New insights and novel perspectives in bileaflet mechanical heart valve prostheses thromboresistance
Bibliographic record
Abstract
Abstract Background Although well-known for their thromboresistance, bileaflet mechanical heart valves (BMHV) require lifelong anti-thrombotic therapy. This must be associated with a certain level of thrombogenicity. Since both thromboresistance and thrombogenicity are explained by the blood-artificial surface or liquid-solid interactions, the aim of the present study was to explore BMHV thromboresistance from new perspectives. The wettability of BMHV pyrolytic carbon (PyC) occluders was investigated in under-liquid conditions. The submerged BMHV wettability clarifies the mechanisms involved in the thromboresistance. Methods The PyC occluders of a SJM Regent™ BMHV were previously laser irradiated, to create a surface hierarchical nano-texture, featuring three nano-configurations. Additionally, four PyC occluders of standard BMHV (Carbomedics, SJM Regent ™ , Bicarbon ™ , On-X ® ), were investigated. All occluders were evaluated in under-liquid configuration, with silicon oil used as the working droplet, while water, simulating blood, was used as the surrounding liquid. The under-liquid droplet-substrate wetting interactions were analyzed using contact angle goniometry. Results All the standard occluders showed very low contact angle, reflecting a pronounced affinity for non-polar molecules. No receding of the contact line could be observed for the untreated occluders. The smallest static contact angle of around 61° could be observed for On-X ® valve (the only valve made of full PyC). The laser-treated occluders strongly repelled oil in underwater conditions. A drastic change in their wetting behaviour was observed depending on the surrounding fluid, displaying a hydrophobic behaviour in the presence of air (as the surrounding medium), and showing instead a hydrophilic nature, when surrounded by water. Conclusions BMHV “fear” water and blood. The intrinsic affinity of BMHV for nonpolar fluids can be translated into a tendency to repel polar fluids, such as water and blood. The blood-artificial surface interaction in BMHV is minimized. The contact between blood and BMHV surface is drastically reduced by polar-nonpolar Van der Waals forces. The “hydro/bloodphobia” of BMHV is intrinsically related to their chemical composition and their surface energy, thus their material: PyC indeed. Pertaining to thromboresistance, the surface roughness does not play a significant role. Instead, the thromboresistance of BMHV lies in molecular interactions. BMHV wettability can be tuned by altering the surface interface, by means of nanotechnology.
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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) | 0.001 | 0.002 |
| 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".