Vestibular effects on relative arterial blood flow to and venous return from the limbs during postural changes of conscious felines
Bibliographic record
Abstract
Prior studies showed that vestibular signals influence cardiovascular regulation by eliciting vasoconstriction in the dependent limbs during postural changes. These findings led to the hypothesis that loss of vestibular inputs would exacerbate blood pooling in the body regions below the heart during alterations of body position. We tested this hypothesis by comparing blood flow measured using transit‐time ultrasound technology from the femoral vein and artery in conscious cats subjected to head‐up tilt (HUT) at amplitudes up to 60°. Responses were recorded before and after bilateral ablation of vestibular afferents. Before vestibular lesions, blood flow decreased 24‐35% in the femoral artery by ≈9 sec following 60° HUT, presumably due to vasoconstriction, while venous blood flow decreased 55‐65%. After lesions, arterial blood flow dropped only 0‐14% at the time maximal vasoconstriction previously occurred, while venous blood flow decreased 57‐87%. The alterations were shown to be significant by ANOVA (P<0.05). Because vestibular lesions resulted in more blood flow to the leg in the femoral artery and less venous return in the femoral vein, we concluded that the lesions resulted in hindlimb blood pooling during HUT.
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 | 0.001 |
| 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 | 0.001 |
| 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".