T Cell Proliferation Is Induced by Chronically TLR2-Stimulated Gingival Fibroblasts or Monocytes
Bibliographic record
Abstract
During inflammation of the gums, resident cells of the periodontium, gingival fibroblasts (GFs), interact with heterogeneous cell populations of the innate and adaptive immune system that play a crucial role in protecting the host from pathogenic infectious agents. We investigated the effects of chronic inflammation, by exposing peripheral blood mononuclear cells (PBMCs), peripheral blood lymphocyte (PBL) cultures, and GF–PBMC cocultures to Toll-like receptor 2 (TLR2) and TLR4 activators for 21 days and assessed whether this influenced leukocyte retention, survival, and proliferation. Chronic stimulation of PBMC–GF cocultures with TLR2 and TLR4 agonists induced a reduction of NK (CD56+CD3−), T (CD3+), and B (CD19+) cells, whereas the number of TLR-expressing monocytes were unaffected. TLR2 agonists doubled the T cell proliferation, likely of a selective population, given the net decrease of T cells. Subsequent chronic exposure experiments without GF, using PBMC and PBL cultures, showed a significantly (p < 0.0001) increased proinflammatory cytokine production of TNF-α and IL-1β up to 21 days only in TLR2-activated PBMC with concomitant T cell proliferation, suggesting a role for monocytes. In conclusion, chronic TLR activation mediates the shift in cell populations during infection. Particularly, TLR2 activators play an important role in T cell proliferation and proinflammatory cytokine production by monocytes, suggesting that TLR2 activation represents a bridge between innate and adaptive immunity.
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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) | 0.003 | 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".