Tissue structure, and IL‐1β, IL‐8, and TNF‐α secretions after contact by engineered human oral mucosa with dentifrices
Bibliographic record
Abstract
The use of dentifrice is part of an oral prophylaxis that aims at keeping bacteria in check within the dental plaque. When introduced into the oral cavity, dentifrice also comes in close contact with the oral epithelium. Our goal was to evaluate the effects of dentifrices on tissue structure and pro-inflammatory mediator release by epithelial cells. For this purpose, tri-dimensional engineered human oral mucosa (EHOM) was produced using normal human palatal fibroblasts and epithelial cells. EHOMs were either treated with Aquafresh(R) or Crest(R) for 1, 4, 8, and 24 h, or untreated, then used for cell viability assessment and structural analyses. Cultured supernatants were used to evaluate cytokine (interleukin (IL)-1beta, IL-8 and tumor necrosis factor (TNF)-alpha) secretion, and metalloproteinase (MMP)-2 and -9 activities. The present in vitro study using engineered oral mucosa confirms that dentifrices (Aquafresh and Crest) contribute to tissue desquamation. The desquamation was substantial at 24 h of contact but was limited to the upper layers of the treated tissues. Cell death in these tissues was not increased, suggesting that the dentifrice had accelerated desquamation of the layers containing differentiated cells. Measurement of cytokines revealed that dentifrices up-regulated IL-1beta while down-regulating IL-8 and TNF-alpha secretion, thus indicating an impaired cascade of inflammatory responses. These dentifrices may also impair normal repair mechanisms as suggested by an up-regulation of gelatinase activities. In conclusion, this study suggested that, via cytokines, dentifrice contributes to the modulation of the inflammatory (pro-inflammatory/anti-inflammatory responses) process.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".