Evidence map›Paper›PMID 38843447›Full record

ArticleACS sensors2024

Rapid Detection of SARS-CoV-2 Variants Using an Angiotensin-Converting Enzyme 2-Based Surface-Enhanced Raman Spectroscopy Sensor Enhanced by CoVari Deep Learning Algorithms.

Yanjun Yang, Jiaheng Cui, Dan Luo, Jackelyn Murray, Xianyan Chen, Sebastian Hülck, Ralph A Tripp, Yiping Zhao

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in ACS sensors, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–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

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. 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.

Yanjun YangDepartment of Physics and Astronomy, The University of Georgia, Athens, Georgia 30602, United States.ORCID 0000-0002-1822-7364
Jiaheng CuiSchool of Electrical and Computer Engineering, College of Engineering, The University of Georgia, Athens, Georgia 30602, United States.
Dan LuoDepartment of Statistics, The University of Georgia, Athens, Georgia 30602, United States.
Jackelyn MurrayDepartment of Infectious Diseases, College of Veterinary Medicine, The University of Georgia, Athens, Georgia 30602, United States.
Xianyan ChenDepartment of Epidemiology & Biostatistics, College of Public Health, The University of Georgia, Athens, Georgia 30602, United States.
Sebastian HülckTec5USA Inc., Plainview, New York 11803, United States.
Ralph A TrippDepartment of Infectious Diseases, College of Veterinary Medicine, The University of Georgia, Athens, Georgia 30602, United States.
Yiping ZhaoDepartment of Physics and Astronomy, The University of Georgia, Athens, Georgia 30602, United States.ORCID 0000-0002-3710-4159

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

An integrated approach combining surface-enhanced Raman spectroscopy (SERS) with a specialized deep learning algorithm to rapidly and accurately detect and quantify SARS-CoV-2 variants is developed based on an angiotensin-converting enzyme 2 (ACE2)-functionalized AgNR@SiO

Indexed as

Angiotensin-Converting Enzyme 2COVID-19Deep LearningSARS-CoV-2Spectrum Analysis, RamanAlgorithmsBiosensing TechniquesHumansLimit of DetectionMetal NanoparticlesSilicon DioxideSilverACE2 protein, humanAngiotensin-Converting Enzyme 2Silicon DioxideSilverangiotensin-converting enzyme 2 (ACE2)convolutional neural networkdeep learningSARS-CoV-2 detectionsilver nanorod arraysurface-enhanced Raman scattering (SERS)

Identifiers

PMID38843447
PMCPMC11217934

What OpenQuestion holds

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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.