Brain and Placental Pathology in Fetal COL4A1 Related Disease
Bibliographic record
Abstract
Introduction Although fetal brain injury due to COL4A1 gene mutation is well documented, fetal central nervous system (CNS) and placental histopathology lack description. We report CNS and placental pathology in fetal cases with symptomatic COL4A1 mutation. Methods We retrieved four autopsy cases of COL4A1 related disease, confirmed by genetic sequencing after fetal brain injury was detected. Results One case was a midgestation fetus with residua of ventricular zone hemorrhage and normal placental villi. Three cases were 30-32 week gestation fetuses: two demonstrated CNS small vessel thrombosis, with CNS injury. Both demonstrated high grade placental fetal vascular malperfusion (FVM). One additionally showed villous dysmorphism, the other demonstrated mild villous immaturity. The fetus whose placenta demonstrated high grade FVM was growth restricted. A fourth fetus demonstrated schizencephaly with a CNS arteriopathy with smooth muscle cell degeneration and cerebral infarcts; the placenta demonstrated severe villous dysmorphism and low grade FVM. Discussion These cases confirm that small vessel disease is important in producing intracranial pathology in COL4A1mutation. We report an arteriopathy distinct from microvascular thrombosis and demonstrate that placental pathology is common in fetal COL4A1 related disease. This tentatively suggests that placental pathology may contribute to CNS abnormalities by affecting circulatory sufficiency.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".