The effects of nitric oxide on coagulation and inflammation in ex vivo models of extracorporeal membrane oxygenation and cardiopulmonary bypass
Bibliographic record
Abstract
BACKGROUND: Extracorporeal life support (ECLS) has extensive applications in managing patients with acute cardiac and pulmonary failure. Two primary modalities of ECLS, cardiopulmonary bypass (CPB) and extracorporeal membrane oxygenation (ECMO), include several similarities in their composition, complications, and patient outcomes. Both CPB and ECMO pose a high risk of thrombus formation and platelet activation due to the large surface area of the devices and bleeding due to system anticoagulation. Therefore, novel methods of anticoagulation are needed to reduce the morbidity and mortality associated with extracorporeal support. Nitric oxide (NO) has potent antiplatelet properties and presents a promising alternative or addition to anticoagulation with heparin during extracorporeal support. METHODS: We developed two ex vivo models of CPB and ECMO to investigate NO effects on anticoagulation and inflammation in these systems. RESULTS: Sole addition of NO as an anticoagulant was not successful in preventing thrombus formation in the ex vivo setups, therefore a combination of low-level heparin with NO was used. Antiplatelet effects were observed in the ex vivo ECMO model when NO was delivered at 80 ppm. Platelet count was preserved after 480 min when NO was delivered at 30 ppm. CONCLUSION: Combined delivery of NO and heparin did not improve haemocompatibility in either ex vivo model of CPB and ECMO. Anti-inflammatory effects of NO in ECMO systems have to be evaluated further.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".