The anti-inflammatory role of granulocyte colony-stimulating factor in macrophage–dendritic cell crosstalk after<i>Lactobacillus rhamnosus</i>GR-1 exposure
Bibliographic record
Abstract
MΦs are important sensory cells of the innate immune system and regulate immune responses through releasing different combinations of cytokines. In this study, we examined whether cytokines released by MΦs in response to the probiotic bacterial strain GR-1 modulate the responses of DCs. The cytokine profile released by GR-1-treated MΦs was characterized by low levels of TNF-α, GM-CSF, IL-6, and IL-12 but very high levels of G-CSF. GR-1 CM did not induce expression of the shared p40 subunit of IL-12 and IL-23 and costimulatory molecules CD80 or CD86 or increase T cell stimulatory capacity in DCs. However, in G-CSFR-deficient DCs or after antibody-mediated neutralization of G-CSF, GR-1 CM induced IL-12/23 p40 production significantly, indicating that G-CSF within the GR-1 CM inhibits IL-12/23 p40 production induced by other CM components. GR-1 CM and rG-CSF also inhibited LPS-induced IL-12 production at the mRNA and protein levels. The inhibition of IL-12 production by G-CSF was at least in part mediated through inhibition of JNK activation. Finally, splenic DCs of GR-1-injected mice produced less IL-12/23 p40 than those of PBS-injected mice in response to LPS ex vivo, and this was at least partially dependent on exposure to GR-1-induced G-CSF in vivo. Altogether, these results suggest that G-CSF modulates the IL-12/23 p40 response of DCs in the context of the probiotic GR-1 through MΦ-DC crosstalk.
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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) | 0.001 | 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".