Three‐dimensional capillary geometry in gut tissue
Bibliographic record
Abstract
Accurate characterization of capillary geometry is of the utmost importance for physiological tissue studies such as oxygen transport. We show that 3D microscopy can be used to measure tissue capillary geometry both in normal and disease states. We imaged fluorescently labeled gut mucosa capillary beds of three control rats and three rats 4 hours after i.p. injection of 9 mg/kg endotoxin. We used serial optical sectioning microscopy coupled with deconvolution to reconstruct 3D capillary geometry. Theoretical point spread functions accounting for depth into the specimen resulted in better reconstructions than experimentally measured point spread functions. We next derived the distribution of the shortest distances to the nearest capillary from all extravascular tissue voxels. In normal rats the shortest-distance distributions were remarkably constant despite widely varying capillary geometry. Furthermore, the mean of the shortest-distance distributions increased significantly for endotoxemic rats (4.8+/-0.4 microm) compared to controls (4.3+/-0.3 microm, P<0.05). Hence, serial sectioning microscopy provides an accurate venue for measuring physiologically relevant 3D capillary structure.
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 | 0.000 | 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 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".