Use of a Cellulase from Trichoderma reesei as an Adjuvant for Enterococcus faecalis Biofilm Disruption in Combination with Antibiotics as an Alternative Treatment in Secondary Endodontic Infection
Bibliographic record
Abstract
Apical periodontitis is an inflammation leading to the injury and destruction of periradicular tissues. It is a sequence of events that starts from root canal infection, endodontic treatment, caries, or other dental interventions. Enterococcus faecalis is a ubiquitous oral pathogen that is challenging to eradicate because of biofilm formation during tooth infection. This study evaluated a hydrolase (CEL) from the fungus Trichoderma reesei combined with amoxicillin/clavulanic acid as a treatment against a clinical E. faecalis strain. Electron microscopy was used to visualize the structure modification of the extracellular polymeric substances. Biofilms were developed on human dental apices using standardized bioreactors to evaluate the antibiofilm activity of the treatment. Calcein and ethidium homodimer assays were used to evaluate the cytotoxic activity in human fibroblasts. In contrast, the human-derived monocytic cell line (THP-1) was used to evaluate the immunological response of CEL. In addition, the secretion of the pro-inflammatory cytokines IL-6 and TNF-α and the anti-inflammatory cytokine IL-10 were measured by ELISA. The results demonstrated that CEL did not induce the secretion of IL-6 and TNF-α when compared with lipopolysaccharide used as a positive control. Furthermore, the treatment combining CEL with amoxicillin/clavulanic acid showed excellent antibiofilm activity, with a 91.4% reduction in CFU on apical biofilms and a 97.6% reduction in the microcolonies. The results of this study could be used to develop a treatment to help eradicate persistent E. faecalis in apical 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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".