Особенности взаимодействия in vitro остеобластоподобных клеток MG-63 с поверхностью сплавов системы Ti-ZrNb, обладающих памятью формы
Bibliographic record
Abstract
The paper studies the influence of the Ti-Zr-Nb (TZN) shape memory alloys surface on adhesion, proliferation, viability and actin cytoskeleton organization of osteoblast-like cells MG-63. The studied materials have a unique combination of mechanical properties that determine their prospects for creating bone implants with high biomechanical compatibility: low value of the Young’s modulus and superelastic behavior, similar to the behavior of bone tissue. We used thin plates of the experimental alloy TZN and the Ti-Al-Nb medical alloy (TAN) as a control material. A study of the growth dynamics of the MG-63 cell culture was made using the MTT test and counting the number of nuclei per unit area using scanning microscopy. It was found that on 4 and 7 days the number of cells on the TZN alloy is higher than on the TAN alloy. This may be due to the influence of the qualitative and quantitative composition of materials on the surface microstructure and chemistry. The viability over the cultivation time was close to 100% on both alloys. The analysis of cytoskeleton images showed the predominance of fibrillary actin on samples of the TZN system, as well as the organization characteristic of fibroblast-like polygonal cells.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".