Evidence map›Paper›PMID 41415071›Full record

ArticleAmerican journal of translational research2025

Exploring the potential of Buzhong Yiqi decoction in the treatment of membranous nephropathy: a comprehensive study combining bioinformatics, network pharmacology, and experimental validation.

Yihan Wang, Ruizhi Tan, Qi Liu, Jian Jia, Junming Fan, Li Wang

Abstract read
In one paragraph

Article in American journal of translational research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Yihan WangDepartment of Nephrology, The Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University Luzhou 646000, Sichuan, China.
Ruizhi TanResearch Center of Integrated Traditional Chinese and Western Medicine, The Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University Luzhou 646000, Sichuan, China.
Qi LiuDepartment of Neurosurgery, The Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University Luzhou 646000, Sichuan, China.
Jian JiaResearch Center of Integrated Traditional Chinese and Western Medicine, The Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University Luzhou 646000, Sichuan, China.
Junming FanDepartment of Nephrology, The Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University Luzhou 646000, Sichuan, China.
Li WangResearch Center of Integrated Traditional Chinese and Western Medicine, The Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University Luzhou 646000, Sichuan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

objectiveBuzhong Yiqi Decoction (BZYQ), a classic Chinese herbal formula, demonstrates clinical efficacy in treating membranous nephropathy (MN), yet its molecular mechanisms remains undefined. This study aims to uncover the therapeutic mechanisms of BZYQ against MN at the molecular level.

methodsBZYQ components/targets were retrieved from TCMSP and UniProtKB, while MN-related targets were obtained from GeneCards and GEO. Core targets were identified via Protein-Protein Interaction (PPI) analysis and machine learning (ML). Immune cell infiltration was analyzed using CIBERSORT, and diagnostic values were evaluated via Receiver Operating Characteristic (ROC) curves. Molecular docking validated interactions between core targets and BZYQ's active ingredients, with efficacy and mechanisms further verified in a cationic bovine serum albumin-induced mouse MN model.

resultsScreening identified

conclusionBZYQ emerges as a promising therapeutic for MN. Elucidating its anti-MN mechanisms provides a theoretical basis for clinical translation.

Indexed as

animal experimentsBioinformatics analysisBuzhong Yiqi decoctionimmune cell infiltrationmachine learningmembranous nephropathy

Identifiers

PMID41415071
PMCPMC12709325

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.