Differential abilities of mouse liver parenchymal and nonparenchymal cells in HDL and LDL (native and oxidized) association and cholesterol efflux
Bibliographic record
Abstract
The aim of this study was to quantify the abilities of mouse liver parenchymal and nonparenchymal cells with respect to (i) cholesteryl ester (CE) selective uptake from low-density lipoproteins (LDL), oxidized LDL (OxLDL), and high-density lipoprotein (HDL); and (ii) their free cholesterol efflux to HDL. The preparations of cells were incubated with lipoproteins labelled either in protein with iodine-125 or in CE with 3H-cholesterol oleate, and lipoprotein-protein and lipoprotein-CE associations were measured. The associations of LDL-protein and LDL-CE with nonparenchymal cells were 5- and 2-fold greater, respectively, than with parenchymal cells. However, in terms of CE-selective uptake (CE association minus protein association) both types of cell were equivalent. Similar results were obtained with OxLDL, but both types of cell showed higher abilities in OxLDL-CE than in LDL-CE selective uptake (on average by 3.4-fold). The association of HDL-protein with nonparenchymal cells was 3x that with parenchymal cells; however, nonparenchymal cells associated 45% less HDL-CE. Contrary to parenchymal cells, nonparenchymal cells did not show HDL-CE selective uptake activity. Thus parenchymal cells selectively take CE from the 3 types of lipoproteins, whereas nonparenchymal cells exert this function only on LDL and OxLDL. Efflux was 3.5-fold more important in nonparenchymal than in parenchymal cells.
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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 | 0.000 | 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) | 0.001 | 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".