Comparative analysis of virulence determinants and mass spectral profiles of Finnish and Lithuanian endodontic <i>Enterococcus faecalis</i> isolates
Bibliographic record
Abstract
INTRODUCTION: Putative virulence factors of Enterococcus faecalis have been proposed by several workers and, by analogy, these have been linked to strains of endodontic origin. However, their distribution within the cell population is unknown. In the present study, isolates were taken from the dental root canals of two defined human populations, Lithuanian and Finnish, and examined for a range of virulence properties. In addition, surface-associated molecules and intracellular proteins were compared using matrix-assisted laser desorption-ionization/mass spectrometry (MALDI-TOF-MS) and ProteinChip capture/MS (SELDI-TOF-MS), respectively. METHODS: Twenty-three Lithuanian and 35 Finnish dental root canal isolates were included. The esp, gelE, ace and efaA genes were detected by polymerase chain reaction, and cytolysin and gelatinase phenotypes were determined by hydrolysis of horse blood agar and gelatine agar, respectively. Protein extracts and surface-associated molecules of whole cells were analysed by SELDI-TOF-MS and MALDI-TOF-MS, respectively. RESULTS: Presence of esp (n = 15), cytolysin (n = 9), ace (n = 55) and efaA (n = 58) was not statistically different in the two samples, whereas gelE and gelatinase production was detected more frequently in the Finnish material (chi-squared, P < 0.01). Analysis of protein profiles by SELDI-TOF-MS showed clustering of cytolysin-producing strains, whereas MALDI-TOF-MS generated profiles that clustered according to the samples' origin and, furthermore, to atypical quinupristin-dalfopristin susceptibility. CONCLUSION: A high prevalence of virulence factors was demonstrated in both population types. SELDI-TOF-MS and MALDI-TOF-MS proved useful in distinguishing between different E. faecalis phenotypes and they may be useful technologies for elucidating the eco-distribution of E. faecalis in humans.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".