Evidence map›Paper›PMID 38726156›Full record

ArticleHeliyon2024

High-performance grating-like SERS substrate based on machine learning for ultrasensitive detection of Zexie-Baizhu decoction.

Wenying Zhou, Xue Han, Yanjun Wu, Guochao Shi, Shiqi Xu, Mingli Wang, Wenzhi Yuan, Jiahao Cui, Zelong Li

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Rapid discrimination between wild and cultivatedCurrent research in food science · 2024
    Article
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

9 authors.

Wenying ZhouHebei International Research Center for Medical-Engineering, Chengde Medical University, Chengde, 067000, Hebei, China.
Xue HanDepartment of Neurology, Affiliated Hospital of Chengde Medical University, Chengde, 067000, Hebei, China.
Yanjun WuHebei International Research Center for Medical-Engineering, Chengde Medical University, Chengde, 067000, Hebei, China.
Guochao ShiHebei International Research Center for Medical-Engineering, Chengde Medical University, Chengde, 067000, Hebei, China.
Shiqi XuHebei International Research Center for Medical-Engineering, Chengde Medical University, Chengde, 067000, Hebei, 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.
Wenzhi YuanHebei International Research Center for Medical-Engineering, Chengde Medical University, Chengde, 067000, Hebei, China.
Jiahao CuiHebei International Research Center for Medical-Engineering, Chengde Medical University, Chengde, 067000, Hebei, China.
Zelong LiHebei International Research Center for Medical-Engineering, Chengde Medical University, Chengde, 067000, Hebei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rapid, universal and accurate identification of chemical composition changes in multi-component traditional Chinese medicine (TCM) decoction is a necessary condition for elucidating the effectiveness and mechanism of pharmacodynamic substances in TCM. In this paper, SERS technology, combined with grating-like SERS substrate and machine learning method, was used to establish an efficient and sensitive method for the detection of TCM decoction. Firstly, the grating-like substrate prepared by magnetron sputtering technology was served as a reliable SERS sensor for the identification of TCM decoction. The enhancement factor (EF) of 4-ATP probe molecules was as high as 1.90 × 10

Indexed as

Grating-like structureMachine learningSERSTraditional Chinese medicine decoctionZexie-Baizhu decoction

Identifiers

PMID38726156
PMCPMC11079318

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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.