Quantifying Branching Density in Rat Mammary Gland Whole-mounts Using the Sholl Analysis Method
Bibliographic record
Abstract
An increasing number of studies are utilizing the rodent mammary gland as an endpoint for assessing the developmental toxicity of a chemical exposure. The effects these exposures have on mammary gland development are typically evaluated using either basic dimensional measurements or by scoring morphological characteristics. However, the broad range of methods for interpreting developmental changes could lead to inconsistent translations across laboratories. A common method of assessment is needed so that proper interpretations can be formed from data being compared across studies. The present study describes the application of the Sholl analysis method to quantify mammary gland branching characteristics. The Sholl method was originally developed for use in quantifying neuronal dendritic patterns. By using ImageJ, an open-source image analysis software package, and a plugin developed for this analysis, the mammary gland branching density and the complexity of a mammary gland from a peripubertal female rat were determined. The methods described here will enable the use of the Sholl analysis as an effective tool for quantifying an important characteristic of mammary gland development.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".