Quantifying Longitudinal Network Geometry Alterations in Rat Skeletal Microvasculature Subject to Ischemia
Bibliographic record
Abstract
Study objective Intra‐vital video microscopy (IVVM) data of rat subcutaneous skeletal muscle microvasculature under ischemic conditions at two time points (0 hours, 3 hours) was obtained from our collaborator. The objectives of this study are to elicit network geometries from the IVVM data and quantify the alterations in the network statistics of the geometries due to ischemia. Hypothesis We hypothesize that ischemia causes blood vessel de‐recruitment. Methods IVVM data from six experiments was obtained, where each experiment provided a video at the start (0 hours) and end (3 hours) of the experiment. Using consecutive still images from the videos, vessel centerlines were manually drawn using Procreate and the NIH software, ImageJ. The included vessels were chosen based on two features: (1) visible red blood cell flow and (2) clearly defined edges. The centerlines were used to guide thresholding by identifying appropriate vessels. This was followed by the generation of binarized representations of the microvascular networks. The network statistics were quantified by measuring vessel segment lengths and diameters, counting the number of bifurcations, and calculating vessel density in ImageJ. Results At 3 hours, the average vessel length changed from 283.91 + 252.14 pixels to 316.75 + 292.37 pixels, the average diameter changed from 16.02 + 7.99 pixels to 15.42 + 7.50 pixels, the average number of bifurcations changed from 32.42 to 18.92 and the average vessel density changed from 0.0085 + 0.0017 to 0.0061 + 0.0024. Conclusion There is a clear decrease in average diameter, vessel density and the number of bifurcations characterizing the microvascular networks. However, an increase in the average vessel length accompanies these findings. The data indicates that ischemia may cause de‐recruitment of blood vessels.
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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 | 0.001 | 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) | 0.001 | 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".