Effects of LL-37 Peptide Mimetics on Gingival Fibroblast Function
Bibliographic record
Abstract
Effects of LL-37 Peptide Mimetics on Gingival Fibroblast Function Host defence peptides, including the cathelicidin LL-37, play an important role in mucosal immunity, functioning as both antimicrobial agents and modulators of the inflammatory response. In the current climate of antibiotic resistance, the idea of using naturally occurring antimicrobial peptides, or their synthetic mimetics, to combat oral infection is particularly appealing. Objectives: The aim of this study was to investigate the effects of parent LL-37, and two peptide mimetics (KR-12 and KE-18), on cytokine expression and response to bacterial challenge by gingival fibroblasts. Methods: KR-12 and KE-18 are peptide mimetics of the biologically active, mid-region sequence of LL-37. The effects of commercially available LL-37, KR-12 and KE-18 on gingival fibroblast response to E coli and P gingivalis LPS challenge, analysed by IL-6 and IL-8 expression, were determined in cell culture by ELISA. The direct effects of each peptide on IL-6, IL-8, CXCL-1 and HGF expression were also determined by ELISA. The MTT assay was used to evaluate peptide effects on fibroblast viability. Results: LL-37 and KE-18, but not KR-12, inhibited LPS induction of inflammatory cytokine expression and directly stimulated CXCL-1 production by fibroblasts. All 3 peptides stimulated production of IL-8 and HGF. Neither LL-37 nor KE-12 affected cell viability, while KE-18, at higher concentrations, induced cell death. Conclusions: Shorter, peptide mimetics of LL-37, in particular KE-18, retain the immunomodulatory effects of the parent molecule and possess excellent potential as therapeutic agents in the treatment of oral infections including periodontal disease.
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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) | 0.001 | 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) | 0.002 | 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".