Mapping 3-D functional capillary geometry in rat skeletal muscle in vivo
Bibliographic record
Abstract
We have developed a novel mapping software package to reconstruct microvascular networks in three dimensions (3-D) from in vivo video images for use in blood flow and O2 transport modeling. An intravital optical imaging system was used to collect video sequences of blood flow in microvessels at different depths in the tissue. Functional images of vessels were produced from the video sequences and were processed using automated edge tracking software to yield location and geometry data for construction of the 3-D network. The same video sequences were analyzed for hemodynamic and O2 saturation data from individual capillaries in the network. Simple user-driven commands allowed the connection of vessel segments at bifurcations, and semiautomated registration enabled the tracking of vessels across multiple focal planes and fields of view. The reconstructed networks can be rotated and manipulated in 3-D to verify vessel connections and continuity. Hemodynamic and O2 saturation measurements made in vivo can be indexed to corresponding vessels and visualized using colorized maps of the vascular geometry. Vessels in each reconstruction are saved as text-based files that can be easily imported into flow or O2 transport models with complete geometry, hemodynamic, and O2 transport conditions. The results of digital morphometric analysis of seven microvascular networks showed mean capillary diameters and overall capillary density consistent with previous findings using histology and corrosion cast techniques. The described mapping software is a valuable tool for the quantification of in vivo microvascular geometry, hemodynamics, and oxygenation, thus providing rich data sets for experiment-based computational models.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".