Dachengqi decoction alleviates acute lung injury and inhibits inflammatory cytokines production through TLR4/NF‐κB signaling pathway in vivo and in vitro
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Sepsis that arises from uncontrolled pulmonary inflammation could induce acute lung injury (ALI), leading to the high death rate. Dachengqi decoction (DCQD) is a common traditional Chinese herbal medicine with strong anti-inflammatory effects. The current study aimed to explore the effect of DCQD on the inflammatory cytokines production, the aquaporin-1 (AQP-1) and AQP-5 protein expression in lipopolysaccharide (LPS)-induced ALI models, and the potential mechanisms underlying its effects. METHODS: Sprague-Dawley rats and HULEC-5a cells were used as study models in the research. To detect related molecules in the study, the real-time polymerase chain reaction analysis, cell counting kit-8 assay, Western blot analysis, and enzyme-linked immunosorbent assay were performed. RESULTS: DCQD could inhibit the expression of LPS-induced inflammatory cytokines, including interleukin-6 (IL-6), IL-8, and tumor necrosis factor-α (TNF-α), in lung tissues and could reduce pulmonary edema by upregulating the expression of AQP-1 and AQP-5 in rats with LPS-induced ALI. Moreover, the results suggested that the toll-like receptor 4 (TLR4)/NF-κB signaling is indispensable for DCQD to increase the expression of AQP-1 and AQP-5 and inhibits the production of IL-6, IL-8, and TNF-α in LPS-induced HULEC-5a cells. CONCLUSION: The results of our study suggested that DCQD suppresses the TLR4/NF-κB signaling pathway, increases the protein expression of AQP-1 and AQP-5, and inhibits the production of inflammatory cytokines, by which it may alleviate the inflammatory reactions in ALI and benefit the treatments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".