Endodontic pathogens possess collagenolytic properties that degrade human dentine collagen matrix
Bibliographic record
Abstract
Abstract Aim To measure collagenolytic protease activity from Enterococcus faecalis and Micrococcus luteus and their ability to degrade human dentinal collagen. Methodology Proteases activity of E. faecalis ATCC 29212, ATCC 47077 and M. luteus towards generic and specific human matrix metalloproteinase (MMP) substrates was measured using a fluorimetric assay. The ability of the bacteria to degrade dentinal collagen was tested by quantifying the amount of hydroxyproline released into the media following incubation of the bacteria or heat‐inactivated bacteria (HIN) with demineralized human dentine samples for 24 h and by scanning electron microscopy (SEM). Multifactorial anova and Tukey's post hoc test were used to analyse the data ( P < 0.05). Results All strains had MMP‐like activities, but with different substrate affinity; E. faecalis ATCC 29212, ATCC 47077 and M. luteus had the greatest affinity towards MMP‐8 (7.75 ± 0.88 μmol L −1 /3 × 10 6 CFU), MMP‐9 (33.86 ± 5.16 μmol L −1 /3 × 10 6 CFU) and generic MMP (26.08 ± 4.48 μmol L −1 /3 × 10 6 CFU), respectively. The amount of hydroxyproline released from demineralized dentine was similar ( P > 0.05) for the three strains (range 1.8 ± 0.17 to 2.38 ± 0.39 μg 50 μL −1 ) and was significantly higher ( P < 0.001) compared to their HIN counterparts (0.61 ± 0.22 μg 50 μL −1 ). SEM revealed increased collagen network degradation after incubation with bacteria versus HIN. Conclusions Endodontic pathogens possess collagenolytic protease properties that enable them to degrade dentinal collagen, potentially compromising the restoration‐tooth and sealer‐tooth interfaces. These collagenolytic protease properties could facilitate the migration of pathogenic bacteria into the root canal system and explain in part their role in root canal infections.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; both teacher heads agree on what is shown here.
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".