Yiqi-Huoxue Granule (YQHX) Downregulates Prothrombotic Factors by Modulating KLF2 and NF-<i>κ</i>B in HUVECs following LPS Stimulation
Post-publication record
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Bibliographic record
Abstract
The Yiqi-Huoxue granule (YQHX) is a traditional Chinese medication widely used in the therapy of the traditional Chinese medicine diagnosis “Qi deficiency” or “blood stasis” in China. Both these symptoms are related to inflammation, but the mechanisms of YQHX against inflammation are largely unknown. Thus, our present study investigated the effects of YQHX on regulating inflammatory responses induced by lipopolysaccharides (LPS) in HUVECs. Our data found that YQHX remarkably inhibits the production of prothrombotic factors, plasminogen activator inhibitor-1 (PAI-1) and tissue factor (TF), while it upregulates the protein expression of Kruppel-like factor 2 (KLF2). The increase in PAI-1 and TF was significantly attenuated through a transgenic knockdown in KLF2 with a Lenti-shKLF2 vector. YQHX also decreases the phosphorylation of nuclear factor- κ B (NF- κ B) p65 and I κ B following LPS stimulation, and it effectively suppresses PAI-1 and TF via a NF- κ B-dependent mechanism. Taken together, our results suggest that YQHX provides a notable antithrombotic activity via regulating the KLF2 expression and NF- κ B signaling pathway in HUVECs. The KLF2 and NF- κ B may be potential therapeutic targets for interventions of inflammation associated with atherosclerosis.
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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".