Characterization of an arachidonic acid-deficient (Fads1 knockout) mouse model
Bibliographic record
Abstract
Arachidonic acid (20:4(Δ5,8,11,14), AA)-derived eicosanoids regulate inflammation and promote cancer development. Previous studies have targeted prostaglandin enzymes in an attempt to modulate AA metabolism. However, due to safety concerns surrounding the use of pharmaceutical agents designed to target Ptgs2 (cyclooxygenase 2) and its downstream targets, it is important to identify new targets upstream of Ptgs2. Therefore, we determined the utility of antagonizing tissue AA levels as a novel approach to suppressing AA-derived eicosanoids. Systemic disruption of the Fads1 (Δ5 desaturase) gene reciprocally altered the levels of dihomo-γ-linolenic acid (20:3(Δ8,11,14), DGLA) and AA in mouse tissues, resulting in a profound increase in 1-series-derived and a concurrent decrease in 2-series-derived prostaglandins. The lack of AA-derived eicosanoids, e.g., PGE₂ was associated with perturbed intestinal crypt proliferation, immune cell homeostasis, and a heightened sensitivity to acute inflammatory challenge. In addition, null mice failed to thrive, dying off by 12 weeks of age. Dietary supplementation with AA extended the longevity of null mice to levels comparable to wild-type mice. We propose that this new mouse model will expand our understanding of how AA and its metabolites mediate inflammation and promote malignant transformation, with the eventual goal of identifying new drug targets upstream of Ptgs2.
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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 | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".