Proteomic profile of root canal contents in teeth with post‐treatment endodontic disease
Bibliographic record
Abstract
AIM: To characterize the proteome of 20 root canals in teeth with post-treatment endodontic disease using mass spectrometry and to correlate the identified proteins with clinical features. METHODOLOGY: Twenty patients with radiographic evidence of apical periodontitis and need for root canal re-treatment were selected. Samples from the root canal contents were collected and processed using two-dimensional capillary nano-flow liquid chromatography and electrospray ionization tandem mass spectrometry. The acquired spectra were separately searched against specific protein database. The results obtained were analysed using descriptive statistics. Additionally, Pearson's chi-square test or one-sided Fisher's exact test, as appropriate, was chosen to examine the null hypothesis that there is no relationship between each clinical feature and the presence of specific microbial or human proteins. Significance levels were set at 5% (P < 0.05). RESULTS: A total of 1153 human and 720 microbial UniProt accession numbers corresponding to proteins were recovered. The greater prevalence of proteins was related to biological functions, such as cellular and metabolic processes. A considerable number of microbial proteins with clinical relevance functions, such as pathogenesis/virulence, proteolysis, cell adhesion and drug resistance, were detected. Common endodontic pathogens related to post-treatment endodontic disease such as Enterococcus spp., Propionibacterium spp. and Streptococcus spp. were associated with 23, 40 and 94 distinct proteins, respectively. As for human proteins, many factors related to the immune system process were detected. No significant correlations were found between microbial and human proteins and the clinical features investigated (P > 0.05). CONCLUSIONS: A considerable number of microbial and human proteins were identified using proteomic analyses, being mainly related to processes indicating cell viability. No significant correlation was found between proteins and clinical features. These findings suggest a network of important microbial pathogenic functions that may be responsible for the host immune system response.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.003 | 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 teacher head, 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".