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Influence of microbiome species in hard-to-heal wounds on disease severity and treatment duration

Overview of attention for article published in Brazilian Journal of Infectious Diseases, November 2015
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (57th percentile)
  • Good Attention Score compared to outputs of the same age and source (77th percentile)

Mentioned by

twitter
7 tweeters

Citations

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9 Dimensions

Readers on

mendeley
40 Mendeley
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Title
Influence of microbiome species in hard-to-heal wounds on disease severity and treatment duration
Published in
Brazilian Journal of Infectious Diseases, November 2015
DOI 10.1016/j.bjid.2015.08.013
Pubmed ID
Authors

Dagmar Chudobova, Kristyna Cihalova, Roman Guran, Simona Dostalova, Kristyna Smerkova, Radek Vesely, Jaromir Gumulec, Michal Masarik, Zbynek Heger, Vojtech Adam, Rene Kizek

Abstract

Infections, mostly those associated with colonization of wound by different pathogenic microorganisms, are one of the most serious health complications during a medical treatment. Therefore, this study is focused on the isolation, characterization, and identification of microorganisms prevalent in superficial wounds of patients (n=50) presenting with bacterial infection. After successful cultivation, bacteria were processed and analyzed. Initially the identification of the strains was performed through matrix assisted laser desorption/ionization time-of-flight mass spectrometry based on comparison of protein profiles (2-30kDa) with database. Subsequently, bacterial strains from infected wounds were identified by both matrix assisted laser desorption/ionization time-of-flight mass spectrometry and sequencing of 16S rRNA gene 108. The most prevalent species was Staphylococcus aureus (70%), and out of those 11% turned out to be methicillin-resistant (mecA positive). Identified strains were compared with patients' diagnoses using the method of artificial neuronal network to assess the association between severity of infection and wound microbiome species composition. Artificial neuronal network was subsequently used to predict patients' prognosis (n=9) with 85% success. In all of 50 patients tested bacterial infections were identified. Based on the proposed artificial neuronal network we were able to predict the severity of the infection and length of the treatment.

Twitter Demographics

The data shown below were collected from the profiles of 7 tweeters 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 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 40 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 18%
Student > Postgraduate 5 13%
Student > Bachelor 5 13%
Student > Master 4 10%
Student > Ph. D. Student 3 8%
Other 9 23%
Unknown 7 18%
Readers by discipline Count As %
Medicine and Dentistry 12 30%
Agricultural and Biological Sciences 7 18%
Nursing and Health Professions 2 5%
Pharmacology, Toxicology and Pharmaceutical Science 2 5%
Psychology 2 5%
Other 7 18%
Unknown 8 20%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 19 August 2021.
All research outputs
#10,376,721
of 18,822,351 outputs
Outputs from Brazilian Journal of Infectious Diseases
#202
of 565 outputs
Outputs of similar age
#122,113
of 295,884 outputs
Outputs of similar age from Brazilian Journal of Infectious Diseases
#3
of 9 outputs
Altmetric has tracked 18,822,351 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 565 research outputs from this source. They receive a mean Attention Score of 3.4. This one has gotten more attention than average, scoring higher than 63% of its peers.
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 295,884 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 57% of its contemporaries.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than 6 of them.