The plasma lipidome of the Quaker parrot (Myiopsitta monachus)
Bibliographic record
Abstract
Dyslipidemias and lipid-accumulation disorders are common in captive parrots, in particular in Quaker parrots. Currently available diagnostic tests only measure a fraction of blood lipids and have overall problematic cross-species applicability. Comprehensively analyzing lipids in the plasma of parrots is the first step to better understand their lipid metabolism in health and disease, as well as to explore new lipid biomarkers. The plasma lipidome of 12 Quaker parrots was investigated using UHPLC-MS/MS with both targeted and untargeted methods. Targeted methods on 6 replicates measured 432 lipids comprised of sterol, cholesterol ester, bile acid, fatty acid, acylcarnitine, glycerolipid, glycerophospholipid, and sphingolipid panels. For untargeted lipidomics, precursor ion mass-to-charge ratios were matched to corresponding lipids using the LIPIDMAPS structure database and LipidBlast at the sum composition or acyl species level of information. Sterol lipids and glycerophospholipids constituted the majority of plasma lipids on a molar basis. The most common lipids detected with the targeted methods included free cholesterol, CE(18:2), CE(20:4) for sterol lipids; PC(36:2), PC(34:2), PC(34:1) for glycerophospholipids; TG(52:3), TG(54:4), TG(54:5), TG(52:2) for glycerolipids; SM(d18:1/16:0) for sphingolipids; and palmitic acid for fatty acyls. Over a thousand different lipid species were detected by untargeted lipidomics. Sex differences in the plasma lipidome were observed using heatmaps, principal component analysis, and discriminant analysis. This report presents the first comprehensive database of plasma lipid species in psittacine birds and paves the way for further research into blood lipid diagnostics and the impact of diet, diseases, and drugs on the parrot plasma lipidome.
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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 | 0.000 |
| 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".