Evidence map›Paper›PMID 35774504›Full record

ArticleFrontiers in genetics2022

A Bioinformatics Approach to Investigate Structural and Non-Structural Proteins in Human Coronaviruses.

Vittoria Cicaloni, Filippo Costanti, Arianna Pasqui, Monica Bianchini, Neri Niccolai, Pietro Bongini

Open access · goldAbstract read
In one paragraph

Article in Frontiers in genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
2.8field-weighted citation impact, top 8% 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

11 citing papers in PubMed, 28 citations in OpenAlex.

  1. Article
  2. In Silico Analysis of CMolecules (Basel, Switzerland) · 2025
    Article
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  10. Functional SARS-CoV-2 cross-reactive CD4Proceedings of the National Academy of Sciences of the United States of America · 2023
    Article
  11. Article
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

6 authors at 3 institutions in 1 country.

Vittoria CicaloniToscana Life Science Foundation, Siena, Italy.
Filippo CostantiDepartment of Information Engineering and Mathematics, University of Siena, Siena, Italy.
Arianna PasquiToscana Life Science Foundation, Siena, Italy.
Monica BianchiniDepartment of Information Engineering and Mathematics, University of Siena, Siena, Italy.
Neri NiccolaiDepartment of Biotechnology, Chemistry, and Pharmacy, University of Siena, Siena, Italy.
Pietro BonginiDepartment of Information Engineering and Mathematics, University of Siena, Siena, Italy.
University of Siena · ITToscana Life Sciences · ITUniversity of Florence · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent studies confirmed that people unexposed to SARS-CoV-2 have preexisting reactivity, probably due to previous exposure to widely circulating common cold coronaviruses. Such preexistent reactivity against SARS-CoV-2 comes from memory T cells that can specifically recognize a SARS-CoV-2 epitope of structural and non-structural proteins and the homologous epitopes from common cold coronaviruses. Therefore, it is important to understand the SARS-CoV-2 cross-reactivity by investigating these protein sequence similarities with those of different circulating coronaviruses. In addition, the emerging SARS-CoV-2 variants lead to an intense interest in whether mutations in proteins (especially in the spike) could potentially compromise vaccine effectiveness. Since it is not clear that the differences in clinical outcomes are caused by common cold coronaviruses, a deeper investigation on cross-reactive T-cell immunity to SARS-CoV-2 is crucial to examine the differential COVID-19 symptoms and vaccine performance. Therefore, the present study can be a starting point for further research on cross-reactive T cell recognition between circulating common cold coronaviruses and SARS-CoV-2, including the most recent variants Delta and Omicron. In the end, a deep learning approach, based on Siamese networks, is proposed to accurately and efficiently calculate a BLAST-like similarity score between protein sequences.

Indexed as

cross-reactivitylong short-term memoriesSARS-CoV-2Siamese networkssimilarity score

Identifiers

PMID35774504
PMCPMC9237418
OpenAlexW4282824856

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.