Comparison of Lipoprotein Analysis Using Gel-Permeation High-Performance Liquid Chromatography and a Biochemistry Analyzer in Normolipidemic and Dyslipidemic Quaker Parrots (Myiopsitta monachus)
Bibliographic record
Abstract
Lipid accumulation disorders are common in psittacine birds and can be associated with changes in plasma lipoproteins, most notably low-density lipoprotein (LDL) and high-density lipoprotein (HDL). However, lipoprotein analysis by standard laboratory analyzers or an indirect method, such as the Friedewald formula, has not been validated in parrots. A research colony of 12 Quaker parrots (Myiopsitta monachus) were used to compare plasma values from the Roche Cobas c501 biochemistry analyzer for total cholesterol, total triglycerides, LDL, and HDL to gel-permeation high-performance liquid chromatography (GP-HPLC). To increase sample size and broaden the analytical range to include dyslipidemic samples, 2 cross-over studies were performed on a 0.3% cholesterol diet and a 20% fat diet. Agreement between methods was assessed by linear mixed models and Bland and Altman plots. The LDL concentrations calculated by the Friedewald formula and alternative formulas, and the effects of triglycerides on the biases, were also evaluated. Forty-five plasma samples were used. The cholesterol diet induced a marked increase in cholesterol and all lipoproteins, whereas the fat diet did not lead to dyslipidemia. Direct and indirect LDL measurements obtained with the clinical analyzer were not in clinical agreement with GP-HPLC, whereas HDL had acceptable agreement for normotriglyceridemic samples. Hypertriglyceridemic plasma samples were found to interfere with lipoprotein measurements. This study found LDL measured by the Roche Cobas c501 biochemistry analyzer and indirect estimations cannot be recommended in the Quaker parrot, and non-HDL cholesterol should be used instead. Lipoprotein panels obtained from hypertriglyceridemic samples should be interpreted with care.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".