<i>Capnocytophaga</i> spp. in Periodontitis Patients Manifesting Diabetes Mellitus
Bibliographic record
Abstract
BACKGROUND: The subgingival microflora in patients presenting concurrently with periodontitis and diabetes mellitus (DM) are poorly understood. While traditional putative periodontal pathogens are implicated, research involving other oral organisms; e.g., Capnocytophaga spp., is lacking. These organisms produce a range of bacterial enzymes relevant to periodontal breakdown. It is inferred that periodontal bacteria acquire systemic access through the ulcerated periodontal pocket surface; conclusive evidence supporting this notion is limited. The aims of this investigation were to: 1) quantify and identify Capnocytophaga spp. present in healthy and diseased sites in periodontitis patients with and without DM, and 2) isolate periodontal pathogens from these patients' blood. METHODS: Twenty-one DM-periodontitis and 25 periodontitis patients were recruited. Subgingival plaque was collected from three healthy and three diseased sites per subject. Capnocytophaga spp. and total (facultative and obligate) anaerobic counts from each site were estimated. Capnocytophaga spp. were identified using 16S rRNA polymerase chain reaction (PCR) restriction fragment length polymorphism (RFLP). Statistical analyses were performed using multilevel modeling. Blood samples were subjected to HbA(1c) estimation and bacterial culture. RESULTS: A total of 848 Capnocytophaga spp. were isolated and identified. Significantly higher numbers of Capnocytophaga spp. (P <0.001) and anaerobes (P <0.001) were present in diseased sites in DM-periodontitis subjects compared to healthy sites in non-DM-periodontitis and DM-periodontitis subjects. C. ochracea (and variant) and C. granulosa were the most prevalent species. Blood samples were negative for Capnocytophaga spp. CONCLUSIONS: Total mean counts for Capnocytophaga spp. were significantly higher in DM-periodontitis subjects versus non-DM-periodontitis (P = 0.025) and at diseased sites versus healthy sites (P <0.001). Analysis of individual species revealed that the outcome varied with site status and DM status.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 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".