Level of toll-like receptor agonist exposure differentially determines chemokine production in humansThis article is one of a selection of papers published in the Special Issue on Recent Advances in Asthma Research.
Bibliographic record
Abstract
Toll-like receptor (TLR) agonists, ubiquitously present in the environment, are key players in activating synthesis of cytokines and chemokines that control normal and pathophysiological processes, including multiple inflammatory diseases. TLR2 and TLR4 respond to bacterial cell wall products. We examined the impact of TLR activation on human immune capacity using stimuli ranging from the low levels seen in most environments to the high concentrations widely used for in vitro studies. Peripheral blood mononuclear cells from 117 healthy children were activated with lipopolysaccharide (TLR4 ligand) or peptidoglycan (TLR2 ligand) over a million-fold range of concentrations. Resulting interleukin-6, CCL2, and CCL22 production were quantified by ELISA. The intensity of cytokine production elicited was linearly related to the intensity of the stimulus up to maximal responses. In marked contrast, chemokine production was not linearly related to agonist concentration. Responses rose with increasing stimulation, and then were markedly reduced (40%-100%, p < 0.0001) in response to the high levels of TLR stimulation most commonly cited. Thus, the levels of TLR4 and TLR2 agonists typically used for in vitro interrogation of immune capacity yield results clearly distinct from those obtained using commonly occurring environmental levels of TLR ligands. These findings demonstrate the importance of utilizing TLR ligands at concentrations more closely mimicking environmental levels when assessing immune capacity.
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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) | 0.002 | 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".