Selective Reduction of Brain Docosahexaenoic Acid after Experimental Brain Injury and Mitigation of Neuroinflammatory Outcomes with Dietary DHA
Bibliographic record
Abstract
Background Docosahexaenoic acid (DHA) is an omega-3 fatty acid that is important for brain development and function, but the interactions of dietary DHA with fatty acid profiles, sensory sensitivities, and inflammation that may change after traumatic brain injury (TBI) are poorly understood. It is also unknown whether DHA alters experimental TBI outcomes measured more than 2 weeks after injury. The current study investigated whether dietary DHA, provided before (PreDHA) or after (PostDHA) experimental TBI, would improve outcomes for up to 24 days after injury. Methods Rats consumed predetermined diets for 28 days prior to midline fluid percussion injury (mFPI) or to sham surgery. The effects of PreDHA, TBI, and PostDHA on comprehensive fatty acid profiles, neuroinflammation, sensory sensitivity, and spatial learning were then evaluated. Results The results provided novel evidence that TBI selectively reduced brain DHA content, as injury did not decrease any other fatty acid that was measured. Furthermore, PreDHA and PostDHA attenuated injury-induced increases in sensory sensitivity as well as in tumor necrosis factor-α (TNF-α), interleukin-10, and interleukin-1β in the somatosensory cortex. However, [3H]PK11195 autoradiography showed that PostDHA was more effective than PreDHA in reducing microglial/macrophage activation in the somatosensory cortex, hippocampus, and substantia nigra. Spatial learning outcomes were largely unaffected by diet or injury, but PostDHA was associated with shorter swimming distances in the Morris water maze (MWM) at 15 days post-injury. Conclusion Overall, sufficient DHA intake may be necessary to replace DHA that is lost to TBI and may improve some symptoms of post-concussive syndrome (PCS) over an extended period through inflammation-related mechanisms.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".