Investigating the role of the brain-derived neurotrophic factor Val66Met polymorphism in repetitive mild traumatic brain injury outcomes in rats
Bibliographic record
Abstract
BACKGROUND: Mild traumatic brain injury (mTBI) poses a significant public health concern, particularly regarding repetitive injury, with outcomes ranging from acute neurobehavioral deficits to long-term impairments. While demographic factors like age and sex influence outcomes, the understanding of genetic contributions, particularly the role of the brain-derived neurotrophic factor (BDNF) Val66Met polymorphism, remains limited. This study aimed to characterize acute effects of repetitive mTBI (rmTBI) in rats with the Val68Met SNP, the rodent equivalent of the human Val66Met, focusing on behavioral, fluid biomarker, and histological changes. METHODS: Using a closed-head injury model, rats underwent five mTBIs over consecutive days. Behavioral assessments included sensorimotor function, anxiety-like behavior, spatial learning and memory, and nociceptive response. Plasma neurofilament light (NfL) levels served as a biomarker of axonal injury and immunohistochemistry evaluated microglial activation. RESULTS: Sensorimotor deficits and increased anxiety-like behavior were found in rats with rmTBI, but these changes were not affected by sex or genotype. Plasma NfL levels were higher in rmTBI compared with sham rats, with levels greater in female rmTBI when compared with male rmTBI rats. Microglial activation was observed in the hypothalamus of injured rats, but was not influenced by genotype or sex. CONCLUSIONS: While the Val68Met SNP did not significantly influence acute responses to rmTBI in this study, further investigation into alternative functional and pathophysiological outcomes, as well as long-term effects, is required.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".