Lower limb vascular conductance and resting popliteal blood flow during head‐up and head‐down postural challenges
Bibliographic record
Abstract
This study hypothesized that central and local reflex mechanisms affecting vascular conductance (VC) through the popliteal artery compensated for the reduction in muscle perfusion pressure (MPP) to maintain popliteal blood flow (PBF) during head-down tilt (35˚ HDT), but not in head-up tilt (45˚ HUT). Resting measurements were made on 15 healthy men in prone position to facilitate the access to the popliteal artery, on two separate days in random order during horizontal (HOR), HDT or HUT. In each body position, the body was supported, and the ankles were maintained in relaxed state so that there was no muscle tension, as with normal standing. Popliteal blood flow velocity and popliteal arterial diameter were measured by ultrasound, and PBF was calculated. MPP was corrected to mid-calf from measured finger cuff pressure, and VC was estimated by dividing PBF by MPP. The MPP in HDT (48 ± 2 mmHg) was ~100mmHg less than in HUT (145 ± 2 mmHg). PBF was similar between HOR (51 ± 18 ml( ) min(-1) ) and HDT (47 ± 13 ml min(-1) ), but was lower in HUT (30 ± 9 ml min(-1) ). VC was different between HDT (1·0 ± 0·3 ml min(-1) mmHg(-1) ), HOR (0·6 ± 0·2 ml min(-1) mmHg(-1) ) and HUT (0·2 ± 0·1 ml min(-1) mmHg(-1) ). In conclusion, the interactions of central and local regulatory mechanisms resulted in a disproportionate reduction of VC during HUT lowering PBF even though MPP was higher, while in HDT, increased VC contributed to maintain PBF at the same level as the HOR control condition.
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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 | 0.001 |
| 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".