ArticleTalanta2022
Rapid electrochemical immunodetection of SARS-CoV-2 using a pseudo-typed vesicular stomatitis virus model.
Article in Talanta, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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.
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.
Who cites it
7 citing papers in PubMed, 15 citations in OpenAlex.
- Article
- Impact of nanotechnology on conventional and artificial intelligence-based biosensing strategies for the detection of viruses.Discover nano · 2023Review
- Emerging trends in point-of-care biosensing strategies for molecular architectures and antibodies of SARS-CoV-2.Biosensors & bioelectronics: X · 2023Review
- Review
- Impedimetric Detection Based on Label-Free Immunoassay Developed for Targeting Spike S1 Protein of SARS-CoV-2.Diagnostics (Basel, Switzerland) · 2022Article
- The role of electrochemical biosensors in SARS-CoV-2 detection: a bibliometrics-based analysis and review.RSC advances · 2022Review
- A Label-Free Electrochemical Impedimetric Immunosensor with Biotinylated-Antibody for SARS-CoV-2 Nucleoprotein Detection in Saliva.Biosensors · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The COVID-19 pandemic has highlighted the need for reliable and accurate diagnostic tools that provide quantitative results at the point of care. Real-time RT-PCR requires large laboratories, a skilled workforce, complex and costly equipment, and labor-intensive sample processing. Despite tremendous efforts, scaling up RT-PCR tests is seemingly unattainable. To date, hundreds of millions of COVID-19 tests have been performed globally, but the demand for timely, accurate testing continues to outstrip supply. Antigen-based rapid diagnostic testing is emerging as an alternative to RT-PCR. However, the performance of these tests, namely their sensitivity, is still inadequate. To overcome the limitations of currently employed diagnostic tests, new tools that are both sensitive and scalable are urgently needed. We have developed a miniaturized electrochemical biosensor based on the integration of specific monoclonal antibodies with a biochip and a measurement platform, and applied it in the detection of Spike S1 protein, the binding protein of SARS-CoV-2. Using electrochemical impedance spectroscopy, quantitative detection of sub-nanomolar concentrations of Spike S1 was demonstrated, exhibiting a broad detection range. To demonstrate the applicability of the biosensor, we have further developed a SARS-CoV-2 pseudovirus based on Spike protein-pseudo-typed VSV platform. Specific detection of different concentrations of pseudovirus particles was feasible in <30 min. This new tool may largely contribute to the fight against COVID-19 by enabling intensive testing to be performed and alleviating most of the hurdles that plague current diagnostics.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.