Functional human T-cell immunity and osteoprotegerin ligand control alveolar bone destruction in periodontal infection
Bibliographic record
Abstract
Periodontitis, a prime cause of tooth loss in humans, is implicated in the increased risk of systemic diseases such as heart failure, stroke, and bacterial pneumonia. The mechanisms by which periodontitis and antibacterial immunity lead to alveolar bone and tooth loss are poorly understood. To study the human immune response to specific periodontal infections, we transplanted human peripheral blood lymphocytes (HuPBLs) from periodontitis patients into NOD/SCID mice. Oral challenge of HuPBL-NOD/SCID mice with Actinobacillus actinomycetemcomitans, a well-known Gram-negative anaerobic microorganism that causes human periodontitis, activates human CD4(+) T cells in the periodontium and triggers local alveolar bone destruction. Human CD4(+) T cells, but not CD8(+) T cells or B cells, are identified as essential mediators of alveolar bone destruction. Stimulation of CD4(+) T cells by A. actinomycetemcomitans induces production of osteoprotegerin ligand (OPG-L), a key modulator of osteoclastogenesis and osteoclast activation. In vivo inhibition of OPG-L function with the decoy receptor OPG diminishes alveolar bone destruction and reduces the number of periodontal osteoclasts after microbial challenge. These data imply that the molecular explanation for alveolar bone destruction observed in periodontal infections is mediated by microorganism-triggered induction of OPG-L expression on CD4(+) T cells and the consequent activation of osteoclasts. Inhibition of OPG-L may thus have therapeutic value to prevent alveolar bone and/or tooth loss in human periodontitis.
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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) | 0.000 | 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 | 0.001 |
| 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".