Neonatal dietary supplementation of arachidonic acid increases prostaglandin levels in adipose tissue but does not promote fat mass development in guinea pigs
Bibliographic record
Abstract
The role of arachidonic acid (AA) on the development of adipose tissue is still controversial since its metabolites, i.e., prostaglandins, can either stimulate or inhibit preadipocyte differentiation in vitro. In the present study, we evaluated the effects of early postnatal supplementation of AA on body weight and adipose tissue development in guinea pigs. Male newborn guinea pigs were fed for 21 days (day 21) with diets (milk and pellet) supplemented (+AA) or not (-AA) with 1.2% (total fatty acids) AA. From day 21 to day 105 both groups were fed a chow diet. The 21-days-old +AA pups showed a twofold higher AA accretion in phospholipids associated with a two- to sixfold increase in several prostaglandins, such as 6-keto PGF(1alpha) (the stable hydrolysis product of PGI(2)), PGF(2alpha), PGE(2), and PGD(2) in adipose tissue, compared with the -AA group. No difference in fat pad and body weight, aP2, and leptin gene expression in adipose tissue, fasting plasma glucose, free-fatty acids, and triglyceride concentration was observed between groups at day 21 or day 105. These results show that dietary supplementation of AA during the suckling/weaning period increases prostaglandin levels in adipose tissue but does not influence early fat mass development in the guinea pig.
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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) | 0.001 | 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".