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
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it