Lipoprotein characterization in Quaker parrots (<i>Myiopsitta monachus</i>) using gel‐permeation high‐performance liquid chromatography
Bibliographic record
Abstract
BACKGROUND: Lipid accumulation disorders, such as atherosclerosis and hepatic lipidosis, are common in psittacine birds and associated with various dyslipidemias. Gel-permeation high-performance liquid chromatography (GP-HPLC) is a reference method for advanced lipoprotein profiling based on particle size separation, followed by an analysis of lipid contents. OBJECTIVES: The objectives were to (a) characterize Quaker parrot lipoproteins using a commercial GP-HPLC method (Liposearch panel), and (b) obtain preliminary information on the reliability of the Friedewald formula for low-density lipoprotein-cholesterol (LDL-C) measurements. METHODS: Plasma samples were collected from 12 fasted healthy Quaker parrots. Cholesterol concentrations, triglyceride concentrations, particle sizes, and particle numbers were determined by GP-HPLC for four classes and 20 sub-fractions of lipoproteins. The LDL-C concentrations obtained using the Friedewald formula and direct measurements were compared with Bland-Altman plots. Alternate formulas were determined using multiple linear regression. RESULTS: High-density lipoprotein (HDL) was the predominant lipoprotein in Quaker parrots, and most particles were of medium-to-small sizes belonging to two sub-fractions (average size, 10.6 nm). LDL was the second most common lipoprotein and included large-to-small particles belonging to three sub-fractions (average size, 24.9 nm). Very-low-density lipoproteins (VLDL) and portomicrons were present in low concentrations. The Friedewald formula underestimated LDL-C concentrations with a significant bias of 0.44 mmol/L. An alternate formula was proposed: LDL-C = 0.75*Non-HDL-C. CONCLUSIONS: GP-HPLC allowed unprecedented characterization of plasma lipoproteins in Quaker parrots. Characterizing psittacine lipoprotein is useful for validation and interpretation of routine clinical tests as well as for use in epidemiologic and experimental research on psittacine lipid accumulation disorders.
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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 | 0.001 | 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) | 0.000 | 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".