Evaluation of cell adhesion and osteoconductivity in bone substitutes modified by polydopamine
Bibliographic record
Abstract
Bones damaged due to disease or accidents can be repaired in different ways. Tissue engineering has helped with scaffolds made of different biomaterials and various methods. Although all kinds of biomaterials can be useful, sometimes their weakness in cellular activity or osteoconductivity prevents their optimal use in the fabrication of bone scaffolds. To solve this problem, we need additional processes, such as surface modification. One of the common methods is coating with polydopamine. Polydopamine can not only cover the weakness of the scaffolds in terms of cellular properties, but it can also create or increase osteoconductivity properties. Polydopamine creates a hydrophilic layer on the surface of scaffolds due to a large number of functional groups such as amino and hydroxyl groups. This layer allows bone cells to anchor and adheres well to the surfaces. In addition, it creates a biocompatible environment for proliferation and differentiation. Besides, the polydopamine coating makes the surfaces chemically active by catechol and amine group, and as a result of their presence, osteoconductivity increases. In this mini-review, we investigated the characteristics, structure, and properties of polydopamine as a modifier of bone substitutes. Finally, we evaluated the cell adhesion and osteoconductivity of different polydopamine-modified bone scaffolds.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".