Repetitive Traumatic Brain Injury Is Associated With TDP-43 Alterations, Neurodegeneration, and Glial Activation in Mice
Bibliographic record
Abstract
Increasing evidence points to a relationship between repetitive mild traumatic brain injury (mTBI), the Tar DNA binding protein 43 (TDP-43) pathology and some neurodegenerative diseases, but the underlying pathophysiological mechanisms are still unknown. We examined TDP-43 regulation, neurodegeneration, and glial responses following repetitive mTBI in nontransgenic mice and in animals with overexpression of human mutant TDP-43 protein (TDP-43G348C). In the frontal cortices of the injured nontransgenic animals, early TDP-43 cytoplasmatic translocation and overexpression of the protein and its pathological forms were detected. In the injured animals of both genotypes, neurodegeneration and pronounced glial activity were detected in the optic tract. In TDP-43G348C mice, these changes were significantly higher at day 7 after the last mTBI compared with the values in the nontransgenic animals. Results of this study suggest that the changes in the TDP-43 regulation in the frontal cortices of the nontransgenic animals were a transient stress response to the brain injury. Repetitive mTBI did not produce additional TDP-43 dysregulation or neurodegeneration or pronounced gliosis in the frontal cortex of TDP-43G348C mice. Our research also suggests that overexpression of mutated human TDP-43 possibly predisposes the brain to more intense neurodegeneration and glial activation in the optic tract after repetitive mTBI.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".