Evidence map›Paper›PMID 39278957›Full record

ArticleScientific reports2024

Salivary detection of Chikungunya virus infection using a portable and sustainable biophotonic platform coupled with artificial intelligence algorithms.

Marco Guevara-Vega, Rafael Borges Rosa, Douglas Carvalho Caixeta, Mariana Araújo Costa, Rayany Cristina de Souza, Giulia Magalhães Ferreira, Anagê Calixto Mundim Filho, Murillo Guimarães Carneiro, Ana Carolina Gomes Jardim, Robinson Sabino-Silva

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Marco Guevara-VegaInnovation Center in Salivary Diagnostic and Nanobiotechnology, Department of Physiology, Institute of Biomedical Sciences, Federal University of Uberlandia, ARFIS, Av. Pará, 1720, Campus Umuarama, Uberlândia, Minas Gerais, CEP 38400-902, Brazil.
Rafael Borges RosaRodents Animal Facilities Complex, Federal University of Uberlândia, Uberlândia, Minas Gerais, Brazil.
Douglas Carvalho CaixetaInnovation Center in Salivary Diagnostic and Nanobiotechnology, Department of Physiology, Institute of Biomedical Sciences, Federal University of Uberlandia, ARFIS, Av. Pará, 1720, Campus Umuarama, Uberlândia, Minas Gerais, CEP 38400-902, Brazil.
Mariana Araújo CostaInnovation Center in Salivary Diagnostic and Nanobiotechnology, Department of Physiology, Institute of Biomedical Sciences, Federal University of Uberlandia, ARFIS, Av. Pará, 1720, Campus Umuarama, Uberlândia, Minas Gerais, CEP 38400-902, Brazil.
Rayany Cristina de SouzaInnovation Center in Salivary Diagnostic and Nanobiotechnology, Department of Physiology, Institute of Biomedical Sciences, Federal University of Uberlandia, ARFIS, Av. Pará, 1720, Campus Umuarama, Uberlândia, Minas Gerais, CEP 38400-902, Brazil.
Giulia Magalhães FerreiraLaboratory of Antiviral Research, Institute of Biomedical Science, Federal University of Uberlandia, Uberlandia, Minas Gerais, Brazil.
Anagê Calixto Mundim FilhoFaculty of Computing, Federal University of Uberlandia, Uberlândia, Minas Gerais, Brazil.
Murillo Guimarães CarneiroFaculty of Computing, Federal University of Uberlandia, Uberlândia, Minas Gerais, Brazil.
Ana Carolina Gomes JardimLaboratory of Antiviral Research, Institute of Biomedical Science, Federal University of Uberlandia, Uberlandia, Minas Gerais, Brazil.
Robinson Sabino-SilvaInnovation Center in Salivary Diagnostic and Nanobiotechnology, Department of Physiology, Institute of Biomedical Sciences, Federal University of Uberlandia, ARFIS, Av. Pará, 1720, Campus Umuarama, Uberlândia, Minas Gerais, CEP 38400-902, Brazil. robinsonsabino@gmail.com.

Funding

CNPq productivity fellowship 306050/2021-8
6 · The paper itself

Abstract

The current detection method for Chikungunya Virus (CHIKV) involves an invasive and costly molecular biology procedure as the gold standard diagnostic method. Consequently, the search for a non-invasive, more cost-effective, reagent-free, and sustainable method for the detection of CHIKV infection is imperative for public health. The portable Fourier-transform infrared coupled with Attenuated Total Reflection (ATR-FTIR) platform was applied to discriminate systemic diseases using saliva, however, the salivary diagnostic application in viral diseases is less explored. The study aimed to identify unique vibrational modes of salivary infrared profiles to detect CHIKV infection using chemometrics and artificial intelligence algorithms. Thus, we intradermally challenged interferon-gamma gene knockout C57/BL6 mice with CHIKV (20 µl, 1 X 10

Indexed as

AlgorithmsArtificial IntelligenceChikungunya FeverChikungunya virusSalivaAnimalsMiceMice, Inbred C57BLMice, KnockoutSpectroscopy, Fourier Transform InfraredATR-FTIRChikungunya virusMachine learning algorithmsSalivary diagnosis

Identifiers

PMID39278957
PMCPMC11402986

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.