Potencies of vitamin D analogs, 1α‐hydroxyvitamin D<sub>3</sub>, 1α‐hydroxyvitamin D<sub>2</sub> and 25‐hydroxyvitamin D<sub>3</sub>, in lowering cholesterol in hypercholesterolemic mice <i>in vivo</i>
Bibliographic record
Abstract
Abstract Vitamin D3 and the synthetic vitamin D analogs, 1α‐hydroxyvitamin D3 [1α(OH)D3], 1α‐hydroxyvitamin D2 [1α(OH)D2] and 25‐hydroxyvitamin D3 [25(OH)D3] were appraised for their vitamin D receptor (VDR) associated‐potencies as cholesterol lowering agents in mice in vivo. These precursors are activated in vivo: 1α(OH)D3 and 1α(OH)D2 are transformed by liver CYP2R1 and CYP27A1 to active VDR ligands, 1α,25‐dihydroxyvitamin D3 [1,25(OH)2D3] and 1α,25‐dihydroxyvitamin D2 [1,25(OH)2D2], respectively. 1α(OH)D2 may also be activated by CYP24A1 to 1α,24‐dihydroxyvitamin D2 [1,24(OH)2D2], another active VDR ligand. 25(OH)D3, the metabolite formed via CYP2R1 and or CYP27A1 in liver from vitamin D3, is activated by CYP27B1 in the kidney to 1,25(OH)2D3. In C57BL/6 mice fed the high fat/high cholesterol Western diet for 3 weeks, vitamin D analogs were administered every other day intraperitoneally during the last week of the diet. The rank order for cholesterol lowering, achieved via mouse liver small heterodimer partner (Shp) inhibition and increased cholesterol 7α‐hydroxylase (Cyp7a1) expression, was: 1.75 nmol/kg 1α(OH)D3 > 1248 nmol/kg 25(OH)D3 (dose ratio of 0.0014) > > 1625 nmol/kg vitamin D3. Except for 1.21 nmol/kg 1α(OH)D2 that failed to lower liver and plasma cholesterol contents, a significant negative correlation was observed between the liver concentration of 1,25(OH)2D3 formed from the precursors and liver cholesterol levels. The composite results show that vitamin D analogs 1α(OH)D3 and 25(OH)D3 exhibit cholesterol lowering properties upon activation to 1,25(OH)2D3: 1α(OH)D3 is rapidly activated by liver enzymes and 25(OH)D3 is slowly activated by renal Cyp27b1 in mouse.
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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 | 0.001 |
| 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".