Epithelial cell response to challenge of bacterial lipoteichoic acids and lipopolysaccharides in vitro
Bibliographic record
Abstract
Accumulating dental plaque at the gingival margin contains lipoteichoic acids (LTAs) from the cell walls of gram-positive bacteria. In subgingival plaque associated with periodontal disease the amount of lipopolysaccharides (LPSs) from gram-negative bacteria increases. As the gingival junctional epithelium (JE) is an important structural and functional tissue, participating in the first line defence against apical advancement of dental plaque, this study examined the direct effects of LTAs (from Streptococcus mutans and S. sanguis) and LPSs (from Actinobacillus actinomycetemcomitans, Porphyromonas gingivalis, Treponema denticola and Escherichia coli) on two epithelial cell lines (HaCaT and ERM) and a culture model for human JE. The cells were exposed to the LTAs or LPSs (10-50 microg/ml) for variable periods of time. None of the bacterial surface components had any effect on primary adhesion or on the epithelial attachment of the JE cultures. However, cell growth and mitotic activity were consistently reduced in all cultures treated with LTAs. In contrast, LPSs showed only slight or no effects on cell growth and mitotic activity depending on the epithelial cells used. This suggests that LPSs, despite their established role as modulators of inflammation, do not have direct harmful effects - at the concentrations found in dental plaque and gingival crevicular fluid - which would explain the mechanism of epithelial degeneration and detachment from the tooth surface. However, the LTAs appear to inhibit the renewal of epithelium and may thus contribute to degeneration of coronal JE and subgingival colonisation by periodontal pathogens.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".