A novel method for measuring intestinal and hepatic triacylglycerol kinetics
Bibliographic record
Abstract
This study aimed to 1) develop a method that completely separated hepatic (VLDL1, VLDL2) and intestinal [chylomicron (CM)] lipoproteins and 2) use the method to measure triacylglycerol (TAG) kinetics in these lipoproteins in the fed and fasting state in healthy subjects, using intravenous [²H₅]glycerol as the tracer. An immunoaffinity method that completely separated hepatic and intestinal particles using sequential binding to three antibodies to apolipoprotein B-100 (apoB-100) was established and validated. Six healthy volunteers were studied in a fasted and continuous feeding study (study 1). Five additional healthy volunteers were studied in a continuous feeding study that included an oral [¹³C₃]glycerol tripalmitin tracer (study 2). In both studies, an intravenous bolus of [²H₅]glycerol was administered to label TAG in hepatic and intestinal lipoproteins. In both feeding studies there was sufficient incorporation of the [²H₅]glycerol tracer into the exogenous lipoproteins to enable isotopic enrichment to be measured. In study 2, the oral tracer enrichment in VLDL1 was <5% of CM enrichment 150 min after tracer administration, demonstrating negligible contamination of VLDL1 with apoB-48. Western blotting showed no detectable apoB-100 in CMs. VLDL1 and VLDL2 TAG fractional catabolic rate (FCR) did not differ between feeding and fasting (study 1). There was no difference between CM and VLDL1 TAG FCR in both fed studies. In fed study 2, 47% of the total TAG production rate (CM + VLDL1) was from CM. This methodology may be a useful tool for understanding the abnormalities in postprandial TAG kinetics in metabolic syndrome and type 2 diabetes.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".