Association between root canals and gingival sulci microbiota in secondary and persistent endodontic infections
Bibliographic record
Abstract
Secondary/persistent endodontic infections (SPEIs) result from failed root canal therapy, causing persistent apical periodontitis. Current diagnostic methods for SPEIs predominantly rely on clinical and radiographic indicators, which often lack adequate sensitivity and specificity. Consequently, there is an urgent need to effectively detect SPEIs or monitor their progression. The aim of this study was to compare and characterize the microbiota of root canals and gingival sulci of teeth affected by SPEI to identify keystone pathogens as potential diagnostic biomarkers through advanced next-generation sequencing (NGS) techniques. Ninety samples from 30 affected teeth in 25 patients undergoing nonsurgical retreatment were analyzed. Bacterial DNA was extracted, the V3-V4 region of the 16S rRNA gene was amplified, and sequencing was performed (Illumina MiSeq). Amplicon sequence variants (ASVs) identified 16 phyla, 182 genera, and 390 species. Microbiota in root canals differed from gingival sulci, with Acinetobacter and Veillonella prevalent in canals, and Streptococcus and Actinomyces dominant in sulci. Certain species, including Shuttleworthella satelles, Olsenella uli, Dialister invisus, Massilia timonae, and Klebsiella pneumoniae were detected in both sites, suggesting microbial migration via anatomical structures. Detecting these potential keystone pathogens of SPEI as biomarkers in readily accessible sulcus fluid could facilitate diagnoses and monitoring of progression and/or resolution. These insights provide a foundation for more accurate and targeted diagnostic and therapeutic strategies for management of SPEIs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".