Histomorphologic Analysis of the Late-term Rat Fetus and Placenta
Bibliographic record
Abstract
Histological examination of the rat placenta and fetus is uncommon. Toxicological studies mainly rely on gross examination of the fetus and on fetal and placental weights. These are often insufficient to assess the fetal and placental toxicity of xenobiotics. The small size of the fetus makes its dissection labor-intensive. Thus, our objective was to develop a simple and accurate technique to evaluate the rat fetus and placenta. Sprague-Dawley rat fetuses at gestational day 19.5 ( n = 18) and their placentas ( n = 32) were fixed in formalin. Placentas were cut transversally in the center. Fetuses were cut following a freehand whole-body serial sectioning diagram adapted from Wilson’s method. Sections were stained with hematoxylin–eosin–phloxine–saffron, and histomorphometry was used to measure the area of the fetal placental region (27.2 ± 1.7 mm 2 ), including the labyrinth (22.2 ± 1.0 mm 2 ) and the basal zone (4.8 ± 0.8 mm 2 ). Our whole-fetus serial sectioning technique resulted in 12 precise cutting planes that fit on 3 histological slides, enabling the examination of most organs without labor-intensive dissection. Quantitative analysis of placental areas improves the understanding of the pathogenesis of treatment-related changes. This technique provides a standardized method for future research in pertinent fields such as developmental biology and toxicology.
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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 | 0.000 |
| 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".