Biological evaluation of femtosecond-laser-textured Fe–Mn alloys for vascular applications
Bibliographic record
Abstract
Biodegradable metals represent a valuable solution for the development of temporary vascular implants. These are expected to dissolve in the body over time, avoiding side effects typical of permanent implants, such as thrombosis, in-stent restenosis and chronic inflammation. Iron (Fe)-based alloys, such as iron–manganese (Mn) alloys, are of particular interest for cardiovascular applications due to their intrinsic properties. However, their degradation behavior and biological performance need to be improved. Femtosecond (fs)-laser-induced surface topography could affect both their degradation and cell–material interaction. In this work, fs-laser-induced patterning was performed on a Fe–Mn20 alloy to tune both the degradation behavior of the material and its interaction with the biological environment for cardiovascular applications. Processing parameters were varied to select an optimized surface morphology, characterized by linear grooves. Profilometric analysis, scanning electron microscopy and degradation rate analysis were performed on the treated samples. Thereafter, endothelial cell viability tests and hemocompatibility assessment were carried out on selected process conditions. The obtained fs-laser-induced linear patterns were demonstrated to decrease the degradation rate and to improve the biological response toward both endothelial cells and blood. These results demonstrate how fs-laser-induced patterning is a promising solution for the development of biodegradable metal-based vascular implants.
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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) | 0.001 | 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".