The Normal Liver Harbors the Vitamin D Nuclear Receptor in Nonparenchymal and Biliary Epithelial Cells
Bibliographic record
Abstract
The liver is generally considered negative for the vitamin D nuclear receptor (VDR(n)), even though several studies have shown significant effects of 1,25-dihydroxyvitamin D(3) (1,25(OH)(2)D(3)) on liver cell physiology. The low abundance of VDR(n) in the liver led us to propose that hepatocytes (the largest hepatic cell population) were most likely negative for the receptor, whereas the small hepatic sinusoidal and ductular cell populations that contain cell types known to express VDR(n) in other tissues should express the receptor. Using freshly isolated cells from normal livers as well as biliary and epithelial hepatic cell lines, our data show that the human, rat, and mouse hepatocytes express very low VDR(n) messenger RNA (mRNA) and protein levels. In contrast, sinusoidal endothelial, Kupffer, and stellate cells of normal rat livers as well as the mouse biliary cell line BDC and rat hepatic neonatal epithelial SD6 cells clearly expressed both VDR(n) mRNA and protein. In addition, specimens of human hepatocarcinoma as well as intrahepatic colon adenocarcinoma metastases were also found to express the VDR(n) gene transcript. Kupffer, stellate, and endothelial cells responded to 1,25(OH)(2)D(3) by a significant increase in the CYP24, indicating that the VDR(n) is fully functional in these cells. In conclusion, selective hepatic cell populations are targets for the vitamin D endocrine/paracrine/intracrine system.
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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".