Renal adaptations in gestational hypertension preserves K <sup>+</sup> while minimizing Na <sup>+</sup> retention
Bibliographic record
Abstract
During pregnancy, major adaptations in renal morphology, hemodynamics, and transport occur to achieve the volume and electrolyte retention required in pregnancy. These changes are complex, and in isolation are sometimes counterintuitive. Additionally, during pregnancies complicated by a disorder, such as gestational hypertension, altered renal function from normal pregnancy values occur. The goal of this study is to analyze how altered renal function during gestational hypertension impacts renal function during pregnancy. We hypothesize that hypertension‐induced changes in renal transport pattern shifts Na + transport to downstream segments, while retaining sufficient K + for pregnancy needs. To test this hypothesis, we developed epithelial cell‐based computational models of solute and water transport in the superficial and juxtamedullary nephrons of the kidney for a pregnant rat with hypertension. The model represents reduction in proximal tubule and medullary loop transporters. As a result, urine Na + and volume output are predicted to increase, which is evidence for pressure natriuresis. Due to differential changes in transporter activities, Na + transport load is predicted to shift to distal segments, where transporters are upregulated. Consequently, natriuresis and diuresis are partially counteracted. Simulation results also suggest that differential regulation of medullary (decrease) and cortical (increase) thick ascending limb transporters is important to preserve K + while minimizing Na + retention during gestational hypertension.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".