Network pharmacology prediction and experimental validation to explore the potential mechanism of Danggui Shaoyao San against Acute Kidney Injury
Bibliographic record
Abstract
ETHNOPHARMACOLOGICAL RELEVANCE: Acute kidney injury (AKI) is a growing worldwide health concern. Danggui Shaoyao San (DGSYS) was an frequently-used representative prescription to "promote blood and water and harmonize the body" in traditional Chinese medicine, and its underlying mechanism against AKI remains to be elucidated. AIM OF THE STUDY: To investigate the protective effect and potential molecular mechanism of DGSYS in alleviating AKI by network pharmacology and experiment validation. MATERIALS AND METHODS: DGSYS was analyzed and separated by UHPLC. Network pharmacology, molecular docking and molecular dynamics simulation were used to screen the active ingredient-target pathway of DGSYS for AKI treatment. Meanwhile, the predictions were experimentally validated both in vivo and in vitro. RESULTS: 30 bioactive compounds and 821 non-repetitive targets were screened for DGSYS, and 292 potential DGSYS-AKI intersection targets were identified. According to the protein-protein interaction results and GO and KEGG analyses, we found that apoptosis mediated by SRC pathway exerted a vital role in DGSYS against AKI. In vivo experiments indicated that DGSYS mitigated I/R-induced kidney injury by suppressing SRC signaling pathway. Molecular docking and molecular dynamics simulation revealed that paeoniflorin (PF) have good binding affinity to SRC. Subsequently, in vitro experiments suggested that PF was the key bioactive compound against AKI from DGSYS by inhibiting SRC/ERK1/2 signaling pathway. CONCLUSION: We exhibited the promising anti-AKI mechanism of DGSYS, and our findings also opened up an avenue for future basic experimental validation, indicating a novel research direction.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".