Pharmacologic modulation of brain metabolism by valproic acid can induce a neuroprotective environment
Bibliographic record
Abstract
OBJECTIVE: Traumatic brain injury (TBI) is a leading cause of trauma-related morbidity and mortality. Valproic acid (VPA) has been shown to attenuate brain lesion size and swelling within the first few hours following TBI. Because injured neurons are sensitive to metabolic changes, we hypothesized that VPA treatment would alter the metabolic profile in the perilesional brain tissues to create a neuroprotective environment. METHODS: We subjected swine to combined TBI (12-mm cortical impact) and hemorrhagic shock (40% blood volume loss and 2 hours of hypotension) and randomized them to two groups (n = 5/group): (1) normal saline (NS; 3× hemorrhage volume) and (2) NS-VPA (NS, 3× hemorrhage volume; VPA, 150 mg/kg). After 6 hours, brains were harvested, and 100 mg of the perilesional tissue was used for metabolite extraction. Samples were analyzed using reversed-phase liquid chromatography-mass spectrometry in positive and negative ion modes, and data were analyzed using MetaboAnalyst software (McGill University, Quebec, Canada). RESULTS: In untargeted reversed-phase liquid chromatography-mass spectrometry analysis, we detected 3,750 and 1,955 metabolites in positive and negative ion modes, respectively. There were no significantly different metabolites in positive ion mode; however, 167 metabolite features were significantly different (p < 0.05) in the negative ion mode, which included VPA derivates. Pathway analysis showed that several pathways were affected in the treatment group, including the biosynthesis of unsaturated fatty acids (p = 0.001). Targeted amino acid analysis on glycolysis/tricarboxylic acid (TCA) cycle revealed that VPA treatment significantly decreased the levels of the excitotoxic amino acid serine (p = 0.001). CONCLUSION: Valproic acid can be detected in perilesional tissues in its metabolized form. It also induces metabolic changes in the brains within the first few hours following TBI to create a neuroprotective environment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".