Influence of Titanium Surface Charge on Fibroblast Adhesion
Bibliographic record
Abstract
BACKGROUND: Although dental implants have a high success rate, failure owing to the absence of adhesion between the gingival connective tissue and the implant surface is still being reported. PURPOSE: This study was designed to evaluate the effect of a titanium surface charge on fibroblast adhesion. MATERIAL AND METHODS: An electrical chamber was custom-made to generate negative and positive surface charges on commercially pure titanium cylinders with a potential difference of 4.5 V. Twenty-seven titanium cylinders were divided into three experimental groups. In each group, cell attachment to a positively charged titanium cylinder, a negatively charged titanium cylinder, and a titanium cylinder (control) was studied at three time intervals of 15, 30, and 60 minutes. NCTC clone 929 fibroblasts were used in these experiments. The effect of the potential difference in the pH of Dulbecco's Modified Eagle Medium (DMEM) was also evaluated using two new specimens at time intervals of 15, 30, 60, and 80 minutes. RESULTS: The fibroblast cell attachment was more statistically significant to the positively charged titanium cylinder than the negatively charged titanium cylinder (p =.002) and the control (p=.000), whereas the cell adhesion difference between the control and the negatively charged titanium cylinder was not statistically significant (p=.808). The range of pH difference of the DMEM in the negative and positive parts of the electrical chamber was 0.46 and 0.30, respectively. CONCLUSION: Within the limitations of this in vitro study, the positive surface charge of the titanium cylinder results in significantly favorable cell adhesion.
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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 | 0.001 |
| 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".