G(−) Anaerobes–Reactive CD4+ T-Cells Trigger RANKL-Mediated Enhanced Alveolar Bone Loss in Diabetic NOD Mice
Bibliographic record
Abstract
Diabetic patients experience a higher risk for severe periodontitis; however, the underlying mechanism remains unclear. We investigated the contribution of antibacterial T-cell-mediated immunity to enhanced alveolar bone loss during periodontal infection in nonobese diabetic (NOD) mice by oral inoculation with Actinobacillus actinomycetemcomitans, a G(-) anaerobe responsible for juvenile and severe periodontitis. The results show that 1) inoculation with A. actinomycetemcomitans in pre-diabetic NOD mice does not alter the onset, incidence, and severity of diabetes; 2) after A. actinomycetemcomitans inoculation, diabetic NOD mice (blood glucose >200 mg/dl and with severe insulitis) exhibit significantly higher alveolar bone loss compared with pre-diabetic and nondiabetic NOD mice; and 3) A. actinomycetemcomitans-reactive CD4+ T-cells in diabetic mice exhibit significantly higher proliferation and receptor activator of nuclear factor kappaB ligand (RANKL) expression. When diabetic mice are treated with the RANKL antagonist osteoprotegerin (OPG), there is a significant reversal of alveolar bone loss, as well as reduced RANKL expression in A. actinomycetemcomitans-reactive CD4+ T-cells. This study clearly describes the impact of autoimmunity to anaerobic infection in an experimental periodontitis model of type 1 diabetes. Thus, microorganism-reactive CD4+ T-cells and the RANKL-OPG axis provide the molecular basis of the advanced periodontal breakdown in diabetes and, therefore, OPG may hold therapeutic potential for treating bone loss in diabetic subjects at high risk.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".