A new method for assessing arteriolar diameter and hemodynamic resistance using image analysis of vessel lumen
Bibliographic record
Abstract
To characterize the nonuniform diameter response in a blood vessel after a given stimulus (e.g., arteriolar conducted response), frequent serial diameter measurements along the vessel length are required. We used an advanced image analysis algorithm (the "discrete dynamic contour") to develop a quick, reliable method for serial luminal diameter measurements along the arteriole visualized by intravital video microscopy. With the use of digitized images of the arteriole and computer graphics, the method required an operator to mark the image of the two inner edges of the arteriole at several places along the arteriolar length. The algorithm then "filled in" these marks to generate two continuous contours that "hugged" these edges. A computer routine used these contours to determine luminal diameters every 20 microm. Based on these diameters and on Poiseuille's law, the routine also estimated the hemodynamic resistance of the blood vessel. To demonstrate the usefulness of the method, we examined the character of spatial decay of KCl-induced conducted constriction along approximately 500-microm-long arteriolar segments and the KCl-induced increase in hemodynamic resistance computed for these segments. The decay was only modestly fitted by a simple exponential, and the computed increase in resistance (i.e., 5- to 70-fold) was only modestly predicted by resistance increase based on our mathematical model involving measurements at two arteriolar sites (Tyml K, Wang X, Lidington D, and Oullette Y. Am J Physiol Heart Circ Physiol 281: H1397-H1406, 2001). We conclude that our method provides quick, reliable serial diameter measurements. Because the change in hemodynamic resistance could serve as a sensitive index of conducted response, use of this index in studies of conducted response may lead to new mechanistic insights on the response.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".