Estimation of Endothelin‐Mediated Vasoconstriction in Acute Pulmonary Thromboembolism
Bibliographic record
Abstract
We aimed to investigate the role of endothelin-mediated vasoconstriction following acute pulmonary thromboembolism (APTE). Thirteen anesthetized piglets (~25 kg) were ventilated with 0 PEEP. Cardiac output (Qt) and wedge pressure (Pw) were measured by a Swan Ganz catheter, along with arterial and venous blood gases. APTE was induced by autologous blood clots (~0.8 g/kg, 12-16 pieces) via a jugular venous catheter at time = 0 minutes until the mean pulmonary arterial pressure (Ppa) was about 2.5 times the baseline at 30 minutes. Eight control animals (Group 1) received only normal saline afterward, while the remaining five (Group 2) received at time = 40-minute saline plus Tezosentan, a nonspecific endothelin antagonist. The drug was initially given as an intravenous bolus (10 mg/kg), followed by an infusion (2 mg/min) until the end of the experiment at 2 hours. Hemodynamic data were measured before APTE and then at 30-minute intervals. Pulmonary vascular resistance index (PVRI) was calculated as (Ppa-Pw)/CI, where CI was cardiac index or Qt/W (body weight). Fluorescent microspheres (FMS) were used to mark regional blood flows and ventilation for cluster analysis. PVRI acutely increased within minutes and remained high despite some recovery over time. With Tezosentan treatment, the results showed that endothelin-mediated vasoconstriction persisted significantly up to 2 hours and accounted for about 25% of the increase in PVRI while clot obstruction accounted for the remaining 75%. CI remained relatively constant throughout. Tezosentan also affected PVRI indirectly by mitigating the shift of regional blood flow back to the embolized areas over time, possibly by attenuating vasoconstriction in the nonembolized areas. We conclude that following APTE, although the increased PVRI is mostly due to mechanical embolic obstruction, secondary factors such as vasoconstriction and pattern of regional blood flow over time also play important roles.
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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 | 0.000 |
| 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".