Evidence map›Paper›PMID 41892032›Full record

ArticleBiosensors2026

Rapid Identification of Trace Pharmacodynamic Substances in Traditional Chinese Medicine via SERS and Deep Learning.

Huixuan Yang, Mingyuan Chen, Jiayi Wang, Xufei Tong, Huiru Li, Hao Chen, Zengshan Yu, Chunying Zhao, Mingli Wang, Guochao Shi

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biosensors, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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

10 authors.

Huixuan YangHebei International Research Center for Medical-Engineering, Chengde Medical University, Chengde 067000, China.
Mingyuan ChenHebei Key Laboratory of Study and Exploitation of Chinese Medicine, Chengde Medical University, Chengde 067000, China.
Jiayi WangSchool of Nursing, Chengde Medical University, Chengde 067000, China.
Xufei TongHebei International Research Center for Medical-Engineering, Chengde Medical University, Chengde 067000, China.
Huiru LiHebei International Research Center for Medical-Engineering, Chengde Medical University, Chengde 067000, China.
Hao ChenHebei International Research Center for Medical-Engineering, Chengde Medical University, Chengde 067000, China.
Zengshan YuHebei International Research Center for Medical-Engineering, Chengde Medical University, Chengde 067000, China.
Chunying ZhaoHebei Key Laboratory of Study and Exploitation of Chinese Medicine, Chengde Medical University, Chengde 067000, China.
Mingli WangState Key Laboratory of Metastable Materials Science and Technology, Key Laboratory for Microstructural Material Physics of Hebei Province, School of Science, Yanshan University, Qinhuangdao 066004, China.
Guochao ShiHebei International Research Center for Medical-Engineering, Chengde Medical University, Chengde 067000, China.

Funding

Hebei Natural Science Foundation F2022406001Hebei Province College Students' Innovation and Entrepreneurship Training Program 2023024Open Research Project of the Key Laboratory of Traditional Chinese Medicine Research and Development in Hebei Province ZYKF202502
6 · The paper itself

Abstract

In the modernization of traditional Chinese medicine (TCM), trace detection of pharmacodynamic substances faces a critical challenge: insufficient sensitivity, which significantly hinders accurate quality assessment and standardization. Conventional techniques often fail to measure trace components in complex sample matrices. Therefore, the development of a rapid, effective, sensitive, and reliable analytical method, along with a corresponding quality evaluation system, is of great importance. This study used moth wing (MW) scales as a template to fabricate an Ag/MW SERS substrate via magnetron sputtering. The optimal Ag

Indexed as

Deep LearningDrugs, Chinese HerbalMedicine, Chinese TraditionalSpectrum Analysis, RamanAlgorithmsAnimalsPrincipal Component AnalysisReproducibility of ResultsSilverDrugs, Chinese HerbalSilverAg nanoparticlesdeep learningpharmacodynamic substancesurfaced-enhanced Raman scatteringtraditional Chinese medicine

Identifiers

PMID41892032
PMCPMC13023520

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Registered trials

None linked

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.