Characterisation of the Selective Reduced Uteroplacental Perfusion (sRUPP) Model of Preeclampsia
Bibliographic record
Abstract
Preeclampsia is a complication of pregnancy characterised by gestational hypertension, proteinuria and/or end organ disease. The reduced uteroplacental perfusion (RUPP) model, via partial occlusion of the lower abdominal aorta, mimics insufficient placental perfusion as a primary causal characteristic of preeclampsia. However, a major limitation of the RUPP model is that perfusion is reduced to the entire hindquarters of the rat resulting in hindlimb ischemia. We hypothesised that clipping the uterine and ovarian arteries in the selective (s)RUPP model would provoke signs of preeclampsia while avoiding systemic ischemia. Sham, RUPP or sRUPP procedures were performed in pregnant Sprague Dawley rats on gestational day (GD)14. On GD21 uterine blood flow was significantly reduced in both the RUPP and sRUPP models while aortic flow was reduced only in RUPP. Both models resulted in increased MAP, increased vascular oxidative stress (superoxide generation), increased pro-inflammatory (RANTES) and reduced pro-angiogenic (endoglin) mediators. Vascular compliance and constriction were unaltered in either RUPP or sRUPP groups. In summary, refinements to the RUPP model simultaneously maintain the characteristic phenotype of preeclampsia and avoid peripheral ischemia; providing a useful tool which may be used to increase our knowledge and bring us closer to a solution for women affected by preeclampsia.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".