Development of a novel in situ model for visualizing murine endometrial vasculature using fluorescence intravital microscopy
Bibliographic record
Abstract
Uterine vascular modification is essential for a healthy pregnancy. However, the mechanisms underlying this modification are yet to be elucidated, and there is a significant lack of functional data regarding these transient vessels. Functional quantification such as vasoreactivity, permeability parameters and blood flow dynamics is crucial to understanding these unique vessels and requires clearly defined vessel wall identification. Therefore, we developed an in situ murine model using intravital microscopy to observe endometrial blood vessels over the period of modification. Mice were anaesthetized and the pregnant uterus (gestation day 8–12) exteriorized. The anti‐mesometrial uterine wall was incised and retracted and the fetal tissue removed. The placenta was then placed fetal‐side down and superfused with physiological buffered‐superfusate. Endometrial vasculature was visualized via transillumination or fluorescence microscopy using fluorescent markers for membrane expressed glycoproteins on endothelial cells using 0.1M Lycopersicon (FITC) or 0.067M Isolectin (Alexa‐Fluor 488). Transillumination allowed for visualization of superficial vessels with limited resolution. Isolectin and Lycopersicon staining were successful in fluorescing the endothelium. There was increased background fluorescence with Lycopersicon which was not seen with Isolectin. We have successfully developed an in situ preparation for visualizing endometrial vasculature. Isolectin provides ideal staining resolution for functional characterization of these unique vessels. This work was supported by CIHR and NSERC.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".