Effect of pregnancy and hypertension on kidney function in female rats: Modeling and functional implications
Bibliographic record
Abstract
Throughout pregnancy, the kidneys undergo significant adaptations in morphology, hemodynamics, and transport to achieve the volume and electrolyte retention required to support a healthy pregnancy. Additionally, during pregnancies complicated by chronic hypertension, altered renal function from normal pregnancy occurs. The goal of this study is to analyze how inhibition of critical transporters affects gestational kidney function as well as how renal function is affected during chronic hypertension in pregnancy. To do this, we developed epithelial cell-based multi-nephron computational models of solute and water transport in the kidneys of a female rat in mid- and late pregnancy. We simulated the effects of key individual pregnancy-induced changes on renal Na+ and K+ transport: proximal tubule length, Na+/H+ exchanger isoform 3 (NHE3) activity, epithelial Na+ channel activity (ENaC), K+ secretory channel expression, and H+-K+-ATPase activity. Additionally, we conducted simulations to predict the effects of inhibition and knockout of the ENaC and H+-K+-ATPase transporters on virgin and pregnant rat kidneys. Our simulation results predicted that the ENaC and H+-K+-ATPase transporters are essential for sufficient Na+ and K+ reabsorption during pregnancy. Last, we developed models to capture changes made during hypertension in female rats and considered what may occur when a rat with chronic hypertension becomes pregnant. Model simulations predicted that in hypertension for a pregnant rat there is a similar shift in Na+ transport from the proximal tubules to the distal tubules as in a virgin rat.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".