A systematic characterization of the factors influencing polymerization and dynamic behavior of n-butyl cyanoacrylate
Bibliographic record
Abstract
INTRODUCTION: Brain arteriovenous malformations are abnormal connections between arteries and veins without an intervening capillary bed. Endovascular glue embolization with N-butyl cyanoacrylate (NBCA) is an accepted form of treatment. The reported complication rates vary widely from 2% to 15%, and timing of polymerization appears to play a major role. Additionally, the interaction between NBCA and vessel surface as well as the presence of biological catalysts are poorly understood. METHODS: Polymerization time was measured for mixtures of Lipiodol/NBCA of 50/50, 70/30, and 60/40. The influence of pH, temperature, and the presence of biological catalysts on polymerization time was investigated. Contact angles were measured on polyvinyl alcohol cryogel (PVA-C), silicone, and endothelial surfaces in a submerged aqueous environment to assess physical surface interactions. High speed video analysis of glue injection through a microcatheter was performed to characterize simulated coaxial flow. RESULTS: NBCA polymerization rate increased with pH and temperature. A hydrophilic surface such as PVA-C was better than silicone at mimicking the physical properties of endothelium. Live endothelium provided a catalytic surface that at least doubled the rate of polymerization. Blood products further increased the polymerization rate in the following order (slowest to fastest): plasma, platelets, red blood cells (RBCs), and lysed RBCs. These factors could explain the discrepancy between in vitro and in vivo results reported in the current literature. High speed video analysis of NBCA injection showed dripping to jetting transition with significant wall effect which deviated from previous ideal assumptions. CONCLUSIONS: The determinants of NBCA polymerization rate are multifactorial and dependent mainly on the presence of biological catalysts coupled with flow related wall interaction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".