Fractal and Image Analysis of the Microvasculature in Normal Intestinal Submucosa and Intestinal Polyps in <i>Apc</i><sup>Min/+</sup> Mice
Bibliographic record
Abstract
Tumors are supported by the development of a unique vascular bed. We used fractal dimension (Db) and image analysis to quantify differences in the complexity of the vasculature in normal intestinal submucosa and intestinal polyps. Apc(Min/+) mice and wild-type mice were perfused with a curable latex compound, intestines sectioned, and images collected via confocal microscopy. The images were analyzed and area (A), perimeter (P), and integrated optical density (IOD) of the normal and tumor vascular beds were measured. The Db, a quantitative descriptor of morphological complexity, was significantly greater for the polyp vasculature from Apc(Min/+) mice than controls. This indicates that the polyp microvasculature is more chaotic than that of the controls, while the IOD and average vascular density values displayed no differences. This suggests the mass of blood volume is equivalent in normal and polyp microvasculature. The lower vascular area-perimeter ratios expressed by the polyp microvasculature suggest it is composed of smaller, more tortuous vessels. These data demonstrate that fractal analysis is applicable for providing a quantitative description of vascular complexity associated with angiogenesis occurring in normal or diseased tissue. Application of Db, IOD, and average density provides a clearer quantification of the complex morphology associated with tissue microvasculature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".