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Nature and Consequences of Biological Reductionism for the Immunological Study of Infectious Diseases

Overview of attention for article published in Frontiers in immunology, May 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (73rd percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

Mentioned by

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13 X users

Citations

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21 Dimensions

Readers on

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39 Mendeley
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Title
Nature and Consequences of Biological Reductionism for the Immunological Study of Infectious Diseases
Published in
Frontiers in immunology, May 2017
DOI 10.3389/fimmu.2017.00612
Pubmed ID
Authors

Ariel L. Rivas, Gabriel Leitner, Mark D. Jankowski, Almira L. Hoogesteijn, Michelle J. Iandiorio, Stylianos Chatzipanagiotou, Anastasios Ioannidis, Shlomo E. Blum, Renata Piccinini, Athos Antoniades, Jane C. Fazio, Yiorgos Apidianakis, Jeanne M. Fair, Marc H. V. Van Regenmortel

Abstract

Evolution has conserved "economic" systems that perform many functions, faster or better, with less. For example, three to five leukocyte types protect from thousands of pathogens. To achieve so much with so little, biological systems combine their limited elements, creating complex structures. Yet, the prevalent research paradigm is reductionist. Focusing on infectious diseases, reductionist and non-reductionist views are here described. The literature indicates that reductionism is associated with information loss and errors, while non-reductionist operations can extract more information from the same data. When designed to capture one-to-many/many-to-one interactions-including the use of arrows that connect pairs of consecutive observations-non-reductionist (spatial-temporal) constructs eliminate data variability from all dimensions, except along one line, while arrows describe the directionality of temporal changes that occur along the line. To validate the patterns detected by non-reductionist operations, reductionist procedures are needed. Integrated (non-reductionist and reductionist) methods can (i) distinguish data subsets that differ immunologically and statistically; (ii) differentiate false-negative from -positive errors; (iii) discriminate disease stages; (iv) capture in vivo, multilevel interactions that consider the patient, the microbe, and antibiotic-mediated responses; and (v) assess dynamics. Integrated methods provide repeatable and biologically interpretable information.

X Demographics

X Demographics

The data shown below were collected from the profiles of 13 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 39 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 21%
Researcher 7 18%
Other 6 15%
Student > Master 5 13%
Professor > Associate Professor 4 10%
Other 3 8%
Unknown 6 15%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 6 15%
Veterinary Science and Veterinary Medicine 5 13%
Medicine and Dentistry 5 13%
Agricultural and Biological Sciences 5 13%
Nursing and Health Professions 2 5%
Other 8 21%
Unknown 8 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 27 March 2022.
All research outputs
#5,402,428
of 25,604,262 outputs
Outputs from Frontiers in immunology
#6,017
of 32,042 outputs
Outputs of similar age
#86,948
of 330,843 outputs
Outputs of similar age from Frontiers in immunology
#94
of 385 outputs
Altmetric has tracked 25,604,262 research outputs across all sources so far. Compared to these this one has done well and is in the 78th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 32,042 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.4. This one has done well, scoring higher than 80% 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 330,843 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 73% of its contemporaries.
We're also able to compare this research output to 385 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.