Mapping of the functional microcirculation in vital organs using contrast-enhanced in vivo video microscopy
Bibliographic record
Abstract
A functional microcirculation is vital to the survival of mammalian tissues. In vivo video microscopy is often used in animal models to assess microvascular function, providing real-time observation of blood flow in normal and diseased tissues. To extend the capabilities of in vivo video microscopy, we have developed a contrast-enhanced system with postprocessing video analysis tools that permit quantitative assessment of microvascular geometry and function in vital organs and tissues. FITC-labeled dextran (250 kDa) was injected intravenously into anesthetized mice to provide intravascular fluorescence contrast with darker red blood cell (RBC) motion. Digitized video images of microcirculation in a variety of internal organs (e.g., lung, liver, ovary, and kidney) were processed using computer-based motion correction to remove background respiratory and cardiac movement. Stabilized videos were analyzed to generate a series of functional images revealing microhemodynamic parameters, such as plasma perfusion, RBC perfusion, and RBC supply rate. Fluorescence contrast revealed characteristic microvascular arrangements within different organs, and images generated from video sequences of liver metastases showed a marked reduction in the proportion of tumor vessels that were functional. Analysis of processed video sequences showed large reductions in vessel volume, length, and branch-point density, with a near doubling in vessel segment length. This study demonstrates that postprocessing of fluorescence contrast video sequences of the microcirculation can provide quantitative images useful for studies in a wide range of model systems.
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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".