Unraveling the Mechanistic Links Between Blood Pressure Regulation and Calcium-Magnesium Homeostasis: Insights into Hypertension, Hyperparathyroidism, and Mineral Disorders
Bibliographic record
Abstract
Abstract The systems regulating blood pressure and calcium-magnesium (Ca 2+ -Mg 2+ ) homeostasis are increasingly recognized to have significant, clinically relevant interactions, where alterations in one can lead to significant changes in the other. In this study, we developed a computational model integrating blood pressure regulation and Ca 2 □-Mg 2 □ homeostasis in a male rat. We simulated various conditions, including hypertension, Ca 2 □, Mg 2 □, and vitamin D 3 deficiencies, and primary hyperparathyroidism. Simulations of hypertension, induced by various stimuli like increased renin or aldosterone secretion, demonstrated significant effects on parathyroid hormone (PTH), calcitriol, renal Ca 2 □/Mg 2 □ handling, and bone resorption. Dietary Ca 2 □, Mg 2 □, and vitamin D 3 deficiencies elevated mean arterial pressure, with Mg 2 □ deficiency having a stronger effect. Furthermore, the model predicted that primary hyperparathyroidism elevates PTH, Ca 2 □, and calcitriol, leading to increased mean arterial pressure and bone loss. Overall, this model provides valuable insights into the mechanistic links between blood pressure regulation and Ca 2 □-Mg 2 □ homeostasis, offering insights into clinical conditions like hypertension and hyperparathyroidism.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".