Exploring the Impact of Biotic and Abiotic Surfaces on Protein Binding Modulation and Bacteria Attachment: Integrating Biological and Mathematical Approaches
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The oral environment is composed of a diverse array of proteins, and any substrate inserted into this habitat promptly becomes subjected to protein adsorption and bacterial colonization. However, the predictive and modulatory nature of implant surfaces coated with salivary pellicle proteomes in microbial adhesion has not been explored using high-throughput techniques. Thus, using human saliva for salivary pellicle adsorption and microbial accumulation, we compared adsorption and community formation on titanium (Ti) biomaterials (implant devices) and dental surfaces (enamel and dentine). The proteomic profile was evaluated by liquid chromatography coupled with tandem mass spectrometry, and the microbiome was assessed using 16S RNA sequencing. Linear discriminant analysis (LDA) and canonical correlation analysis (CCA) were used to quantify variation in analyte amounts and identify likely biomarkers. Substrates were analyzed regarding their physical, chemical, and topographical properties. Our results showed that the salivary pellicle proteomes on Ti exhibited differences in composition and protein intensities compared with dental surfaces. These differences in proteomes affected the biological processes at the level of microbiome accumulation. Geometric analysis showed greater similarity between Ti and enamel proteomes, while dentine differed markedly. Ti harbors a microbiome community that differs from that of dental surfaces. Canonical correlation analysis (CCA) pinpointed proteins that promoted or inhibited the adherence of specific microbes. Apolipoprotein E showed a strong negative correlation (>0.8) with Streptococcus parasanguinis . Higher levels of the protein on dental surfaces were associated with reduced microbial adhesion, whereas its absence on Ti surfaces facilitated increased bacterial adhesion. These findings provide valuable insights into the initial biological responses after the insertion of implanted devices, which can be leveraged by biomedical engineering to develop biomaterials with enhanced outcomes and prevent microbial accumulation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".