Comparison of endothelial cell attachment on surfaces of biodegradable polymer-coated magnesium alloys in a microfluidic environment
Bibliographic record
Abstract
Polymeric coatings can provide temporary stability to bioresorbable metallic stents at the initial stage of deployment by alleviating rapid degradation and providing better interaction with surrounding vasculature. To understand this interfacing biocompatibility, this study explored the endothelial-cytocompatibility of polymer-coated magnesium (Mg) alloys under static and dynamic conditions compared to that of non-coated Mg alloy surfaces. Poly (carbonate urethane) urea (PCUU) and poly (lactic-co-glycolic acid) (PLGA) were coated on Mg alloys (WE43, AZ31, ZWEKL, ZWEKC) and 316L stainless steel (316L SS, control sample), which were embedded into a microfluidic device to simulate a vascular environment with dynamic flow. The results from attachment and viability tests showed that more cells were attached on the polymer-coated Mg alloys than on non-coated Mg alloys in both static and dynamic conditions. In particular, the attachment and viability on PCUU-coated surfaces were significantly higher than that of PLGA-coated surfaces of WE43 and ZWEKC in both static and dynamic conditions, and of AZ31 in dynamic conditions (P<0.05). The elementary distribution map showed that there were relatively higher Carbon weight percentages and lower Mg weight percentages on PCUU-coated alloys than PLGA-coated alloys. Various levels of pittings were observed underneath the polymer coatings, and the pittings were more severe on the surface of Mg alloys that corroded rapidly. Polymer coatings are recommended to be applied on Mg alloys with relatively low corrosion rates, or after pre-stabilizing the substrate. PCUU-coating has more selective potential to enhance the biocompatibility and mitigate the endothelium damage of Mg alloy stenting.
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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) | 0.000 | 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 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".