Evidence map›Paper›PMID 36834622›Full record

ArticleInternational journal of molecular sciences2023

Identify the Virus-like Models for COVID-19 as Bio-Threats: Combining Phage Display, Spectral Detection and Algorithms Analysis.

Yuting Wu, Zhiwei Liu, Sihan Mao, Bing Liu, Zhaoyang Tong

Abstract read
In one paragraph

Article in International journal of molecular sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Yuting WuState Key Laboratory of NBC Protection for Civilian, Beijing 102205, China.ORCID 0000-0002-7720-8024
Zhiwei LiuState Key Laboratory of NBC Protection for Civilian, Beijing 102205, China.
Sihan MaoSchool of Chemistry and Chemical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
Bing LiuState Key Laboratory of NBC Protection for Civilian, Beijing 102205, China.
Zhaoyang TongState Key Laboratory of NBC Protection for Civilian, Beijing 102205, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rapid identification and recognition of COVID-19 have been challenging since its outbreak. Multiple methods were developed to realize fast monitoring early to prevent and control the pandemic. In addition, it is difficult and unrealistic to apply the actual virus to study and research because of the highly infectious and pathogenic SARS-CoV-2. In this study, the virus-like models were designed and produced to replace the original virus as bio-threats. Three-dimensional excitation-emission matrix fluorescence and Raman spectroscopy were employed for differentiation and recognition among the produced bio-threats and other viruses, proteins, and bacteria. Combined with PCA and LDA analysis, the identification of the models for SARS-CoV-2 was achieved, reaching a correction of 88.9% and 96.3% after cross-validation, respectively. This idea might provide a possible pattern for detecting and controlling SARS-CoV-2 from the perspective of combining optics and algorithms, which could be applied in the early-warning system against COVID-19 or other bio-threats in the future.

Indexed as

BacteriophagesCOVID-19Disease OutbreaksHumansSARS-CoV-2identificationPCA-LDAphage displaySARS-CoV-2spectroscopyvirus-like models

Identifiers

PMID36834622
PMCPMC9967019

What OpenQuestion holds

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Registered trials

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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.