Effects of sample handling and storage on quantitative lipid analysis in human serum
Why this work is in the frame
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Bibliographic record
Abstract
There is sparse information about specific storage and handling protocols that minimize analytical error and variability in samples evaluated by targeted metabolomics. Variance components that affect quantitative lipid analysis in a set of human serum samples were determined. The effects of freeze-thaw, extraction state, storage temperature, and freeze-thaw prior to density-based lipoprotein fractionation were quantified. The quantification of high abundance metabolites, representing the biologically relevant lipid species in humans, was highly repeatable (with coefficients of variation as low as 0.01 and 0.02) and largely unaffected by 1-3 freeze-thaw cycles (with 0-8% of metabolites affected in each lipid class). Extraction state had effects on total lipid class amounts, including decreased diacylglycerol and increased phosphatidylethanolamine in thawed compared with frozen samples. The effects of storage temperature over 1 week were minimal, with 0-4% of metabolites affected by storage at 4 degrees C, -20 degrees C, or -80 degrees C in most lipid classes, and 19% of metabolites in diacylglycerol affected by storage at -20 degrees C. Freezing prior to lipoprotein fractionation by density ultracentrifugation decreased HDL free cholesterol by 37% and VLDL free fatty acid by 36%, and increased LDL cholesterol ester by 35% compared with fresh samples. These findings suggest that density-based fractionation should preferably be undertaken in fresh serum samples because up to 37% variability in HDL and LDL cholesterol could result from a single freeze-thaw cycle. Conversely, quantitative lipid analysis within unfractionated serum is minimally affected even with repeated freeze-thaw cycles.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it