Influence of bio‐aging on corrosion behavior of different implant materials
Bibliographic record
Abstract
BACKGROUND: Dental implants and abutments are exposed to challenging oral environment. Corrosion of these materials can affect the overall performance of titanium implants. PURPOSE: To investigate the effects of biofilm-induced bio-aging on corrosion behavior of different implant materials surface. MATERIALS AND METHODS: Commercial polished titanium (Polish), sand-blasted, large grit, acid-etched surface treated titanium (SLA), microarc oxidation (MAO), and hydroxyapatite (HA) coated titanium were bio-aged with saliva biofilm for 30 days. Titanium surfaces topography, chemical composition, roughness, and water contact angle changes were evaluated. In addition, human gingival fibroblasts (HGFs) adhesion, Streptococcus sanguinis (S. sanguinis) biofilm formation were determined. RESULTS: Surface topography, roughness, and chemical composition have no significant changes for all groups after bio-aging (P > .05). Water contact angle of bio-aged SLA was greatly increased (P < .05). While other groups showed no sign of change (P > .05). Adhesion and proliferation of HGFs on the bio-aged SLA titanium surfaces were decreased (P < .05), but increased on bio-aged Polish and HA titanium (P < .05). S. sanguinis biofilm viability was promoted with bio-aging in HA group (P < .05). CONCLUSIONS: Biological characteristics of Polish, SLA, and HA titanium surfaces were influenced by bio-aging. While MAO group was relatively resistant to saliva biofilm bio-aging.
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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".