Evidence map›Paper›PMID 35126481›Full record

ArticleScience China. Chemistry2022

An automated nucleic acid detection platform using digital microfluidics with an optimized Cas12a system.

Zhen Sun, Kang-Feng Lin, Ze-Hang Zhao, Yang Wang, Xin-Xin Hong, Jian-Guang Guo, Qing-Yu Ruan, Lian-Yu Lu, Xiao Li, Rui Zhang and 2 more

Abstract read
In one paragraph

Article in Science China. Chemistry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

  1. Review
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  4. Recent advances in organocatalytic atroposelective reactions.Beilstein journal of organic chemistry · 2025
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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

12 authors.

Zhen Sun *State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network and Engineering Research Center of Molecular Diagnostics of the Ministry of Education, School of Life Sciences, Xiamen University, Xiamen, 361005 China.
Kang-Feng Lin *State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network and Engineering Research Center of Molecular Diagnostics of the Ministry of Education, School of Life Sciences, Xiamen University, Xiamen, 361005 China.
Ze-Hang Zhao *State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network and Engineering Research Center of Molecular Diagnostics of the Ministry of Education, School of Life Sciences, Xiamen University, Xiamen, 361005 China.
Yang WangMOE Key Laboratory of Spectrochemical Analysis & Instrumentation, Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Engineering, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005 China.
Xin-Xin HongState Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network and Engineering Research Center of Molecular Diagnostics of the Ministry of Education, School of Life Sciences, Xiamen University, Xiamen, 361005 China.
Jian-Guang GuoState Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network and Engineering Research Center of Molecular Diagnostics of the Ministry of Education, School of Life Sciences, Xiamen University, Xiamen, 361005 China.
Qing-Yu RuanMOE Key Laboratory of Spectrochemical Analysis & Instrumentation, Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Engineering, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005 China.
Lian-Yu LuMOE Key Laboratory of Spectrochemical Analysis & Instrumentation, Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Engineering, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005 China.
Xiao LiState Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network and Engineering Research Center of Molecular Diagnostics of the Ministry of Education, School of Life Sciences, Xiamen University, Xiamen, 361005 China.
Rui ZhangDepartment of Thoracic Surgery, The First Affiliated Hospital, Xiamen University, Xiamen, 361005 China.
Chao-Yong YangMOE Key Laboratory of Spectrochemical Analysis & Instrumentation, Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Engineering, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005 China.
Bo-An LiState Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network and Engineering Research Center of Molecular Diagnostics of the Ministry of Education, School of Life Sciences, Xiamen University, Xiamen, 361005 China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Outbreaks of both influenza virus and the novel coronavirus SARS-CoV-2 are serious threats to human health and life. It is very important to establish a rapid, accurate test with large-scale detection potential to prevent the further spread of the epidemic. An optimized RPA-Cas12a-based platform combined with digital microfluidics (DMF), the RCD platform, was established to achieve the automated, rapid detection of influenza viruses and SARS-CoV-2. The probe in the RPA-Cas12a system was optimized to produce maximal fluorescence to increase the amplification signal. The reaction droplets in the platform were all at the microliter level and the detection could be accomplished within 30 min due to the effective mixing of droplets by digital microfluidic technology. The whole process from amplification to recognition is completed in the chip, which reduces the risk of aerosol contamination. One chip can contain multiple detection reaction areas, offering the potential for customized detection. The RCD platform demonstrated a high level of sensitivity, specificity (no false positives or negatives), speed (≤30 min), automation and multiplexing. We also used the RCD platform to detect nucleic acids from influenza patients and COVID-19 patients. The results were consistent with the findings of qPCR. The RCD platform is a one-step, rapid, highly sensitive and specific method with the advantages of digital microfluidic technology, which circumvents the shortcomings of manual operation. The development of the RCD platform provides potential for the isothermal automatic detection of nucleic acids during epidemics. Electronic Supplementary Material: Supplementary material is available in the online version of this article at 10.1007/s11426-021-1169-1.

Indexed as

Cas12adigital microfluidicsinfluenza virusnucleic acid detectionSARS-CoV-2

Identifiers

PMID35126481
PMCPMC8809245

What OpenQuestion holds

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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.