Chronic periodontitis is associated with platelet factor 4 (<scp>PF</scp>4) secretion: A pilot study
Bibliographic record
Abstract
Abstract Aim Platelets contribute to chronic inflammation, but their role in periodontitis is not well understood. The aim of this study was to compare platelet recruitment and activation in healthy and inflamed periodontium. Materials and Methods Gingival crevicular fluid (GCF) samples were obtained from sites of healthy periodontium, gingivitis and periodontitis. Platelets were quantified in the GCF by staining and microscopy. GCF concentrations of platelet factor 4 (PF4) [PF4]GCF and glycoprotein IIbIIIa ([GPIIbIIIa]GCF) were determined by ELISA. Blood samples were obtained from the three patient groups. Platelets were isolated from whole blood and stimulated with lipopolysaccharide (LPS) from Porphyromonas gingivalis to evaluate and compare the LPS‐induced PF4 release. Results Compared to controls, platelet recruitment was increased at gingivitis and periodontitis sites, based on platelet counts and [GPIIbIIIa]GCF. [PF4]GCF was elevated in periodontal pockets but not at gingivitis or healthy sites. Circulating plasma levels of PF4 were higher in patients with generalized severe periodontitis (SP), compared to patients with gingivitis or healthy periodontium. Platelets isolated from SP patients contained and released more PF4 in response to P. gingivalis LPS than platelets from gingivitis or periodontally healthy patients. Conclusions Periodontitis is associated with increased platelet activation and PF4 release, both locally and systemically.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".