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Mathematical modeling of postcoinfection with influenza A virus and Streptococcus pneumoniae, with implications for pneumonia and COPD-risk assessment

Overview of attention for article published in International Journal of Chronic Obstructive Pulmonary Disease, July 2017
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1 tweeter

Citations

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28 Mendeley
Title
Mathematical modeling of postcoinfection with influenza A virus and <em>Streptococcus pneumoniae</em>, with implications for pneumonia and COPD-risk assessment
Published in
International Journal of Chronic Obstructive Pulmonary Disease, July 2017
DOI 10.2147/copd.s138295
Pubmed ID
Authors

Yi-Hsien Cheng, Shu-Han You, Yi-Jun Lin, Szu-Chieh Chen, Wei-Yu Chen, Wei-Chun Chou, Nan-Hung Hsieh, Chung-Min Liao

Abstract

The interaction between influenza and pneumococcus is important for understanding how coinfection may exacerbate pneumonia. Secondary pneumococcal pneumonia associated with influenza infection is more likely to increase respiratory morbidity and mortality. This study aimed to assess exacerbated inflammatory effects posed by secondary pneumococcal pneumonia, given prior influenza infection. A well-derived mathematical within-host dynamic model of coinfection with influenza A virus and Streptococcus pneumoniae (SP) integrated with dose-response relationships composed of previously published mouse experimental data and clinical studies was implemented to study potentially exacerbated inflammatory responses in pneumonia based on a probabilistic approach. We found that TNFα is likely to be the most sensitive biomarker reflecting inflammatory response during coinfection among three explored cytokines. We showed that the worst inflammatory effects would occur at day 7 SP coinfection, with risk probability of 50% (likely) to develop severe inflammatory responses. Our model also showed that the day of secondary SP infection had much more impact on the severity of inflammatory responses in pneumonia compared to the effects caused by initial virus titers and bacteria loads. People and health care workers should be wary of secondary SP infection on day 7 post-influenza infection for prompt and proper control-measure implementation. Our quantitative risk-assessment framework can provide new insights into improvements in respiratory health especially, predominantly due to chronic obstructive pulmonary disease (COPD).

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 28 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 25%
Student > Master 4 14%
Student > Ph. D. Student 3 11%
Student > Doctoral Student 2 7%
Student > Postgraduate 2 7%
Other 3 11%
Unknown 7 25%
Readers by discipline Count As %
Immunology and Microbiology 5 18%
Veterinary Science and Veterinary Medicine 3 11%
Medicine and Dentistry 2 7%
Mathematics 2 7%
Environmental Science 2 7%
Other 6 21%
Unknown 8 29%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 July 2017.
All research outputs
#9,156,533
of 11,438,239 outputs
Outputs from International Journal of Chronic Obstructive Pulmonary Disease
#1,049
of 1,328 outputs
Outputs of similar age
#189,139
of 259,672 outputs
Outputs of similar age from International Journal of Chronic Obstructive Pulmonary Disease
#53
of 61 outputs
Altmetric has tracked 11,438,239 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,328 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 7th percentile – i.e., 7% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 259,672 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 61 others from the same source and published within six weeks on either side of this one. This one is in the 8th percentile – i.e., 8% of its contemporaries scored the same or lower than it.