Evidence map›Paper›PMID 42254506›Full record

ArticleFrontiers in microbiology2026

Comparative evaluation of ELISA, PPIA, and HPLC/MS for microcystin quantification in seven lakes of western Michigan.

Chen Cheng, Xiaonan Tang, Yongjiu Cai, Dailan Deng, Liqiang Xie, Richard R Rediske

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in microbiology, 2026. 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
–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

1 citing paper in PubMed.

  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

6 authors.

Chen ChengState Key Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, China.
Xiaonan TangDepartment of Civil and Environmental Engineering, The George Washington University, Washington, DC, United States.
Yongjiu CaiState Key Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, China.
Dailan DengState Key Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, China.
Liqiang XieSchool of Ecology and Environment, Anhui Normal University, Wuhu, Anhui, China.
Richard R RediskeAnnis Water Resource Institute, Grand Valley State University, Muskegon, MI, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cyanobacterial harmful algal blooms (cHABs) pose escalating risks to aquatic ecosystems and public health through the release of microcystins (MCs), emphasizing the need for accurate detection and quantification. This study compared three widely used MC detection methods-enzyme-linked immunosorbent assay (ELISA), protein phosphatase inhibition assay (PPIA), and high-performance liquid chromatography coupled with mass spectrometry (HPLC/MS)-across about 200 samples from seven drowned river mouth lakes in western Michigan, USA. Results revealed that PPIA and ELISA overestimated the concentration of MC-LR. Specifically, the measurement error of MC-LR equivalent by ELISA and PPIA increased at low concentrations (<0.15 μg/L HPLC/MS-quantified MC-LR) but remained relatively stable at higher levels. Further analysis revealed that among the MC congeners, MC-RR showed the highest contribution rate (60%) to the measurement error by ELISA, while MC-YR showed the highest contribution rate (69%) for PPIA. These discrepancies correlated strongly (

Indexed as

algal bloomsELISAHPLC/MSmicrocystinsPPIA

Identifiers

PMID42254506
PMCPMC13233501

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Registered trials

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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.