Jing Guan Fang, an herbal formula, as an immunomodulator: opposing effects on basal and lipopolysaccharide-induced inflammation of macrophage via JAK/STAT3 and MAPK pathways
Bibliographic record
Abstract
, is commonly used for preventing SARS-CoV-2 infection and alleviating cold-like symptoms. However, the precise immunoregulatory mechanisms underlying its effects remain unclear and warrant investigation. This study aims to investigate the immunomodulatory effects of JGF and further elucidate the underlying mechanisms. The results showed that JGF had minimal impact on the cell viability of RAW264.7 and MH-S. In the absence of LPS stimulation, JGF promoted macrophages to produce NO and pro-inflammatory cytokines in a concentration-dependent manner. However, after LPS treatment, the JGF add-on exhibited contrasting effects, with the half-maximal effective concentrations for reducing macrophage-secreted NO and IL-6 being 80 and 180 μg/mL, respectively. Western blot analysis revealed that the JGF supplement marginally induced the production of iNOS and COX-2 without LPS stimulation. However, in LPS-pretreated cells, JGF demonstrated the opposite effect. JGF monotherapy accelerated phosphorylation in the JNK and JAK2 signaling pathways. In contrast, JGF inhibited LPS-stimulated STAT3 phosphorylation by suppressing JNK1/2 activation. Moreover, JGF reduced LPS-induced expression of IL-6 and TNF-α in the lungs and serum of mice. Collectively, the findings suggest that JGF exhibits immunomodulatory activity and suppresses pro-inflammatory cytokine expression caused by LPS.
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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".