Differential impacts of pregnancy and lactation on maternal calcium homeostasis: a mathematical modeling analysis
Bibliographic record
Abstract
The maternal physiological adaptations during pregnancy and lactation impact almost all tissues and organs, including those involved in calcium homeostasis. Despite having a similar additional calcium demand, maternal adaptations in pregnancy and lactation are different. During pregnancy, the mother’s body increases intestinal absorption of calcium. However, during lactation, intestinal absorption returns to normal levels and the calcium needs of breastmilk are met by increased bone resorption and renal calcium reabsorption. Existing mathematical models of calcium homeostasis do not consider these unique physiological states. Given this observation, the goal of this project is to develop the first pregnancy- and lactation-specific mathematical models of calcium regulation. The resulting models represent how a female body adapts to support the excess demands brought on by pregnancy and lactation. Our computational models reveal how both differential adaptations support calcium delivery to the fetus and breastmilk while maintaining normal calcium ranges in the maternal body. This work is supported by the Canada 150 Research Chair program and by the Natural Sciences and Engineering Research Council of Canada. This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".