Platelet Arachidonic Acid Deficiency May Contribute to Abnormal Platelet Function During Parenteral Fish Oil Monotherapy in a Piglet Model
Bibliographic record
Abstract
BACKGROUND: Fish oil monotherapy has been an advance for treating intestinal failure-associated liver disease (IFALD). However, such patients are at risk of bleeding complications from liver disease and because fish oil can inhibit thrombosis. We have previously reported abnormal platelet function in neonatal piglets given fish oil monotherapy during parenteral nutrition (PN). The purpose of this study was to determine if abnormal fatty acid composition of the platelets could explain the prior observed antiplatelet effect. METHODS: Neonatal piglets were assigned to 2 treatments: PN with fish oil monotherapy (FO; n = 4) or PN with soy oil (SO; n = 5). On day 14, plasma was collected and platelets isolated by centrifuging. The fatty acid content in plasma and platelet plug were measured using gas liquid chromatography and compared with controls (CON; n = 5). RESULTS: The arachidonic acid (AA) content in the FO group was on average half that of the SO group, in both the platelets (FO, 3.5% vs SO, 7.6%; P = .021; CON, 4.5%-11%) and the plasma (FO, 3.8% vs SO, 9.2%; P = .002; CON, 6.1%-9.5%). No bleeding complications were observed for any piglets during PN treatment. CONCLUSIONS: Using platelet mapping, we have previously shown that neonatal piglets given fish oil monotherapy have abnormal platelet function in the AA pathway. This report demonstrates that such an abnormality can be explained by platelet AA deficiency. Platelet mapping and platelet fatty acid analysis should be undertaken in human infants treated with fish oil monotherapy during PN.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".