Plasma Protein Interference at the Dentin-Adhesive Interface
Bibliographic record
Abstract
Objective: The purpose of this study is to identify and confirm the presence of macromolecules and their mechanism that interferes with the dentin-adhesive interface. Despite, several advancements of adhesive systems, the bonded interface remains the weakest area of tooth-coloured restorations”. Our goal is to explore the use of chemical analysis via Fourier Transformed Infra-Red (FTIR) to identify bonds formed between adhesive and dentinal fluid protein. Materials & Methods: The protein solutions prepared in this experiment were the Albumin solution (45mg/mL dilution) and IgG solution (25mg/mL dilution) via 0.01M phosphate buffer solution creating constant concentrations as per human plasma. The adhesive systems used in this experiment were Elekta (BisGMA), All- Bond (MDP), Clear-Fill (MDP+MPDP). The experimentation concentrated on the three adhesive types (cured before contact) and then placed in time conditioned contact with protein solution for interactions of instant, 1min, 2min, and 5min. Chemical analysis was completed via FTIR Analysis Infrared spectra (FTIR) using a Thermo Scientific Nicolet™ 6700; in order to understand and identify the bonds formed between adhesive and dentinal fluid protein. Results: Our results show that albumin was detected after 2 minutes in all SEA samples, and IgG after 1 min for ClearFill, 2min for Elekta, and 5 min for Allbond. The detection identifies that a protein coating formation exists on the adhesive surfaces over time, which validates our hypothesis. Conclusion: Serum proteins react with the adhesive surface dependently on the adhesive composition.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".