Scavenger receptor B1, the HDL receptor, is expressed abundantly in liver sinusoidal endothelial cells
Bibliographic record
Abstract
Cholesterol from peripheral tissue, carried by HDL, is metabolized in the liver after uptake by the HDL receptor, SR-B1. Hepatocytes have long been considered the only liver cells expressing SR-B1; however, in this study we describe two disparate immunofluorescence (IF) experiments that suggest otherwise. Using high-resolution confocal microscopy employing ultrathin (120 nm) sections of mouse liver, improving z-axis resolution, we identified the liver sinusoidal endothelial cells (LSEC), marked by FcγRIIb, as the cell within the liver expressing abundant SR-B1. In contrast, the hepatocyte, identified with β-catenin, expressed considerably weaker levels, although optical resolution of SR-B1 was inadequate. Thus, we moved to a different IF strategy, first separating dissociated liver cells by gradient centrifugation into two portions, hepatocytes (parenchymal cells) and LSEC (non-parenchymal cells). Characterizing both portions for the cellular expression of SR-B1 by flow cytometry, we found that LSEC expressed considerable amounts of SR-B1 while in hepatocytes SR-B1 expression was barely perceptible. Assessing mRNA of SR-B1 by real time PCR we found messenger expression in LSEC to be about 5 times higher than in hepatocytes.
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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 | 0.001 |
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".