α‐tocopherol decreases interleukin‐1β and ‐6 and increases human β‐defensin‐1 and ‐2 secretion in human gingival fibroblasts stimulated with <i>Porphyromonas gingivalis</i> lipopolysaccharide
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Periodontitis, a disease associated with chronic inflammation, results in significant destruction of periodontal tissues. Uncontrolled, periodontal disease negatively affects general patient health. We sought to evaluate the effect of α-tocopherol on gingival fibroblast behavior following exposure to Porphyromonas gingivalis lipopolysaccharide (LPS). MATERIAL AND METHODS: Primary human gingival fibroblasts were cultured for 24 and 48 h with α-tocopherol at various concentrations (0, 50, 100 and 200 μm) in the presence or absence of 1 μg/mL of LPS. At the end of each time point, cell adhesion and growth were evaluated by means of optical microscope observations and MTT assay. The secretion levels of cytokines interleukin (IL)-1β and IL-6 and human β-defensins 1 and 2 were measured by specific enzyme-linked immunosorbent assay. Finally, an in vitro scratch wound assay was performed to investigate the effect of α-tocopherol on fibroblast migration. RESULTS: α-tocopherol alone had no adverse effect on cell adhesion and morphology. Fibroblast proliferation increased in the presence of α-tocopherol with and without LPS. α-tocopherol alone had no effect on inflammatory cytokine (IL-1β and IL-6) secretion. Interestingly, following cell exposure to P. gingivalis LPS, α-tocopherol significantly (p < 0.01) decreased the secretion of these two cytokines and increased human β-defensin-1 and -2 secretion. Finally, α-tocopherol increased the healing rate of the gingival fibroblasts from 12 h up to 48 h. CONCLUSION: These results suggest that α-tocopherol may play an active role in countering the damaging effect of LPS by reducing inflammatory cytokines, increasing β-defensins and promoting fibroblast growth, migration and wound healing.
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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".