Biological responses of human bone mesenchymal stem cells to Ti and TiZr implant materials
Bibliographic record
Abstract
Abstract Background Titanium‐zirconium alloy (TiZr1317) is a new material used for biological implants. There are several studies on the effects of TiZr implants on the biological characteristics of human bone mesenchymal stem cells (hBMSCs). Purpose The purpose of this study was to investigate the biological responses of hBMSCs to implant holes affected by the physicochemical properties of oral implants (TiSLA, TiSLActive, TiZrSLA, and TiZrSLActive). Materials and Methods Grade 4 Ti and TiZr (13‐17% Zr) substrates were modified by sand‐blasted large‐grit acid‐etched (SLA) or hydrophilic sand‐blasted large‐grit acid‐etched (SLActive), resulting in four types of surface with complex microstructures corresponding to the commercially‐available implants SLA, RoxolidSLA, SLActive, and RoxolidSLActive (Institute Straumann AG, Basel, Switzerland). Physicochemical properties were detected and the biological responses of hBMSCs were observed. Results Surface morphology characterization by scanning electron microscopy and atomic force microscopy revealed differences between the four groups. SLActive had higher surface energy/wettability than SLA, indicating that increased surface energy/wettability can promote the absorption of osteogenic proteins and enhance osseointegration. hBMSCs seeded on SLActive substrates exhibited better performance in terms of cell attachment, proliferation and osteoblastic differentiation than cells seeded on SLA. Conclusion Because of their more suitable physicochemical properties, TiSLActive and TiZrSLActive materials demonstrated more pronounced effects on the biological responses of hBMSCs compared with TiSLA and TiZrSLA.
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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".