Evidence map›Paper›PMID 37363301›Full record

ArticleSensors and actuators. B, Chemical2023

Dual-clamped one-pot SERS-based biosensors for rapid and sensitive detection of SARS-CoV-2 using portable Raman spectrometer.

Kiran Kaladharan, Kuan-Hung Chen, Pin-Han Chen, Venkanagouda S Goudar, Tseren-Onolt Ishdorj, Tuhin Subhra Santra, Fan-Gang Tseng

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Sensors and actuators. B, Chemical, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
2.1field-weighted citation impact, top 14% 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

5 citing papers in PubMed, 19 citations in OpenAlex.

  1. Review
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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

7 authors at 3 institutions in 3 countries.

Kiran KaladharanDepartment of Engineering and System Science, National Tsing Hua University, Taiwan, ROC.
Kuan-Hung ChenDepartment of Engineering and System Science, National Tsing Hua University, Taiwan, ROC.
Pin-Han ChenDepartment of Engineering and System Science, National Tsing Hua University, Taiwan, ROC.
Venkanagouda S GoudarDepartment of Engineering and System Science, National Tsing Hua University, Taiwan, ROC.
Tseren-Onolt IshdorjSchool of Information and Communication Technology, Mongolian University of Science and Technology, Mongolia.
Tuhin Subhra SantraDepartment of Engineering Design, Indian Institute of Technology Madras, India.
Fan-Gang TsengDepartment of Engineering and System Science, National Tsing Hua University, Taiwan, ROC.
National Tsing Hua University · TWIndian Institute of Technology Madras · INMongolian University of Science and Technology · MN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rapid and sensitive diagnostics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is of utmost importance to control the widespread coronavirus disease 2019 (COVID-19) upsurge. This study demonstrated a novel one-pot surface-enhanced Raman scattering (SERS) based immunoassay to detect SARS-CoV-2, without any washing process using a portable Raman spectrometer. The SERS-immune assay was designed using a regular digital versatile disk (DVD) substrate integrated with Raman reporter labeled silver nanoparticles for double clamping effects. The disks were molded to form nanopillar arrays and coated with silver film to enhance the sensitivity of immunoassay. The SERS platform demonstrated a limit of detection (LoD) up to 50 pg mL

Indexed as

DVD substrateOne-Pot reactionPortableRapid detectionSARS-CoV-2SERS

Identifiers

PMID37363301
PMCPMC10276524
OpenAlexW4381051358

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.