI) is associated with symptoms including headache, fatigue, and attention problems. While most patients recover within weeks, some have long-term symptoms lasting months or years, with discrepancies reported between sexes. mTBI is associated with an increased risk of neurodegenerative diseases including Alzheimer’s and chronic traumatic encephalopathy (CTE), though most neuropathology studies to date have been exclusive to men. While a relationship between mTBI and long-term brain dysfunction is established, the molecular mechanisms driving this are unclear, and the proposed pathology does not fully explain clinical manifestations nor sex differences observed. In my previous work I showed DNA damage and cellular senescence, a state of chronic low-grade inflammation and altered cell metabolism linked to disease states, in male brains with mTBI history from sports. This thesis has used both human brain tissue and a mouse model of repeated mTBI (rmTBI) to explore sex-based discrepancies in neuropathology and cellular senescence as a driver of brain dysfunction. In a cohort of male brains with rmTBI history through contact sports, I found that playing position did not correlate with CTE at autopsy, despite this indicating head trauma exposure. In a cohort of female brains with rmTBI history through interpartner violence, I found that, despite other pathological findings, none of the cases had CTE. At the time of this thesis publication, there are no published cohort studies on the neuropathology of interpartner violence nor brain trauma in females. These human studies implied that head trauma does not definitively lead to CTE and suggests other molecular changes preceding pathology may better correlate. In a mouse model of rmTBI, I found that injured mice accumulated DNA damage, with marked sex differences, compared to shams. Using single-cell RNA sequencing, I found that injured mice accumulated both senescent neurons and glial cells. When I targeted these senescent cells with the pharmacological drug ABT-263, I found restoration of cognitive function, suggesting that cellular senescence contributes to post-mTBI brain dysfunction. This thesis provides evidence that cellular senescence drives brain dysfunction after mTBI and may be a viable therapeutic target suitable for personalized approaches in the future.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".