Impact of Porphyromonas gingivalis inoculation on ligature-induced alveolar bone loss. A pilot study in rats
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Periodontitis is a polymicrobial infection characterized by the loss of connective tissue attachment, periodontal ligament and alveolar bone. The aim of this study was to evaluate the impact of Porphyromonas gingivalis inoculation on the ligature-induced alveolar bone loss (ABL) model in rats. MATERIAL AND METHODS: Forty male Wistar rats were randomly assigned to the following groups: G1, control (n = 10); G2, ligature-induced ABL (n = 15); and G3, ligature-induced ABL + P. gingivalis inoculation (n = 15). Rats in G2 and G3 were killed 15, 21 and 30 d after ligature placement, and the following parameters were assessed: microbiological load; ABL; and interleukin (IL)-1β (Il1beta)/Il1ra, Il6/Il10 and Rankl/osteoprotegerin (Opg) mRNA ratios in the gingival tissues, as determined by quantitative PCR. RESULTS: Microbiological analyses demonstrated that rats in G1, G2 and G3 were positive for the presence of bacteria (determined using PCR amplification of the 16S gene), but that only the treatment sites of rats in G3 were positive for P. gingivalis at all time-points investigated. Histometrically, significant bone loss (p<0.001) was observed for both ligated groups (G2 and G3) compared with the nonligated group (G1), with higher ABL observed for G2 at all the experimental time-points. Furthermore, gene-expression analysis demonstrated that the presence of P. gingivalis in the dentogingival area significantly decreased the Il1β/Il1ra, Il6/Il10 and Rankl/Opg mRNA ratios compared with ligature alone. CONCLUSION: Within the limits of this pilot study, it was concluded that inoculation of P. gingivalis affected the ligature-induced ABL model by the induction of an anti-inflammatory and antiresorptive host response.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".