Separation and quantitative recovery of mouse serum arylesterase and carboxylesterase activity
Bibliographic record
Abstract
Paraoxonase-1 (PON1) is known to be associated with high density lipoproteins. We optimized buffer conditions to obtain quantitative recovery of PON1 (arylesterase) activity and analyzed the distribution of PON1 in mice using a combination of size-exclusion chromatography and ultracentrifugation. Size-exclusion chromatography of mouse serum separated the esterase activity into two peaks, one overlapping the high density lipoproteins and a second peak of lower molecular weight, consistent with serum carboxylesterase, which accounted for approximately 20% of the total esterase activity of normal mouse serum. Using conditions for the quantitative recovery of arylesterase activity, we fractionated serum by ultracentrifugation into d < 1.21 g/ml, d < 1.25 g/ml, d > 1.21 g/ml, and d > 1.25 g/ml fractions. We observed that PON1 arylesterase activity and mass were isolated in the d < 1.21 g/ml fraction and that serum carboxylesterase was recovered in the d > 1.25 g/ml fraction. The significance of the confounding of PON1 arylesterase activity by serum carboxylesterase was demonstrated by studying mice challenged with a high-fat, high-cholate diet for 14 days. It was shown that all of the decrease in arylesterase activity in response to this diet is attributable to the HDL-associated arylesterase activity (PON1). We conclude that mouse PON1 is quantitatively associated with high density lipoproteins. The contribution of serum carboxylesterase to the total esterase activity significantly confounds the interpretation of total arylesterase activity in mouse serum.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".