T‐cell‐receptor gene usage of <i>Actinobacillus actinomycetemcomitans‐</i>reactive periodontal CD4<sup>+</sup> T cells from localized juvenile periodontitis patients and human peripheral blood leukocyte‐reconstituted NOD/SCID mice
Bibliographic record
Abstract
We investigated the variable Valpha and Vbeta gene usage of Actinobacillus actinomycetemcomitans-reactive periodontal CD4+ T cell receptors (TCR) from: (i) four A. actinomycetemcomitans-infected localized juvenile periodontitis (LJP) patients, (ii) four groups of A. actinomycetemcomitans-inoculated NOD/SCID mice engrafted with individual LJP-derived HuPBL and (iii) HuPBL samples of four LJP patients and two healthy control subjects, by quantitative PCR analyses. The results show that: (i) the majority of the TCR genes (82.5% of Valpha and 91.1% of Vbeta) used by periodontal CD4+ T cells in A. actinomycetemcomitans-inoculated HuPBL-engrafted NOD/SCID mice overlap with those used by local periodontal T cells in LJP patients, (ii) although A. actinomycetemcomitans-reactive periodontal CD4+ TCR repertoire is relatively widespread, there are a few dominant genes shared by the LJP patients, suggesting a limited number of antigens or epitopes commonly recognized and (iii) A. actinomycetemcomitans likely lacks superantigenic characteristics. These results suggest A. actinomycetemcomitans-associated human CD4+ T cell repertoire established in HuPBL-NOD/SCID mice provides a useful approach to study specific aspects of immune-parasite interactions in the periodontium.
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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 | 0.000 |
| 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 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".