Factors affecting variability in free oxylipins in mammalian tissues
Bibliographic record
Abstract
PURPOSE OF THE REVIEW: Along with the growing interest in oxylipins is an increasing awareness of multiple sources of variability in oxylipin data. This review summarizes recent findings that highlight the experimental and biological sources of variation in free oxylipins. RECENT FINDINGS: Experimental factors that affect oxylipin variability include different methods of euthanasia, postmortem changes, cell culture reagents, tissue processing conditions and timing, storage losses, freeze-thaw cycles, sample preparation techniques, ion suppression, matrix effects, use and availability of oxylipin standards, and postanalysis procedures. Biological factors include dietary lipids, fasting, supplemental selenium, vitamin A deficiency, dietary antioxidants and the microbiome. Overt, but also more subtle differences in health affect oxylipin levels, including during resolution of inflammation and long-term recovery from disease. Sex, genetic variation, exposure to air pollution and chemicals found in food packaging and household and personal care products, as well as many pharmaceuticals used to treat health conditions also affect oxylipin levels. SUMMARY: Experimental sources of oxylipin variability can be minimized with proper analytical procedures and protocol standardization. Fully characterizing study parameters will help delineate biological factors of variability, which are rich sources of information that can be used to probe oxylipin mechanisms of action and to investigate their roles in health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".