A systematic review of the protein composition of whole saliva in subjects with healthy periodontium compared with chronic periodontitis
Bibliographic record
Abstract
CONTEXT: Periodontitis is a chronic multifactorial inflammatory disease linked to oral microbiota dysbiosis. This disease progresses to infection that stimulates a host immune/inflammatory response, with progressive destruction of the tooth-supporting structures. OBJECTIVE: This systematic review aims to present a robust critical evaluation of the evidence of salivary protein profiles for identifying oral diseases using proteomic approaches and summarize the use of these approaches to diagnose chronic periodontitis. DATA SOURCES: A systematic literature search was conducted from January 1st, 2010, to December 1st, 2022, based on PICO criteria following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and by searching the three databases Science Direct, Scopus, and Springer Link. STUDY SELECTION: According to the inclusion criteria, eight studies were identified to analyze the proteins identified by proteomics. RESULTS: The protein family S100 was identified as the most abundant in patients with chronic periodontitis. In this family, an increased abundance of S100A8 and S100A9 from individuals with the active disease was observed, which strongly relates to the inflammatory response. Moreover, the ratio S100A8/S100A9 and the metalloproteinase-8 in saliva could differentiate distinct periodontitis groups. The changes in protein profile after non-surgical periodontal therapy improved the health of the buccal area. The results of this systematic review identified a set of proteins that could be used as a complementary tool for periodontitis diagnosis using salivary proteins. CONCLUSION: Biomarkers in saliva can be used to monitor an early stage of periodontitis and the progression of the disease following therapy.
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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 | 0.010 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.010 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".