Discrimination of patients with different serological evolution of HIV and co-infection with HCV using metabolic fingerprinting based on Fourier transform infrared

  1. Pizarro, C. 1
  2. Esteban-Di´ez, I. 1
  3. Arenzana-Rámila, I. 1
  4. González-Sáiz, J.M. 1
  1. 1 Universidad de La Rioja
    info

    Universidad de La Rioja

    Logroño, España

    GRID grid.119021.a

Journal:
Journal of Biophotonics

ISSN: 1864-063X

Year of publication: 2018

Volume: 3

Type: Article

Export: RIS
DOI: 10.1002/jbio.201700035 SCOPUS: 2-s2.0-85033234698 WoS: 000426731000005 GOOGLE SCHOLAR

Metrics

Cited by

  • Scopus Cited by: 10 (12-11-2021)

JCR (Journal Impact Factor)

  • Year 2018
  • Journal Impact Factor: 3.763
  • Best Quartile: Q1
  • Area: BIOPHYSICS Quartile: Q1 Rank in area: 17/73 (Ranking edition: SCIE)
  • Area: BIOCHEMICAL RESEARCH METHODS Quartile: Q1 Rank in area: 16/79 (Ranking edition: SCIE)
  • Area: OPTICS Quartile: Q1 Rank in area: 17/95 (Ranking edition: SCIE)

SCImago Journal Rank

  • Year 2018
  • SJR Journal Impact: 1.039
  • Best Quartile: Q1
  • Area: Biochemistry, Genetics and Molecular Biology (miscellaneous) Quartile: Q1 Rank in area: 57/263
  • Area: Chemistry (miscellaneous) Quartile: Q1 Rank in area: 67/468
  • Area: Engineering (miscellaneous) Quartile: Q1 Rank in area: 42/789
  • Area: Materials Science (miscellaneous) Quartile: Q1 Rank in area: 101/652
  • Area: Physics and Astronomy (miscellaneous) Quartile: Q1 Rank in area: 43/288

CiteScore

  • Year 2018
  • CiteScore of the Journal : 5.0
  • Area: Engineering (all) Percentile: 92
  • Area: Physics and Astronomy (all) Percentile: 85
  • Area: Chemistry (all) Percentile: 79
  • Area: Materials Science (all) Percentile: 78
  • Area: Biochemistry, Genetics and Molecular Biology (all) Percentile: 77

Journal Citation Indicator (JCI)

  • Year 2018
  • Journal Citation Indicator (JCI): 1.16
  • Best Quartile: Q1
  • Area: OPTICS Quartile: Q1 Rank in area: 18/108
  • Area: BIOCHEMICAL RESEARCH METHODS Quartile: Q1 Rank in area: 17/82
  • Area: BIOPHYSICS Quartile: Q1 Rank in area: 9/73

Abstract

Human immunodeficiency virus (HIV) is a retrovirus that weakens the immune system and permits opportunistic diseases such as hepatitis C (HCV) to enter the body. These diseases induce metabolic disorders in the patients and it is therefore logical to approach them from a holistic, functional perspective, studying the metabolome comprehensively to identify metabolic signatures associated with certain disease states. The metabolomics strategy here proposed involves metabolic fingerprinting using Fourier transform infrared spectroscopy and chemometric tools on 72 plasma samples (subdivided into 63 training and 9 test samples) to differentiate between healthy subjects and patients with different disease stages. Several options, relating to the variable selection method used in linear discriminant analysis and the number of categories being considered, were explored to optimize discrimination ability. A total of 18 bands enabled differentiation between control subjects, HIV patients and the group that encompassed patients with acquired immune deficiency syndrome (AIDS), AIDS/HCV and HIV/HCV, providing overall classification and internal prediction rates of 97.67% and 93.65%, respectively. Only 9 bands were required to further discriminate between AIDS, AIDS/HCV and HIV/HCV, with 99.20% (training) and 89.66% (cross-validation) correct classifications. The simplicity and effectiveness of the classification methodology proposed was reinforced by the satisfactory results obtained in external prediction. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.