Evidence map›Paper›PMID 39180572›Full record

ArticleJournal of fluorescence2025

A Sensitive Fluorescence Analysis Method of Pathogenic Microorganisms Based on Silicon Photomultiplier.

Yi Chen, Licheng Dai, Fei Zhang, Tianqi Zhao, Shangzhong Jin

Abstract read
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In one paragraph

Article in Journal of fluorescence, 2025. 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

5 authors.

Yi ChenCollege of Optics and Electronic Science and Technology, China Jiliang University, Hangzhou, China.ORCID http://orcid.org/0000-0002-7196-6895
Licheng DaiCollege of Optics and Electronic Science and Technology, China Jiliang University, Hangzhou, China.ORCID http://orcid.org/0009-0002-5933-2205
Fei ZhangCollege of Optics and Electronic Science and Technology, China Jiliang University, Hangzhou, China.
Tianqi ZhaoCollege of Optics and Electronic Science and Technology, China Jiliang University, Hangzhou, China. 18A0402151@cjlu.edu.cn.ORCID http://orcid.org/0000-0001-5052-9680
Shangzhong JinCollege of Optics and Electronic Science and Technology, China Jiliang University, Hangzhou, China. jinsz@cjlu.edu.cn.

Funding

Fundamental Public Welfare Research Project of Zhejiang Province LY22F040003
6 · The paper itself

Abstract

The monitoring of pathogenic microorganisms in water is important for public health and disease outbreaks prediction. Recently, optical detection techniques have drawn much attention due to the advantages of rapid response, security and high sensitivity. In this paper, a fluorescence spectrometer based on 375 nm exciting laser and the microchannel liquid sample flow technology is proposed. The 4 × 4 narrowband filter array was coupled to a Silicon Photomultiplier (SiPM) array with single-photon sensitivity. B500 fluorescent microspheres and Escherichia coli were used for performance evaluation of the spectrometer. As a result, it is feasible to use random particle counting method to detect the bacteria concentration level in water even low to several CFU/mL. In addition, based on Python tools and neural network algorithm models, the fluorescence spectra of different kinds of substances (biotic and abiotic) can be classified with an accuracy of more than 97%. The method was successfully applied to tap water samples. The results suggest that the proposed method is applicable for on-site bacteria detection.

Indexed as

Escherichia coliSiliconWater MicrobiologyMicrospheresSpectrometry, FluorescenceSiliconBacteria DetectionCounting MethodLaser-induced FluorescenceNarrowband Filter ArraySilicon Photomultiplier

Identifiers

What OpenQuestion holds

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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.