Protective and Destructive Immunity in the Periodontium: Part 1—Innate and Humoral Immunity and the Periodontium
Bibliographic record
Abstract
Based on the results of recent research in the field, the present paper will discuss the protective and destructive aspects of the innate vs. adaptive (humoral and cell-mediated) immunity associated with the bacterial virulent factors or antigenic determinants during periodontal pathogenesis. Attention will be focused on: (i) the Toll-like receptors (TLR), the innate immune repertoire for recognizing the unique molecular patterns of microbial components that trigger innate and adaptive immunity for effective host defenses, in some general non-oral vs. periodontal microbial infections; (ii) T-cell-mediated immunity, Th-cytokines, and osteoclastogenesis in periodontal disease progression; and (iii) some molecular techniques developed and used to identify critical microbial virulence factors or antigens associated with host immunity (using Actinobacillus actinomycetemcomitans and Porphyromonas gingivalis as the model species). Therefore, further understanding of the molecular interactions and mechanisms associated with the host’s innate and adaptive immune responses will facilitate the development of new and innovative therapeutics for future periodontal treatments. Abbreviations used in the paper are as follows: A. actinomycetemcomitans ( Aa), Actinobacillus actinomycetemcomitans; Ab, antibody; DC, dendritic cells; mAb, monoclonal antibody; pAb, polyclonal antibody; PAMP, pathogen-associated molecular patterns; P. gingivalis ( Pg), Porphyromonas gingivalis; and TLR, Toll-like receptors.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".