Toll-like Receptor-7 Tolerizes Malignant B Cells and Enhances Killing by Cytotoxic Agents
Bibliographic record
Abstract
Chronic activation through Toll-like receptors (TLR) occurs in a number of pathologic settings, but has not been studied to the same extent as primary activation. TLR7, expressed by B cells and some dendritic cells, recognizes molecular patterns associated with viruses that can be mimicked by synthetic imidazoquinolines. In response to primary stimulation with the imidazoquinoline, S28690, human mononuclear cells produced tumor necrosis factor-alpha, but were unable to do so upon restimulation with S28690. This state of "tolerization" lasted at least 5 days. Using chronic lymphocytic leukemia B cells as a model to facilitate biochemical analysis, the tolerized state was found to be associated with altered receptor components, including down-regulated expression of TLR7 mRNA and decreased levels of interleukin-1 receptor-associated kinase 1. Tolerization was characterized by a transcriptionally regulated block in stress-activated protein kinase and nuclear factor kappaB activation, with relatively preserved activation of extracellular signal-regulated kinase (ERK). Tolerized chronic lymphocytic leukemia cells were found to be more sensitive to cytotoxic chemotherapeutic agents, in part through altered stress-activated protein kinase signaling pathways. This property of the TLR7-tolerized state may potentially be exploited in the treatment of B cell cancers.
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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".