Periodontal therapy alters gene expression of peripheral blood monocytes
Bibliographic record
Abstract
AIMS: We investigated the effects of periodontal therapy on gene expression of peripheral blood monocytes. METHODS: Fifteen patients with periodontitis gave blood samples at four time points: 1 week before periodontal treatment (#1), at treatment initiation (baseline, #2), 6-week (#3) and 10-week post-baseline (#4). At baseline and 10 weeks, periodontal status was recorded and subgingival plaque samples were obtained. Periodontal therapy (periodontal surgery and extractions without adjunctive antibiotics) was completed within 6 weeks. At each time point, serum concentrations of 19 biomarkers were determined. Peripheral blood monocytes were purified, RNA was extracted, reverse-transcribed, labelled and hybridized with AffymetrixU133Plus2.0 chips. Expression profiles were analysed using linear random-effects models. Further analysis of gene ontology terms summarized the expression patterns into biologically relevant categories. Differential expression of selected genes was confirmed by real-time reverse transcriptase-polymerase chain reaction in a subset of patients. RESULTS: Treatment resulted in a substantial improvement in clinical periodontal status and reduction in the levels of several periodontal pathogens. Expression profiling over time revealed more than 11,000 probe sets differentially expressed at a false discovery rate of <0.05. Approximately 1/3 of the patients showed substantial changes in expression in genes relevant to innate immunity, apoptosis and cell signalling. CONCLUSIONS: The data suggest that periodontal therapy may alter monocytic gene expression in a manner consistent with a systemic anti-inflammatory effect.
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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".