Evidence map›Paper›PMID 33972921›Full record

ReviewResults in chemistry2021

A concise discussion on the potential spectral tools for the rapid COVID-19 detection.

Abhijeet Mohanty, Adarsh P Fatrekar, Saravanan Krishnan, Amit A Vernekar

Open access · goldAbstract readReview
In one paragraph

Review in Results in chemistry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.2field-weighted citation impact, top 47% 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

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. 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

4 authors at 2 institutions in 1 country.

Abhijeet MohantyInorganic and Physical Chemistry Laboratory, Council of Scientific and Industrial Research (CSIR)-Central Leather Research Institute (CLRI), Chennai 600020, India.
Adarsh P FatrekarInorganic and Physical Chemistry Laboratory, Council of Scientific and Industrial Research (CSIR)-Central Leather Research Institute (CLRI), Chennai 600020, India.
Saravanan KrishnanDhanvantari Nano Ayushadi Pvt. Ltd., Chennai 600017, India.
Amit A VernekarInorganic and Physical Chemistry Laboratory, Council of Scientific and Industrial Research (CSIR)-Central Leather Research Institute (CLRI), Chennai 600020, India.
Central Leather Research Institute · INDhanvantari Ayurveda College Hospital and Research Centre · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Developing robust methods to detect the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), a causative agent for the current global health pandemic, is an exciting area of research. Nevertheless, the currently used conventional reverse transcription-polymerase chain reaction (RT-PCR) technique in COVID-19 detection endures with some inevitable limitations. Consequently, the establishment of rapid diagnostic tools and quick isolation of infected patients is highly essential. Furthermore, the requirement of point-of-care testing is the need of the hour. Considering this, we have provided a brief review of the use of very recently reported robust spectral tools for rapid COVID-19 detection. The spectral tools include, colorimetric reverse transcription loop-mediated isothermal amplification (RT-LAMP) and matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS), with the admittance of principal component analysis (PCA) and machine learning (ML) for meeting the high-throughput and fool-proof platforms for the detection of SARS-CoV-2, are reviewed. Recently, these techniques have been readily applied to screen a large number of suspected patients within a short period and they demonstrated higher sensitivity for the detection of COVID-19 patients from unaffected human subjects.

Indexed as

COVID-19Machine learningMALDI-MSRT-LAMPRT-PCRSARS-CoV-2

Identifiers

PMID33972921
PMCPMC8099787
OpenAlexW3158490489

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.