<i>Porphyromonas gingivalis</i> -Epithelial Cell Interactions in Periodontitis
Bibliographic record
Abstract
Emerging data on the consequences of the interactions between invasive oral bacteria and host cells have provided new insights into the pathogenesis of periodontal disease. Indeed, modulation of the mucosal epithelial barrier by pathogenic bacteria appears to be a critical step in the initiation and progression of periodontal disease. Periodontopathogens such as Porphyromonas gingivalis have developed different strategies to perturb the structural and functional integrity of the gingival epithelium. P. gingivalis adheres to, invades, and replicates within human epithelial cells. Adhesion of P. gingivalis to host cells is multimodal and involves the interaction of bacterial cell-surface adhesins with receptors expressed on the surfaces of epithelial cells. Internalization of P. gingivalis within host cells is rapid and requires both bacterial contact-dependent components and host-induced signaling pathways. P. gingivalis also subverts host responses to bacterial challenges by inactivating immune cells and molecules and by activating host processes leading to tissue destruction. The adaptive ability of these pathogens that allows them to survive within host cells and degrade periodontal tissue constituents may contribute to the initiation and progression of periodontitis. In this paper, we review current knowledge on the molecular cross-talk between P. gingivalis and gingival epithelial cells in the development of periodontitis.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".