Evidence map›Paper›PMID 28950872›Full record

ArticleBMC systems biology2017

Forecasting influenza A pandemic outbreak using protein dynamical network biomarkers.

Jie Gao, Kang Wang, Tao Ding, Shanshan Zhu

Abstract read
In one paragraph

Article in BMC systems biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
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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.

Jie GaoSchool of Science, Jiangnan University, Wuxi, 214122, China. gaojie@jiangnan.edu.cn.
Kang WangSchool of Science, Jiangnan University, Wuxi, 214122, China.
Tao DingSchool of Science, Jiangnan University, Wuxi, 214122, China.
Shanshan ZhuSchool of Science, Jiangnan University, Wuxi, 214122, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundInfluenza A virus is prone to mutation and susceptible to human beings and spread in the crowds when affected by the external environment or other factors. It is very necessary to forecast influenza A pandemic outbreak.

methodsThis paper studies the different states of influenza A in the method of dynamical network biomarkers. Through establishing protein dynamical network biomarkers of influenza A virus protein, a composite index is ultimately obtained to forecast influenza A pandemic outbreak.

resultsThe composite index varies along with the state of pandemic influenza virus from a relatively steady state to critical state before outbreak and then to the outbreak state. When the composite index continuous decreases for 2 years and increases of more than o.1 suddenly, it means the next year is normally in the outbreak state. Therefore, we can predict and identify whether a certain year is in the critical state before influenza A outbreak or outbreak state by observing the variation of index value. Meanwhile, through data analysis for different countries influenza A pandemic outbreak in different countries can also be forecasted.

conclusionsThis indicates the composite index can provide significant warning information to detect the stage of influenza A, which will be significantly meaningful for the warning and prevention of influenza A pandemic.

Indexed as

Computational BiologyDisease OutbreaksPandemicsBiomarkersHumansInfluenza, HumanViral ProteinsBiomarkersViral ProteinsInfluenza A pandemicProtein dynamical network biomarker (PDNB)The critical stateThe outbreak state

Identifiers

PMID28950872
PMCPMC5615242

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LicenceCC BY
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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.