Evidence map›Paper›PMID 39407541›Full record

ArticleMolecules (Basel, Switzerland)2024

Determination and Ecological Risk Assessment of Quinolone Antibiotics in Drinking and Environmental Waters Using Fully Automated Disk-Based SPE Coupled with UPLC-MS/MS.

Hongmei Hu, Xingyu Da, Zhenhua Li, Tiejun Li, Xiaoning Zhang, Tianbin Bian, Yanjian Jin, Kaida Xu, Yuanming Guo

Abstract read
In one paragraph

Article in Molecules (Basel, Switzerland), 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
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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.

Hongmei HuKey Laboratory of Sustainable Utilization of Technology Research for Fisheries Resources of Zhejiang Province, Zhejiang Marine Fisheries Research Institute, Zhoushan 316021, China.ORCID 0000-0003-3750-3755
Xingyu DaKey Laboratory of Sustainable Utilization of Technology Research for Fisheries Resources of Zhejiang Province, Zhejiang Marine Fisheries Research Institute, Zhoushan 316021, China.
Zhenhua LiKey Laboratory of Sustainable Utilization of Technology Research for Fisheries Resources of Zhejiang Province, Zhejiang Marine Fisheries Research Institute, Zhoushan 316021, China.ORCID 0009-0002-0219-9669
Tiejun LiKey Laboratory of Sustainable Utilization of Technology Research for Fisheries Resources of Zhejiang Province, Zhejiang Marine Fisheries Research Institute, Zhoushan 316021, China.
Xiaoning ZhangState Key Laboratory of Resource Insects, College of Sericulture, Textile and Biomass Sciences, Southwest University, Chongqing 400715, China.ORCID 0000-0002-1993-2970
Tianbin BianHangzhou Center for Disease Control and Prevention, Hangzhou 310021, China.
Yanjian JinZhejiang Marine Ecology and Environment Monitoring Center, Zhoushan 316021, China.
Kaida XuKey Laboratory of Sustainable Utilization of Technology Research for Fisheries Resources of Zhejiang Province, Zhejiang Marine Fisheries Research Institute, Zhoushan 316021, China.
Yuanming GuoKey Laboratory of Sustainable Utilization of Technology Research for Fisheries Resources of Zhejiang Province, Zhejiang Marine Fisheries Research Institute, Zhoushan 316021, China.

Funding

the Science and technology project of Zhejiang Province, China LTGC24B050013
6 · The paper itself

Abstract

Quinolone antibiotics (QNs) contamination in the aquatic environment is a global public health issue considering their resistance and mobility. In this study, a simple, efficient, and sensitive method was developed for the accurate quantification of fifteen QNs in water using automated disk-based solid-phase extraction (SPE) coupled with ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). By utilizing a 3M SDB-XC disk to enrich QNs from a 1000 mL water sample, the detection limits were improved to 0.008-0.055 ng/L due to the satisfactory enrichment factors of 897-1136, but only requiring about 60 min per six samples. The linearity of the method ranged from 0.05 to 100 μg/L for the 15 QNs, with correlation coefficients of 0.9992-0.9999, and the recoveries were in the range of 81-114%, with relative standard deviations of 0.2-13.3% (n = 6). The developed method was applicable for the quantification of trace QNs at low ng/L levels in drinking and environmental waters. The results showed that no QNs were detected in tap water, while three and four QNs were detected in the river water of Zhoushan and the seawater of Daiquyang and Yueqing Bay, East China, respectively, with a total concentration of 1.600-8.511 ng/L and 1.651-16.421 ng/L, respectively. Among the detected QNs, ofloxacin (OFL) was the predominant compound in river water, while enrofloxacin (ENR) was predominant in seawater. The risk quotient (RQ) results revealed that QNs posed a low risk to crustaceans and fish, but a low-to-medium risk to algae, and OFL presented the main ecological risk factor in river water, while ENR and CIP in seawater. Overall, the proposed automated disk-based SPE-UPLC-MS/MS method is highly efficient and sensitive, making it suitable for routine analysis of QNs in drinking and environmental waters.

Indexed as

Anti-Bacterial AgentsDrinking WaterQuinolonesSolid Phase ExtractionTandem Mass SpectrometryWater Pollutants, ChemicalChromatography, High Pressure LiquidEnvironmental MonitoringLimit of DetectionLiquid Chromatography-Mass SpectrometryRisk AssessmentRiversAnti-Bacterial AgentsDrinking WaterQuinolonesWater Pollutants, Chemicalautomated disk-based solid-phase extractionecological riskquinolone antibioticsultra-performance liquid chromatography–tandem mass spectrometrywater

Identifiers

PMID39407541
PMCPMC11477713

What OpenQuestion holds

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LicenceCC BY
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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.