Evidence map›Paper›PMID 36301483›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2023

An Automated Strategy to Handle Antigenic Variability in Immunisation Protocols, Part I: Nanopore Sequencing of Infectious Agent Variants.

Glaucia C Pereira

Abstract read
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In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2023. 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
–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

1 citing paper in PubMed.

  1. 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

1 author.

Glaucia C PereiraBioengineering Department, Imperial College London, London, UK. glaucia.daconceicaopereira12@alumni.imperial.ac.uk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Infectious agents often challenge therapeutics, from antibiotics resistance to antigenic variability affecting inoculation measures. Over the last decades, genome sequencing arose as an important ally to address such challenges. In bacterial infection, whole-genome-sequencing (WGS) supports tracking pathogenic alterations affecting the human microbiome. In viral infection, the analysis of the relevant sequence of nucleotides helps with determining historical variants of a virus and elucidates details about infection clusters and their distribution. Additionally, genome sequencing is now an important step in inoculation protocols, isolating target genes to design more robust immunisation assays. Ultimately, genetic engineering has empowered repurposing at scale, allowing long-lasting repeating clinical trials to be automated within a much shorter time-frame, by adjusting existing protocols. This is particularly important during sanitary emergencies as the ones caused by the 2014 West African Ebola outbreak, the Zika virus rapid spread in both South and North America in 2015, followed by Asia in 2016, and the pandemic caused by the SARS-CoV-2, which has infected more than 187 million people and caused more than 4 million deaths, worldwide, as per July 2021 statistics. In this scenery, this chapter presents a novel fully automated strategy to handle antigenic variability in immunisation protocols. The methodology comprises of two major steps (1) nanopore sequencing of infectious agent variants - the focus is on the SARS-CoV-2 and its variants; followed by (2) mRNA vector design for immunotherapy. This chapter presents the nanopore sequencing step and Chapter 17 introduces a protocol for mRNA vector design.

Indexed as

COVID-19Nanopore SequencingZika VirusZika Virus InfectionAntigenic VariationHumansImmunizationRNA, MessengerSARS-CoV-2RNA, MessengerACE2Antigenic variabilityCoronavirusesImmunisation metrics for varied infectious agentsNanopore high-throughput sequencingS1/S2 insertsSARS-CoV-2Spike proteinTMPRSS2

Identifiers

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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.