An evaluation of an array of viruses and fungi in adult Lebanese patients presenting with various dental infections: A cross-sectional study
Bibliographic record
Abstract
INTRODUCTION: The role of bacteria in the pathogenesis of periodontitis, pericoronitis, and periapical infections has been well-established. However, the variation in the severity and prognosis of these lesions could suggest a potential role of other microorganisms, such as viruses and fungi. This study aims to evaluate the presence of adenovirus, human papillomavirus-16, Epstein-Barr virus, Candida, and non-Candida fungi in these infections. METHODOLOGY: A cross-sectional study including 120 healthy adult patients presenting with dental infections requiring dental extractions were conducted to assess the prevalence and the relative quantity of viruses and fungi in saliva, infected, and healthy tissues using quantitative polymerase chain reaction tests. Samples were collected, and a categorical scale was used for the prevalence and a continuous scale for the relative quantification. Statistical analyses were performed using Chi-square for the prevalence and Wilcoxon rank test for the relative quantification. RESULTS: Except for the Epstein-Barr virus and Candida, the presence of viruses and fungi was significantly associated with dental infections. Adenovirus showed an association with pericoronitis, while human papilloma virus-16 exhibited an association with periapical infections. Non-Candida fungi, on the other hand, showed a positive association with all infected tissues and saliva as compared to healthy control lesions except for periapical infections. CONCLUSIONS: According to this study, viruses and fungi were found to be prevalent in dental infections. However, their associations with those infections vary depending on the types of viruses or fungi involved and the category of dental infections.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".