Calcitonin Plays a Critical Role in Regulating Skeletal Mineral Metabolism during Lactation
Bibliographic record
Abstract
The maternal skeleton rapidly demineralizes during lactation to provide calcium to milk, responding to the stimuli of estrogen deficiency and mammary-secreted PTH-related protein. We used calcitonin/calcitonin gene-related peptide-alpha (Ctcgrp) null mice to determine whether calcitonin also modulates lactational mineral metabolism. During 21 d of lactation, spine bone mineral content dropped 53.6% in Ctcgrp nulls vs. 23.6% in wild-type (WT) siblings (P < 0.0002). After weaning, bone mineral content returned fully to baseline in 18.1 d in Ctcgrp null vs. 13.1 d in WT (P < 0.01) mice. Daily treatment with salmon calcitonin from the onset of lactation normalized the losses in Ctcgrp null mice, whereas calcitonin gene-related peptide-alpha or vehicle was without effect. Compared with WT, Ctcgrp null mice had increased circulating levels of PTH and up-regulation of mammary gland PTH-related protein mRNA. In addition, lactation caused the Ctcgrp null skeleton to undergo more trabecular thinning and increased trabecular separation compared with WT. Our studies confirm that an important physiological role of calcitonin is to protect the maternal skeleton against excessive resorption and attendant fragility during lactation and reveal that the postweaning skeleton has the remarkable ability to rapidly recover even from losses of over 50% of skeletal mineral content.
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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".