Nutrition and neutraceutical interventions as therapeutic strategies to aid recovery from mild traumatic brain injuries
Bibliographic record
Abstract
Concussion, sometimes called a mild traumatic brain injury (mTBI), is an acquired brain injury resulting in alterations to brain function underpinned by a sequence of neuropathological and neurometabolic events that can result in excitotoxicity, oxidative stress, oedema, neuroinflammation and cell death. To date, pharmaceutical therapies have had limited success in treating TBI, presenting considerable interest in nutritional therapies in the form of dietary supplementation to alleviate the deleterious pathological sequelae following neurotrauma. Many nutritional supplements have low toxicity, few drug interactions, and are already approved for human use making them attractive potential therapies following a concussion. In the setting of brain injury models, a considerable body of preclinical evidence has accumulated supporting the use of nutritional supplements including essential fatty acids, vitamins, minerals, amino acids, polyphenols, bioflavonoids and other bioactive compounds to facilitate aspects of functional recovery. Here, we review studies presenting diet supplements as therapeutic strategies as potential treatments for mTBI or to augment neurological resistance against mTBI in experimental models, as well as emerging therapeutic targets including the gut microbiome, and psychedelic and non-psychedelic compounds derived from fungi.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".