Mechanisms of Nickel-free Stainless Steel Induced Apoptosis in Vascular Endothelial Cells
Bibliographic record
Abstract
Objective To investigate the effects of high nitrogen nickel-free austenitic stainless steel(HNNF SS)on the apoptosis induction of vascular endothelial cells and develop a novel biomaterial for circumventing the in-stent restenosis. Methods Human umbilical vein endothelial cells(HUVECs)were growed on the HNNF SS and general 316 L stainless steel(316L SS). Annexin V-FITC and Propidium Iodide were employed to detect the apoptosis by flow cytometric analysis. The expression of apoptosis.related genes and microRNA were also examined by quantitative real.time PCR(qRT-PCR). Results Flow cytometry analysis revealed that 316 L SS could activate the cellular apoptosis more efficiently than HNNF SS(P 0.05). At the molecular level,qRT-PCR results showed that the cell apoptosis related genes were overexpressed on 316 L SS(P 0.05),including Fas,Caspase-3,and Caspase-8. The overexpression of miR-133 a,186,210,34 a and 124 was also observed in HUVECs growing on the 316 L SS and HNNF SS materials(P 0.05). Conclusion The mechanism of 316 L SS triggering cell apoptosis might be related to the releasing nickel inducing cell apoptosis via Fas-Caspase-8-Caspase-3 exogenous pathway,which is modulated by altered microRNA expression;the RT-PCR results also showed the expression variation of related genes and miRNA. Our results suggested that HNNF SS is better than the 316 L SS material in the aspect of avoiding cell apoptosis and circumventing the in-stent restenosis.
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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".