Abstract 71: Assessment of Reverse Cholesterol Transport in Vivo in Humans: A Novel Method
Bibliographic record
Abstract
Aim: Reverse cholesterol transport (RCT) is one of the main atheroprotective functions of HDL; however no method exists to assess RCT in vivo in humans. We developed a macrophage-specific method using 3H-cholesterol/albumin complexes that has been validated in animal studies. We present the results of a feasibility study in humans. Methods: Thirty subjects received 3H-cholesterol/albumin complexes as i.v. bolus, followed by blood and stool sample collection up to 8 days. Tracer counts were assessed in plasma, non-HDL, HDL and fecal fractions. Data were analyzed using multi-compartmental modeling. Results: Figure 1 shows the tracer kinetics as free cholesterol (FC; panel A) and cholesteryl ester (CE; panel B) specific activity in plasma, HDL and non-HDL. 3H-cholesterol disappeared from plasma rapidly after injection (macrophage uptake of 3H-cholesterol/albumin complexes); nadir was reached by 60 min. Counts present as FC in the HDL fraction increase rapidly and linearly in the first 240 min after nadir (129.9±85.5 cpm/μmols per hour)(3H-cholesterol uptake from macrophages by HDL). Linearity is lost after ∼240 min, as FC is transformed to CE and exchanged with other fractions. In a subset of subjects (n=10), multi-compartmental analysis was used to calculate fractional transfer rates, including macrophage FC to HDL-FC (0.040 ± 0.014 fraction/hour), HDL-FC to HDL-CE (0.238 ± 0.083 fraction/hour), HDL-CE to nonHDL-CE (0.200 ± 0.113 fraction/hour) and fecal excretion (0.032 ± 0.006 fraction/hour). Conclusions: These preliminary data support the feasibility of this approach and suggest that it may be used to measure macrophage RCT in vivo in humans.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".