Differential Effects of Oral Doxercalciferol (Hectorol®) or Paricalcitol (Zemplar®) in the Cyp27b1-Null Mouse Model of Uremia
Bibliographic record
Abstract
BACKGROUND/AIMS: Kidney disease patients experience declining calcitriol levels and develop secondary hyperparathyroidism (SHPT). Animal models of uremia based on 5/6 nephrectomy (NTX) do not consistently reproduce this calcitriol deficiency. We developed an animal model, the NTX Cyp27b1-null mouse, which completely lacks endogenous calcitriol, and examined the suitability of this model for evaluation of treatment with vitamin D analogs in uremia. METHODS: NTX was performed at 2 months of age. One week post-NTX, animals were treated for 4 weeks with vehicle; doxercalciferol at 30, 100 or 300 pg/g body weight (b.w.); or paricalcitol at 100, 300 or 1,000 pg/g b.w. by gavage 3 times per week. RESULTS: Serum blood urea nitrogen and creatinine were elevated. Vehicle-treated NTX null mice had hypocalcemia and SHPT. Doxercalciferol at 100 or 300 pg/g b.w. normalized serum calcium and parathyroid hormone (PTH) levels. Paricalcitol at 300 or 1,000 pg/g normalized serum calcium, but PTH levels remained elevated. Osteomalacia was corrected by 100 pg/g b.w. of doxercalciferol or 1,000 pg/g b.w. of paricalcitol. The highest dose of doxercalciferol, but not of paricalcitol, significantly reduced osteitis fibrosa. CONCLUSION: Our results reveal the differential efficacy of doxercalciferol and paricalcitol in this novel animal model incorporating both calcitriol deficiency and renal insufficiency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.002 |
| 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".