Evidence map›Paper›PMID 35558234›Full record

ArticleRSC advances2018

On-line pre-treatment, separation, and nanoelectrospray mass spectrometric determinations for pesticide metabolites and peptides based on a modular microfluidic platform.

Yinyin Hao, Yajing Bao, Xueying Huang, Yijun Hu, Bo Xiong

Open access · goldAbstract read
In one paragraph

Article in RSC advances, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.2field-weighted citation impact, top 46% of its field
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

1 citing paper in PubMed, 8 citations in OpenAlex.

  1. Review
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

5 authors at 2 institutions in 1 country.

Yinyin HaoSchool of Mathematics and Statistics, Wuhan University Wuhan China.
Yajing BaoKey Laboratory of Pesticides & Chemical Biology, Ministry of Education, Institute of Public Health and Molecular Medicine Analysis, College of Chemistry, Central China Normal University Wuhan China bx@mail.ccnu.edu.cn.
Xueying HuangKey Laboratory of Pesticides & Chemical Biology, Ministry of Education, Institute of Public Health and Molecular Medicine Analysis, College of Chemistry, Central China Normal University Wuhan China bx@mail.ccnu.edu.cn.
Yijun HuSchool of Mathematics and Statistics, Wuhan University Wuhan China.
Bo XiongKey Laboratory of Pesticides & Chemical Biology, Ministry of Education, Institute of Public Health and Molecular Medicine Analysis, College of Chemistry, Central China Normal University Wuhan China bx@mail.ccnu.edu.cn.ORCID https://orcid.org/0000-0002-5384-6494
Central China Normal University · CNWuhan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In order to address time-consuming sample pre-treatment and separation prior to mass spectrometry (MS) identifications, highly integrated chips were developed, but damage to any functional unit in these chips would result in complete replacement. Herein, we propose a modular microfluidic platform comprising pre-treatment, liquid chromatography (LC) separation and nanoelectrospray ionization (nESI) chips for on-line enrichment, separation and nESI MS detection of pesticide metabolites and peptides. The pre-treatment chip is applicable in enriching pyridalyl and its metabolites, and it achieves optimal desalination efficiency, 98.5%, for polymerase chain reaction products. Additionally, the LC separation chip was fully characterised, and it demonstrated satisfactory separation efficiency, quantification ability and pressure durability. Finally, the modular microfluidic platform was used to identify the peptides in trypsin-digested casein. Four additional peptides were identified, indicating an improvement in detection ability compared with using off-line zip tips coupled with MS investigations. Because the proposed modular platform can significantly reduce manual work, it would be a potential tool to achieve high throughput and automatic MS identifications with low sample consumptions.

Identifiers

PMID35558234
PMCPMC9091297
OpenAlexW2902430668

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

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.