Platelet factor 4 upregulates matrix metalloproteinase‐1 production in gingival fibroblasts
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Periodontitis is a highly prevalent chronic inflammatory disease that causes tooth loss, morbidity and confers an increased risk for systemic disease. Tissue destruction during periodontitis is due in large part to collagen-degrading matrix metalloproteinases (MMPs) released by resident cells of the periodontium in response to proinflammatory cytokines. Platelets are immune-competent blood cells with a newly recognized role in chronic inflammation; however, their role in the pathogenesis of periodontitis is undefined. Consequently, the objective of this study was to assess the effect of platelet factor 4 (PF4), a major platelet-derived cytokine, on MMP-1 (collagenase) expression in human gingival fibroblasts (HGFs). MATERIAL AND METHODS: HGFs were cultured in the presence or absence of recombinant PF4. Pro-MMP-1 secretion was quantified by enzyme-linked immunosorbent assay analysis of the cell culture supernatants. MMP-1 transcription was quantified by real-time polymerase chain reaction. Regulation of MMP-1 production by the p44/42 MAP kinase (MAPK) signaling pathway was examined in the presence or absence of PF4. RESULTS: Exposure to PF4 caused a ~ 2-3-fold increase in MMP-1 transcription and secretion from cultured HGFs. PF4 treatment also enhanced phosphorylation of p44/42 MAPK, which has been previously shown to induce MMP-1 expression in fibroblasts. Blockade of p44/42 MAPK signaling with the cell-permeant inhibitors PD98059 and PD184352 abrogated PF4-induced pro-MMP-1 transcription upregulation and release from cultured HGFs. CONCLUSION: We conclude that PF4 upregulates MMP-1 expression in HGFs in a p44/42 MAPK-dependent manner. These findings point to a previously unidentified role for platelets in the pathogenesis of periodontal diseases.
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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".