Evidence map›Paper›PMID 34274470›Full record

ArticleGene2021

Therapeutic perceptions in antisense RNA-mediated gene regulation for COVID-19.

Sabrina Ferreira de Jesus, Laércio Ives Santos, João Felício Rodrigues Neto, Thallyta Maria Vieira, João Batista Mendes, Marcos Flavio Silveira Vasconcelos D'angelo, André Luiz Sena Guimaraes

Open access · greenAbstract read
In one paragraph

Article in Gene, 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
0.3field-weighted citation impact, top 39% 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, 5 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

7 authors at 2 institutions in 1 country.

Sabrina Ferreira de JesusDepartment of Dentistry, State Universit of Montes Claros, Brazil.
Laércio Ives SantosInstituto Federal do Norte de Minas Gerais, Montes Claros Minas Gerais, Brazil; Department of Computer Science, Universidade Estadual de Montes Claros, Montes Claros Minas Gerais, Brazil.
João Felício Rodrigues NetoDepartament of Medicine, Universidade Estadual de Montes Claros, Montes Claros Minas Gerais, Brazil.
Thallyta Maria VieiraDepartament of Biology, Universidade Estadual de Montes Claros, Montes Claros Minas Gerais, Brazil.
João Batista MendesDepartment of Computer Science, Universidade Estadual de Montes Claros, Montes Claros Minas Gerais, Brazil.
Marcos Flavio Silveira Vasconcelos D'angeloDepartment of Computer Science, Universidade Estadual de Montes Claros, Montes Claros Minas Gerais, Brazil. Electronic address: marcos.dangelo@unimontes.br.
André Luiz Sena GuimaraesDepartament of Medicine, Universidade Estadual de Montes Claros, Montes Claros Minas Gerais, Brazil. Electronic address: andreluizguimaraes@gmail.com.
Universidade Estadual de Montes Claros · BRInstituto Federal de Educação Ciência e Tecnologia do Norte de Minas Gerais · BR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

COVID-19 was first reported in Wuhan, China, in December 2019. It is widely accepted that the world will not return to its prepandemic normality until safe and effective vaccines are available and a global vaccination program has been successfully implemented. Antisense RNAs are single-stranded RNAs that occur naturally or are synthetic and enable hybridizing and protein-blocking translation. Therefore, the main objective of this study was to identify target markers in the RNA of the severe acute respiratory syndrome coronavirus, or SARS-CoV-2, with a length between 21 and 28 bases that could enable the development of vaccines and therapies based on antisense RNA. We used a search algorithm in C language to compare 3159 complete nucleotide sequences from SARS-CoV-2 downloaded from the repository of the National Center for Biotechnology Information. The objective was to verify whether any common sequences were present in all 3159 strains of SARS-CoV-2. In the first of three datasets (SARS-CoV-2), the algorithm found two sequences each of 21 nucleotides (Sequence 1: CTACTGAAGCCTTTGAAAAAA; Sequence 2: TGTGGTTATACCTACTAAAAA). In the second dataset (SARS-CoV) and third dataset (MERS-CoV), no sequences of size N between 21 and 28 were found. Sequence 1 and Sequence 2 were input into BLAST® ≫ blastn and recognized by the platform. The gene identified by the sequences found by the algorithm was the ORF1ab region of SARS-CoV-2. Considerable progress in antisense RNA research has been made in recent years, and great achievements in the application of antisense RNA have been observed. However, many mechanisms of antisense RNA are not yet understood. Thus, more time and money must be invested into the development of therapies for gene regulation mediated by antisense RNA to treat COVID-19 as no effective therapy for this disease has yet been found.

Indexed as

AlgorithmsComputer SimulationCOVID-19Gene Expression Regulation, ViralHumansRNA, AntisenseSARS-CoV-2RNA, AntisenseAntisense RNASARS-CoV-2Therapeutic

Identifiers

PMID34274470
PMCPMC8282474
OpenAlexW3183541250

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.