Microbial Diversity Similarities in Periodontal Pockets and Atheromatous Plaques of Cardiovascular Disease Patients
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: The immune and infectious alterations occurring in periodontitis have been shown to alter the development and severity of cardiovascular disease. One of these relationships is the translocation of oral bacteria to atheroma plaques, thereby promoting plaque development. Thus, the aim of this study was to assess, by 16s cloning and sequencing, the microbial diversity of the subgingival environment and atheroma plaques of patients concomitantly suffering from periodontitis and obstructive coronary artery atherosclerosis (OCAA). METHODS: Subgingival biofilm and coronary balloons used in percutaneous transluminal coronary angioplasty were collected from 18 subjects presenting with generalized moderate to severe periodontitis and OCAA. DNA was extracted and the gene 16S was amplified, cloned and sequenced. RESULTS: Significant differences in microbial diversity were observed between both environments. While subgingival samples mostly contained the phylum Firmicutes, in coronary balloons, Proteobacteria (p<0.05) was predominant. In addition, the most commonly detected genera in coronary balloons were Acinetobacter, Alloprevotella, Pseudomonas, Enterobacter, Sphingomonas and Moraxella, while in subgingival samples Porphyromonas, Filifactor, Veillonella, Aggregatibacter and Treponema (p<0.05) were found. Interestingly, 17 identical phylotypes were found in atheroma and subgingival samples, indicating possible bacterial translocation between periodontal pockets and coronary arteries. CONCLUSION: Periodontal pockets and atheromatous plaques of cardiovascular disease patients can present similarities in the microbial diversity.
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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) | 0.002 | 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".