Evidence map›Paper›PMID 35291265›Full record

ArticleiScience2022

Detection of SARS-CoV-2 with RAPID: A prospective cohort study.

Marcelo D T Torres, Lucas F de Lima, André L Ferreira, William R de Araujo, Paul Callahan, Antonio Dávila, Benjamin S Abella, Cesar de la Fuente-Nunez

Abstract read
In one paragraph

Article in iScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Trial
  2. Review
  3. Article
  4. Review
  5. Article
  6. Review
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

8 authors.

Marcelo D T TorresMachine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Lucas F de LimaMachine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
André L FerreiraMachine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
William R de AraujoPortable Chemical Sensors Lab, Department of Analytical Chemistry, Institute of Chemistry, State University of Campinas - UNICAMP, Campinas, Sao Paulo, Brazil.
Paul CallahanPenn Acute Research Collaboration, Department of Emergency Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Antonio DávilaPenn Acute Research Collaboration, Department of Emergency Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Benjamin S AbellaPenn Acute Research Collaboration, Department of Emergency Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Cesar de la Fuente-NunezMachine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

Funding

Combining chemical and computational tools for predictive models of microbiome communitiesR35GM138201 · NIGMS · UNIVERSITY OF PENNSYLVANIA · PI DE LA FUENTE, CESAR · 2020 to 2024
$1.8M
NIGMS NIH HHS R35 GM138201
6 · The paper itself

Abstract

COVID-19 has killed over 6 million people worldwide. Currently available methods to detect SARS-CoV-2 are limited by their cost and need for multistep sample preparation and trained personnel. Therefore, there is an urgent need to develop fast, inexpensive, and scalable point-of-care diagnostics that can be used for mass testing. Between January and March 2021, we obtained 321 anterior nare swab samples from individuals in Philadelphia (PA, USA). For the Real-time Accurate Portable Impedimetric Detection prototype 1.0 (RAPID) test, anterior nare samples were tested via an electrochemical impedance spectroscopy (EIS) approach. The overall sensitivity, specificity, and accuracy of RAPID in this cohort study were 80.6%, 89.0%, and 88.2%, respectively. We present a rapid, accurate, inexpensive (<$5.00 per unit), and scalable test for diagnosing COVID-19 at the point-of-care. We anticipate that further iterations of this approach will enable widespread deployment, large-scale testing, and population-level surveillance.

Indexed as

BioelectronicsMedical laboratory technologyMedicineSensorVirology

Identifiers

PMID35291265
PMCPMC8913428

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.