Quantifying Proximal Collecting Tubule Deficiency in Angiotensin-Converting Enzyme Inhibitor and Angiotensin II Receptor Blocker Fetopathy
Bibliographic record
Abstract
Introduction Angiotensin-Converting Enzyme Inhibitors and Angiotensin II Receptor Blockers (AAs) are used for several indications, with cessation recommended in pregnancy due to toxic effects. AA fetopathy phenotype is similar to renal tubular dysgenesis including reduced proximal convoluted tubules (PCTs). Our study aimed to quantify the reduction of PCTs in fetuses and infants with prenatal exposure to AAs. Materials and methods We identified 5 fetal AA exposure cases that underwent autopsy at our institution between 2011 and 2018 and compared with 5 gestational age-matched controls. Immunohistochemistry with CD10 and epithelial membrane antigen (EMA) was utilized. Results CD10 and EMA identified a median PCT density of 19.0% ± 12.3% in AA fetopathy patients, significantly less than controls (52.8% ± 4.4%; p < 0.0001). One case with in utero cessation had a PCT density of 34.2% ± 0.2%. Among other AA fetopathy findings, 1 case demonstrated unilateral renal vein thrombosis and 4 had hypocalvaria. Conclusions We have quantified the reduction in AA fetopathy PCT density, and demonstrated in utero cessation may recover PCT differentiation. Future studies may benefit from calculating PCT percentage as a potential biomarker to correlate with post-natal renal function and maternal factors including medication type, dosage, duration, and time from medication cessation.
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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 | 0.001 |
| 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 | 0.000 |
| 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".