Transvascular fluid flux from the pulmonary vasculature at rest and during exercise in horses
Bibliographic record
Abstract
Exercise causes changes in pulmonary haemodynamics through redistribution of blood flow, increase in the pulmonary surface area, and increase in pulmonary vascular pressures. These changes contribute to the increase in fluid exchange across the alveolar-capillary barrier. To determine the extent of the fluid exchange across the alveolar-capillary barrier at rest and during exercise, six horses were exercised on a high-speed treadmill until fatigue. Arterial and mixed venous blood were sampled at rest and during exercise and recovery. Blood volume changes across the lung (DeltaBV; measured in percentage) were calculated from changes in plasma protein and haemoglobin concentration, and haematocrit. Cardiac output (Q) was calculated using the Fick equation. Fluid flux (J(V-A); measured in l min(-1)) across the alveolar-capillary barrier was then quantified based on Q and DeltaBV. At rest, no fluid movement occurred across the pulmonary vasculature (0.6 +/- 0.6 l min(-1)). During exercise, the amount of fluid moved from the pulmonary circulation was 8.3 +/- 1.3 l min(-1) at 1 min, 6.4 +/- 2.9 l min(-)(1) at 2 min, 10.1 +/- 1.0 l min(-1) at 3 min, 12.9 +/- 2.5 l min(-1) at 4 and 9.6 +/- 1.5 l min(-1) at fatigue (all P < 0.0001). Erythrocyte volume decreased by 6% (P < 0.01) across the lungs, which decreased the colloid osmotic gradient in the pulmonary vasculature. Decrease colloid osmotic gradient along with increased hydrostatic forces in the pulmonary vasculature would enhance displacement of fluid into the pulmonary interstitium. In conclusion, exercise caused large increases in transpulmonary fluid fluxes in horses. Here, we present a simple method to calculate transpulmonary fluid fluxes in different species, which can be used to elucidate mechanisms of lung fluid balance in vivo.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".