Induction of microvascular network growth in the mouse mesentery
Bibliographic record
Abstract
OBJECTIVE: Motivated by observations of mesenteries harvested from mice treated with tamoxifen dissolved in oil for inducible gene mutation studies, the objective of this study was to demonstrate that microvascular growth can be induced in the avascular mouse mesentery tissue. METHODS: C57BL/6 mice were administered an IP injection for five consecutive days of: saline, sunflower oil, tamoxifen dissolved in sunflower oil, corn oil, or peanut oil. RESULTS: Twenty-one days post-injection, zero tissues from saline group contained branching microvascular networks. In contrast, all tissues from the three oils and tamoxifen groups contained vascular networks with arterioles, venules, and capillaries. Smooth muscle cells and pericytes were present in their expected locations and wrapping morphologies. Significant increases in vascularized tissue area and vascular density were observed when compared to saline group, but sunflower oil and tamoxifen group were not significantly different. Vascularized tissues also contained LYVE-1-positive and Prox1-positive lymphatic networks, indicating that lymphangiogenesis was stimulated. When comparing the different oils, vascularized tissue area and vascular density of sunflower oil were significantly higher than corn and peanut oils. CONCLUSIONS: These results provide novel evidence supporting that induction of microvascular network growth into the normally avascular mouse mesentery is possible.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".