Novel Surfaces in Extracorporeal Membrane Oxygenation Circuits
Bibliographic record
Abstract
The balance between systemic anticoagulation and clotting is challenging. In normal hemostasis, the endothelium regulates the balance between anticoagulant and prothrombotic systems. It becomes particularly more challenging to maintain this physiologic hemostasis when we are faced with extracorporeal life support therapies, where blood is continuously in contact with a foreign extracorporeal circuit surface predisposing a prothrombotic state. The blood-surface interaction during extracorporeal life support therapies requires the use of systemic anticoagulation to decrease the risk of clotting. Unfractionated heparin is the most common anticoagulant agent widely used in this setting. New trends include the use of direct thrombin inhibitor agents for systemic anticoagulation; and surface modifications that aim to overcome the blood-biomaterial surface interaction by modifying the hydrophilicity or hydrophobicity of the polymer surface; and coating the circuit with substances that will mimic the endothelium or anti-thrombotic agents. To improve hemocompatibility in an extracorporeal circuit, replication of the anti-thrombotic and anti-inflammatory properties of the endothelium is ideal. Surface modifications can be classified into three major groups: biomimetic surfaces (heparin, nitric oxide, and direct thrombin inhibitors); biopassive surfaces [phosphorylcholine, albumin, and poly- 2-methoxyethylacrylate]; and endothelialization of blood contacting surface. The focus of this paper will be to review both present and future novel surface modifications that can obviate the need for systemic anticoagulation during extracorporeal life support therapies.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".