Evidence map›Paper›PMID 35738204›Full record

ArticleTalanta2022

One-step colorimetric isothermal detection of COVID-19 with AI-assisted automated result analysis: A platform model for future emerging point-of-care RNA/DNA disease diagnosis.

Wansadaj Jaroenram, Itthi Chatnuntawech, Jantana Kampeera, Sukanya Pengpanich, Pornsawan Leaungwutiwong, Benyatip Tondee, Sarawut Sirithammajak, Rapheephat Suvannakad, Pakapreud Khumwan, Sirintip Dangtip and 8 more

Open access · greenAbstract read
In one paragraph

Article in Talanta, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
2.4field-weighted citation impact, top 12% 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

12 citing papers in PubMed, 31 citations in OpenAlex.

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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

18 authors at 3 institutions in 1 country.

Wansadaj JaroenramBioengineering and Sensing Technology Research Team (IBST), National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), Thailand.
Itthi ChatnuntawechNational Nanotechnology Center (NANOTEC), National Science and Technology Development Agency (NSTDA), Thailand.
Jantana KampeeraBioengineering and Sensing Technology Research Team (IBST), National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), Thailand.
Sukanya PengpanichBioengineering and Sensing Technology Research Team (IBST), National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), Thailand.
Pornsawan LeaungwutiwongDepartment of Microbiology and Immunology, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand.
Benyatip TondeeBioengineering and Sensing Technology Research Team (IBST), National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), Thailand.
Sarawut SirithammajakBioengineering and Sensing Technology Research Team (IBST), National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), Thailand.
Rapheephat SuvannakadBioengineering and Sensing Technology Research Team (IBST), National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), Thailand.
Pakapreud KhumwanBioengineering and Sensing Technology Research Team (IBST), National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), Thailand.
Sirintip DangtipBioengineering and Sensing Technology Research Team (IBST), National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), Thailand.
Narong ArunrutBioengineering and Sensing Technology Research Team (IBST), National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), Thailand.
Sirasate BantuchaiMahidol Vivax Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand.
Wang NguitragoolDepartment of Molecular Tropical Medicine and Genetics, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand.
Suchawit WongwaroranNational Nanotechnology Center (NANOTEC), National Science and Technology Development Agency (NSTDA), Thailand; Electrical Engineering Department, University of Victoria, British Columbia, Canada.
Paisan KhanchaititNational Nanotechnology Center (NANOTEC), National Science and Technology Development Agency (NSTDA), Thailand.
Jetsumon SattabongkotMahidol Vivax Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand.
Surat TeerapittayanonNational Nanotechnology Center (NANOTEC), National Science and Technology Development Agency (NSTDA), Thailand. Electronic address: surat.tee@nanotec.or.th.
Wansika KiatpathomchaiBioengineering and Sensing Technology Research Team (IBST), National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), Thailand. Electronic address: wansika@biotec.or.th.
National Science and Technology Development Agency · THMahidol University · THNational Nanotechnology Center · TH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Colorimetric loop-mediated DNA isothermal amplification-based assays have gained momentum in the diagnosis of COVID-19 owing to their unmatched feasibility in low-resource settings. However, the vast majority of them are restricted to proprietary pH-sensitive dyes that limit downstream assay optimization or hinder efficient result interpretation. To address this problem, we developed a novel dual colorimetric RT-LAMP assay using in-house pH-dependent indicators to maximize the visual detection and assay simplicity, and further integrated it with the artificial intelligence (AI) operated tool (RT-LAMP-DETR) to enable a more precise and rapid result analysis in large scale testing. The dual assay leverages xylenol orange (XO) and a newly formulated lavender green (LG) dye for distinctive colorimetric readouts, which enhance the test accuracy when performed and analyzed simultaneously. Our RT-LAMP assay has a detection limit of 50 viral copies/reaction with the cycle threshold (Ct) value ≤ 39.7 ± 0.4 determined by the WHO-approved RT-qPCR assay. RT-LAMP-DETR exhibited a complete concordance with the results from naked-eye observation and RT-qPCR, achieving 100% sensitivity, specificity, and accuracy that altogether render it suitable for ultrasensitive point-of-care COVID-19 screening efforts. From the perspective of pandemic preparedness, our method offers a simpler, faster, and cheaper (∼$8/test) approach for COVID-19 testing and other emerging pathogens with respect to RT-qPCR.

Indexed as

COVID-19Artificial IntelligenceColorimetryCOVID-19 TestingDNAHumansNucleic Acid Amplification TechniquesPoint-of-Care SystemsRNARNA, ViralSARS-CoV-2Sensitivity and SpecificityDNARNARNA, ViralColorimetric RT-LAMPLAMP-DETRMachine learningSARS-CoV-2

Identifiers

PMID35738204
PMCPMC9404558
OpenAlexW4220712719

What OpenQuestion holds

Textmetadata
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.