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Advances in Yupingfeng San Research: Multi-Target Mechanisms and Clinical Evidence

Drug Des Devel Ther. 2026 May 18;20:603491. doi: 10.2147/DDDT.S603491. eCollection 2026.

ABSTRACT

This review systematically summarizes the chemical composition, mechanisms of action, and recent progress in research and clinical applications of Yupingfeng San (YPFS) in various diseases. Research indicates that YPFS contains abundant active constituents-such as flavonoids, coumarins, and terpenoids, and demonstrates diverse biological activities,including immune modulation, anti-inflammatory, antiviral, antibacterial, and antitumor activities. Recent pharmacological and network pharmacology studies have elucidated that YPFS regulates key signaling pathways - such as PI3K-Akt, NF-κB, TLR4/MyD88, and JAK-STAT - through multi-target and multi-pathway mechanisms. These effects contribute to its therapeutic role in allergic rhinitis (AR), asthma, atopic dermatitis (AD), liver cancer, lung cancer, and other conditions. Although limited clinical trials and meta-analyses suggest that YPFS, when combined with conventional therapies, can improve treatment efficacy, relieve symptoms, and demonstrate good safety and tolerability, current research is constrained by low evidence quality and the absence of standardized quality control measures. Future research should prioritize large-scale, multi-center clinical trials and integrate multi-omics approaches to identify the main active components and mechanisms of action.

PMID:42182585 | PMC:PMC13196821 | DOI:10.2147/DDDT.S603491

Advances in Yupingfeng San Research: Multi-Target Mechanisms and Clinical Evidence

Drug Des Devel Ther. 2026 May 18;20:603491. doi: 10.2147/DDDT.S603491. eCollection 2026.

ABSTRACT

This review systematically summarizes the chemical composition, mechanisms of action, and recent progress in research and clinical applications of Yupingfeng San (YPFS) in various diseases. Research indicates that YPFS contains abundant active constituents-such as flavonoids, coumarins, and terpenoids, and demonstrates diverse biological activities,including immune modulation, anti-inflammatory, antiviral, antibacterial, and antitumor activities. Recent pharmacological and network pharmacology studies have elucidated that YPFS regulates key signaling pathways - such as PI3K-Akt, NF-κB, TLR4/MyD88, and JAK-STAT - through multi-target and multi-pathway mechanisms. These effects contribute to its therapeutic role in allergic rhinitis (AR), asthma, atopic dermatitis (AD), liver cancer, lung cancer, and other conditions. Although limited clinical trials and meta-analyses suggest that YPFS, when combined with conventional therapies, can improve treatment efficacy, relieve symptoms, and demonstrate good safety and tolerability, current research is constrained by low evidence quality and the absence of standardized quality control measures. Future research should prioritize large-scale, multi-center clinical trials and integrate multi-omics approaches to identify the main active components and mechanisms of action.

PMID:42182585 | PMC:PMC13196821 | DOI:10.2147/DDDT.S603491

High-fidelity identification of guest species in porous materials

Nature, Published online: 20 May 2026; doi:10.1038/s41586-026-10527-2

A reconstruction method based on Gaussian-apodized single-sideband electron ptychography removes artefacts to enable the high-fidelity identification of guest species in porous materials.

Can China’s Great Green Wall shape efforts to keep the world’s deserts at bay?

Nature, Published online: 15 April 2026; doi:10.1038/d41586-026-01102-w

Grand anti-desertification schemes often fail when trees die and funding dries up — yet one project has broken the mould.

Building evidence-based knowledge graphs from full-text literature for disease-specific biomedical reasoning

arXiv:2603.28325v2 Announce Type: replace-cross Abstract: Biomedical knowledge resources often either preserve evidence as unstructured text or compress it into flat triples that omit study design, provenance, and quantitative support. Here we present EvidenceNet, a framework and dataset for building disease-specific knowledge graphs from full-text biomedical literature. EvidenceNet uses a large language model (LLM)-assisted pipeline to extract experimentally grounded findings as structured evidence nodes, normalize biomedical entities, score evidence quality, and connect evidence records through typed semantic relations. We release two resources: EvidenceNet-HCC with 7,872 evidence records, 10,328 graph nodes, and 49,756 edges, and EvidenceNet-CRC with 6,622 records, 8,795 nodes, and 39,361 edges. Technical validation shows high component fidelity, including 98.3% field-level extraction accuracy, 100.0% high-confidence entity-link accuracy, 87.5% fusion integrity, and 90.0% semantic relation-type accuracy. In downstream evaluation, EvidenceNet improves internal and external retrieval-augmented question answering and retains structural signal for future link prediction and target prioritization. These results establish EvidenceNet as a disease-specific resource for evidence-aware biomedical reasoning and hypothesis generation.
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