Preventing Data Ambiguity in Infectious Diseases with Four-Dimensional and Personalized Evaluations

Autor: Almira L. Hoogesteijn, Christina Sereti, Jeanne M. Fair, George P. Tegos, Eleftheria Trikka-Graphakos, Nikoletta Charalampaki, Anastasios Ioannidis, Stylianos Chatzipanagiotou, Ariel L. Rivas, Michelle Iandiorio
Rok vydání: 2016
Předmět:
0301 basic medicine
Physiology
lcsh:Medicine
Pilot Projects
Bioinformatics
Monocytes
White Blood Cells
Animal Cells
Antibiotics
Medicine and Health Sciences
Leukocytes
Lymphocytes
lcsh:Science
Temporal scales
Immune Response
Statistical Data
media_common
Multidisciplinary
Antimicrobials
Drugs
Hematology
Ambiguity
Body Fluids
3. Good health
Temporal database
Infectious Diseases
Blood
Physical Sciences
Cellular Types
Anatomy
Statistics (Mathematics)
Research Article
Immune Cells
media_common.quotation_subject
Immunology
Biology
Microbiology
Communicable Diseases
03 medical and health sciences
Dogs
Spatio-Temporal Analysis
Text mining
Microbial Control
Animals
Humans
Directionality
Diagnostic Errors
Pharmacology
Blood Cells
business.industry
lcsh:R
Biology and Life Sciences
Pattern recognition
Cell Biology
Single line
030104 developmental biology
Infectious disease (medical specialty)
lcsh:Q
Artificial intelligence
business
Mathematics
Medical Informatics
Zdroj: PLoS ONE
PLoS ONE, Vol 11, Iss 7, p e0159001 (2016)
ISSN: 1932-6203
Popis: Background Diagnostic errors can occur, in infectious diseases, when anti-microbial immune responses involve several temporal scales. When responses span from nanosecond to week and larger temporal scales, any pre-selected temporal scale is likely to miss some (faster or slower) responses. Hoping to prevent diagnostic errors, a pilot study was conducted to evaluate a four-dimensional (4D) method that captures the complexity and dynamics of infectious diseases. Methods Leukocyte-microbial-temporal data were explored in canine and human (bacterial and/or viral) infections, with: (i) a non-structured approach, which measures leukocytes or microbes in isolation; and (ii) a structured method that assesses numerous combinations of interacting variables. Four alternatives of the structured method were tested: (i) a noise-reduction oriented version, which generates a single (one data point-wide) line of observations; (ii) a version that measures complex, three-dimensional (3D) data interactions; (iii) a non-numerical version that displays temporal data directionality (arrows that connect pairs of consecutive observations); and (iv) a full 4D (single line-, complexity-, directionality-based) version. Results In all studies, the non-structured approach revealed non-interpretable (ambiguous) data: observations numerically similar expressed different biological conditions, such as recovery and lack of recovery from infections. Ambiguity was also found when the data were structured as single lines. In contrast, two or more data subsets were distinguished and ambiguity was avoided when the data were structured as complex, 3D, single lines and, in addition, temporal data directionality was determined. The 4D method detected, even within one day, changes in immune profiles that occurred after antibiotics were prescribed. Conclusions Infectious disease data may be ambiguous. Four-dimensional methods may prevent ambiguity, providing earlier, in vivo, dynamic, complex, and personalized information that facilitates both diagnostics and selection or evaluation of anti-microbial therapies.
Databáze: OpenAIRE
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