Wei-Tong-Xin exerts anti-inflammatory effects through TLR4-mediated macrophages M1/M2 polarization and affects GLP-1 secretion
Bibliographic record
Abstract
OBJECTIVES: The present study was undertaken to explore the effects and mechanisms of Wei-Tong-Xin (WTX) in inhibiting lipopolysaccharide (LPS)-induced inflammatory response of macrophages, in turn, to study the influences on GLP-1 secretion of GLUTag cells. METHODS: We first evaluated the activation of Raw 264.7 cells and measured the intracellular ROS, CD86 and CD206 levels by flow cytometry. The expressions of proteins were detected by western blot and immunofluorescence. GLP-1 levels were detected by ELISA kits. TLR4 siRNA was used to investigate the role of TLR4 in the regulation of macrophage polarization by WTX. KEY FINDINGS: The results showed that WTX inhibited LPS-induced polarization of macrophages toward the M1 phenotype, but promoted the M2 phenotype. Meanwhile, WTX inhibited the TLR4/MyD88 pathway. The polarization of M1 phenotype promoted GLP-1 secretion by GLUTag cells, which was inhibited by WTX. The results of siRNA showed that WTX exhibited anti-inflammatory effects through targeting TLR4. CONCLUSIONS: Overall, WTX inhibited polarization of macrophages towards M1 phenotype but promoted the amounts of M2 phenotype, further the macrophages regulated by WTX alleviated GLP-1 content secreted by GLUTag cells. The aforementioned results were produced by WTX-mediated TLR4.
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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) | 0.002 | 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".