Evidence map›Paper›PMID 28027002›Full record

ArticleHuman vaccines & immunotherapeutics2017

Mathematical modeling based on ordinary differential equations: A promising approach to vaccinology.

Carla Rezende Barbosa Bonin, Guilherme Cortes Fernandes, Rodrigo Weber Dos Santos, Marcelo Lobosco

Open access · greenAbstract read
In one paragraph

Article in Human vaccines & immunotherapeutics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.0field-weighted citation impact, top 21% 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

4 citing papers in PubMed, 19 citations in OpenAlex.

  1. Review
  2. Article
  3. Frontiers in cellular and infection microbiology · 2019
    Review
  4. 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

4 authors at 2 institutions in 1 country.

Carla Rezende Barbosa Bonina Postgraduate Program in Computational Modeling, Federal University of Juiz de Fora , Juiz de Fora , Brazil.
Guilherme Cortes Fernandesb Medical School, Presidente Antônio Carlos University , Juiz de Fora , Brazil.
Rodrigo Weber Dos Santosa Postgraduate Program in Computational Modeling, Federal University of Juiz de Fora , Juiz de Fora , Brazil.
Marcelo Loboscoa Postgraduate Program in Computational Modeling, Federal University of Juiz de Fora , Juiz de Fora , Brazil.
Universidade Federal de Juiz de Fora · BRUniversidade Presidente Antônio Carlos · BR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

New contributions that aim to accelerate the development or to improve the efficacy and safety of vaccines arise from many different areas of research and technology. One of these areas is computational science, which traditionally participates in the initial steps, such as the pre-screening of active substances that have the potential to become a vaccine antigen. In this work, we present another promising way to use computational science in vaccinology: mathematical and computational models of important cell and protein dynamics of the immune system. A system of Ordinary Differential Equations represents different immune system populations, such as B cells and T cells, antigen presenting cells and antibodies. In this way, it is possible to simulate, in silico, the immune response to vaccines under development or under study. Distinct scenarios can be simulated by varying parameters of the mathematical model. As a proof of concept, we developed a model of the immune response to vaccination against the yellow fever. Our simulations have shown consistent results when compared with experimental data available in the literature. The model is generic enough to represent the action of other diseases or vaccines in the human immune system, such as dengue and Zika virus.

Indexed as

Adaptive ImmunityModels, TheoreticalAnimalsHumansYellow FeverYellow Fever VaccineYellow Fever Vaccinecomputational immunologycomputational modelingcomputational sciencecomputational vaccinologyimmune systemordinary differential equationsyellow fever

Identifiers

PMID28027002
PMCPMC5328209
OpenAlexW2565050484

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.