Abstract TP250: N-3 Fatty Acid Diglyceride Emulsions As A Novel Acute Treatment For Ischemic Brain Injury
Bibliographic record
Abstract
Introduction: Omega-3 (n-3) fatty acids (FAs), specifically docohexaenoic acid (DHA) and eicosapentaenoic acid (EPA), act as bioactive unsaturated lipids with pleiotropic effects, affording neuroprotection in ischemic brain injury. Hypothesis: We reported that n-3 FAs injected acutely as triglyceride (TG) emulsions provide neuroprotection after ischemic brain injury. We now questioned whether novel lipid emulsions made from n-3 diglycerides (DG) would improve the delivery and effectiveness of n-3 FAs in brain after injury. Methods: We evaluated in vitro interactions of DG (DG-DHA) vs TG (TG-DHA) in phosphatidylcholine (PC) bilayer liposomes, as a model membrane system, by NMR spectroscopy. We compared the in vitro kinetics of DG vs TG hydrolysis by lipoprotein lipase. We investigated the neuroprotective effects of DG emulsions in a Vannucci murine model of hypoxic-ischemic (HI) brain injury. Results: NMR spectra of PC liposomes incubated with DG-DHA showed an additional peak, adjacent to the phospholipid carbonyl region, indicating a higher incorporation into PC bilayers and a narrower peak at almost the same position in a more fluid phase. In contrast, spectra of liposomes incubated with TG-DHA showed narrow peaks well-separated from PC resonances, representing phase-separated oil droplets. In lipolysis assays, DG emulsions had more efficient hydrolysis than TGs. Neonatal mice treated with DG-EPA, DG-DHA, or the combination of both (DG-DHA+EPA) after HI injury showed up to 3X better reduction in infarct volumes compared to TGs (p<0.05). Conclusions: Our data demonstrate that DG molecules incorporate in membrane bilayers more efficiently than TG. We postulate that the faster hydrolysis of DGs contributes to higher neuroprotection compared with TGs. Our findings indicate that n-3 DG emulsions represent a novel and much more efficient modality than n-3 TG for improving ischemic brain injury outcomes.
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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) | 0.003 | 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".