Performing Vaginal Lavage, Crystal Violet Staining, and Vaginal Cytological Evaluation for Mouse Estrous Cycle Staging Identification
Bibliographic record
Abstract
A rapid means of assessing reproductive status in rodents is useful not only in the study of reproductive dysfunction but is also required for the production of new mouse models of disease and investigations into the hormonal regulation of tissue degeneration (or regeneration) following pathological challenge. The murine reproductive (or estrous) cycle is divided into 4 stages: proestrus, estrus, metestrus, and diestrus. Defined fluctuations in circulating levels of the ovarian steroids 17-β-estradiol and progesterone, the gonadotropins luteinizing and follicle stimulating hormones, and the luteotropic hormone prolactin signal transition through these reproductive stages. Changes in cell typology within the murine vaginal canal reflect these underlying endocrine events. Daily assessment of the relative ratio of nucleated epithelial cells, cornified squamous epithelial cells, and leukocytes present in vaginal smears can be used to identify murine estrous stages. The degree of invasiveness, however, employed in collecting these samples can alter reproductive status and elicit an inflammatory response that can confound cytological assessment of smears. Here, we describe a simple, non-invasive protocol that can be used to determine the stage of the estrous cycle of a female mouse without altering her reproductive cycle. We detail how to differentiate between the four stages of the estrous cycle by collection and analysis of predominant cell typology in vaginal smears and we show how these changes can be interpreted with respect to endocrine status.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".