Dietary intake alters behavioral recovery and gene expression profiles in the brain of juvenile rats that have experienced a concussion
Bibliographic record
Abstract
Concussion and mild traumatic brain injury (mTBI) research has made minimal progress diagnosing who will suffer from lingering symptomology or generating effective treatment strategies. Research demonstrates that dietary intake affects many biological systems including brain and neurological health. This study determined if exposure to a high fat diet (HFD) or caloric restriction (CR) altered post-concussion susceptibility or resiliency using a rodent model of pediatric concussion. Rats were maintained on HFD, CR, or standard diet (STD) throughout life (including the prenatal period and weaning). At postnatal day 30, male and female rats experienced a concussion or a sham injury which was followed by 17 days of testing. Prefrontal cortex and hippocampus tissue was collected for molecular profiling. Gene expression changes in BDNF, CREB, DNMT1, FGF-2, IGF1, LEP, PGC-1α, SIRT1, Tau, and TERT were analyzed with respect to injury and diet. Analysis of telomere length (TL) using peripheral skin cells and brain tissue found that TL in skin significantly correlated with TL in brain tissue and TL was affected by dietary intake and injury status. With respect to mTBI outcomes, diet was correlated with recovery as animals on the HFD often displayed poorer performance than animals on the CR diet. Molecular analysis demonstrated that diet induced epigenetic changes that can be associated with differences in individual predisposition and resiliency to post-concussion syndrome.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".