Evidence map›Paper›PMID 34379400›Full record

ArticleACS applied materials & interfaces2021

Developing a SARS-CoV-2 Antigen Test Using Engineered Affinity Proteins.

Seunghyeon Kim, Emma Yee, Eric A Miller, Yining Hao, Dousabel M Y Tay, Ki-Joo Sung, Huan Jia, Joseph M Johnson, Mohsan Saeed, Charles R Mace and 2 more

Open access · greenAbstract read
PubMed Publisher
In one paragraph

Article in ACS applied materials & interfaces, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.0field-weighted citation impact, top 20% 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

4 citing papers in PubMed, 20 citations in OpenAlex.

  1. Backstage Heroes-Yeast in COVID-19 Research.International journal of molecular sciences · 2024
    Review
  2. Review
  3. Nanobody-Functionalized Cellulose for Capturing SARS-CoV-2.Applied and environmental microbiology · 2022
    Article
  4. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors at 6 institutions in 2 countries.

Seunghyeon KimDepartment of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.ORCID https://orcid.org/0000-0001-6515-2679
Emma YeeDepartment of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.
Eric A MillerDepartment of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.
Yining HaoDepartment of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.
Dousabel M Y TayDepartment of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.
Ki-Joo SungDepartment of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.
Huan JiaAntimicrobial Resistance Integrated Research Group, Singapore-MIT Alliance for Research and Technology, 1 CREATE Way, Singapore 138602, Singapore.
Joseph M JohnsonQuanterix Corporation, Billerica, Massachusetts 01821, United States.
Mohsan SaeedDepartment of Biochemistry, Boston University School of Medicine, Boston, Massachusetts 02118, United States.
Charles R MaceDepartment of Chemistry, Tufts University, Medford, Massachusetts 02155, United States.ORCID https://orcid.org/0000-0003-3410-5014
Deniz Yüksel Yurt3M Company, St. Paul, Minnesota 55133, United States.
Hadley D SikesDepartment of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.ORCID https://orcid.org/0000-0002-7096-138X
Massachusetts Institute of Technology · USSingapore-MIT Alliance for Research and Technology · SG3M (United States) · USBoston University · USQuanterix (United States) · USTufts University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The ongoing COVID-19 pandemic has clearly established how vital rapid, widely accessible diagnostic tests are in controlling infectious diseases and how difficult and slow it is to scale existing technologies. Here, we demonstrate the use of the rapid affinity pair identification via directed selection (RAPIDS) method to discover multiple affinity pairs for SARS-CoV-2 nucleocapsid protein (N-protein), a biomarker of COVID-19, from in vitro libraries in 10 weeks. The pair with the highest biomarker sensitivity was then integrated into a 10 min, vertical-flow cellulose paper test. Notably, the as-identified affinity proteins were compatible with a roll-to-roll printing process for large-scale manufacturing of tests. The test achieved 40 and 80 pM limits of detection in 1× phosphate-buffered saline (mock swab) and saliva matrices spiked with cell-culture-generated SARS-CoV-2 viruses and is also capable of detection of N-protein from characterized clinical swab samples. Hence, this work paves the way toward the mass production of cellulose paper-based assays which can address the shortages faced due to dependence on nitrocellulose and current manufacturing techniques. Further, the results reported herein indicate the promise of RAPIDS and engineered binder proteins for the timely and flexible development of clinically relevant diagnostic tests in response to emerging infectious diseases.

Indexed as

Antigens, ViralBiomarkersBiosensing TechniquesCelluloseCOVID-19COVID-19 Serological TestingEnzyme-Linked Immunosorbent AssayFluorescent DyesHumansMicrofluidic Analytical TechniquesNucleocapsid ProteinsPeptide LibraryProtein BindingSARS-CoV-2Antigens, ViralBiomarkersCelluloseFluorescent DyesNucleocapsid ProteinsPeptide Libraryaffinity proteincelluloseenzyme-linked immunosorbent assayslibrary screeningpeptides and proteins

Identifiers

PMID34379400
OpenAlexW3162268601

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.