SynthesisFrontiers in medicine2025
Q-marker identification strategies in traditional Chinese medicines: a systematic review of research from 2020 to 2024.
Synthesis in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Article
- A Principle-Guided Multistep Workflow for Screening Potential Quality Marker Candidates of Sanqi Shangyao Tablet Based on UHPLC-Q-Orbitrap MS, In Silico Screening, and In Vitro Validation.Biomedical chromatography : BMC · 2026Article
- TCM-Derived Small Molecules Targeting Metabolic Vulnerabilities in NSCLC: Ferroptosis-Centered Mechanisms and Emerging Cuproptosis- and Disulfidptosis-Related Vulnerabilities.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Natural therapeutics and traditional formulas targeting macrophage polarization in the fibrotic niche of idiopathic pulmonary fibrosis.Frontiers in immunology · 2026Review
- Molecular dynamics simulation in traditional Chinese medicine research: from molecular mechanisms to multiscale validation.Frontiers in chemistry · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The concept of "quality markers" (Q-markers) has emerged as a key solution to address limitations in the evaluation and standardization of traditional herbal medicines. Despite the introduction of various Q-marker identification strategies, methodological inconsistencies and a lack of standardization continue to pose challenges. Objectives: This review aims to systematically organize and evaluate Q-marker selection strategies published over the past 5 years and propose an optimal approach based on a comparative analysis of their strengths and limitations. Methods: A comprehensive literature search was performed on the Web of Science and PubMed for studies published between January 2020 and December 2024 using keywords related to Q-marker identification in traditional prescriptions. After removing duplicates and screening for relevance, the eligible studies were systematically reviewed. Key information, including the prescription name, therapeutic targets, methodological steps for Q-marker selection, and the final identified Q-markers, was extracted and organized into summary tables. Based on the analysis, the advantages and limitations of each strategy were evaluated. Results: The studies were categorized into four representative strategies: [S1] mechanism-driven validation, which relies on network pharmacology and bioassays to align compounds with disease pathways (22 cases, 36.67%); [S2] profile-effect correlation modeling, which uses statistical and machine learning tools to link chemical composition with pharmacodynamic outcomes (24 cases, 40%); [S3] Conclusion: This review can provide valuable insights to guide future research and development of traditional herbal medicines, particularly in the context of quality control and innovative drug discovery. The proposed framework improves biological relevance and practical applicability and may serve as a scalable model for the quality assessment of multi-component herbal systems and complex pharmacological formulations.
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Registered trials
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.