Flow mediated dilation response to oscillatory vs. steady shear: evidence for the transduction of the mean shear stimulus
Bibliographic record
Abstract
OBJECTIVE: 1) To compare the flow mediated dilation (FMD) response to an increase in brachial artery (BA) shear stress (SS) via forearm heating (FH) vs. forearm exercise (FE). 2) To isolate the effects of the oscillatory pattern of exercise SS. BA diameter (BAD) and mean blood velocity (MBV) were measured with ultrasound in 16 healthy subjects. BA MBV was elevated to the same mean level for 10min via 3 protocols. 1) FHstdy ‐ A steady MBV was created by controlled release of BA compression upstream of heat‐induced forearm vasodilation. 2) FHosc: rhythmic cuff inflation simulated exercise induced oscillations in MBV. 3) FE ‐ Rhythmic isometric handgrip exercise. RESULTS: The mean increase in shear rate (MBV/BAD) (±SD) was the same in all conditions (FHstdy: 52.2±14.1s −1 ; FHosc: 51.7±15.7s −1 ; FE: 50.1±13.0s −1 P=0.13). Neither the percent change in BAD (End‐trial: FHstdy: 7.0±3.0%; FHosc: 7.8±3.4%; FE: 6.7±3.0% P=0.20) nor the initial response speed (tau‐time to 63% of max response) (FHstdy:27.8±14.4s; FHosc: 22.6±16.7s; FE:25.9±23.3s P=0.69) were different between conditions. CONCLUSION: The FMD was determined by the mean shear independent of stimulus pattern (oscillatory vs. steady) and mode of increase (heating vs. exercise). These data suggest that the endothelium transduces the mean shear stimulus and indicate that exercise may provide a viable technique to investigate human FMD. Funded by NSERC, ACSM and HSFC
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".