ReviewAnalytical science advances2025
Chemometric Methods Applied to Infrared and Raman Spectroscopy for Arboviruses Diagnosis: A Systematic Review With Meta-Analysis.
Review in Analytical science advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
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.
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Who cites it
2 citing papers in PubMed.
- Rapid identification of feline sporotrichosis by ATR-FTIR spectroscopy coupled with machine learning.Analytical and bioanalytical chemistry · 2026Article
- Chemometric Methods Applied to Infrared and Raman Spectroscopy for Arboviruses Diagnosis: A Systematic Review With Meta-Analysis.Analytical science advances · 2025Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Arboviruses such as dengue, Zika, chikungunya and yellow fever share similar clinical manifestations, making differential diagnosis challenging, particularly in endemic regions with viral co-circulation. Conventional laboratory methods present important limitations, including cross-reactivity and reliance on specialized infrastructure. In this scenario, spectroscopic techniques such as Fourier-transform attenuated total reflectance infrared spectroscopy (ATR-FTIR) and Raman, when combined with artificial intelligence (AI), have shown promise by enabling rapid, low-cost analyses. This systematic review (PROSPERO CRD420251006929) aimed to qualitatively and quantitatively synthesize studies that applied infrared and Raman spectroscopy to clinical samples, supported by chemometric models. All 23 included studies investigated dengue patients, with only one also assessing Zika and chikungunya. Most studies employed Raman spectroscopy and multivariate analysis methods, such as principal component analysis with linear discriminant analysis (PCA-LDA, 39.1%) and partial least squares with discriminant analysis (PLS-DA, 21.7%), with an overall sensitivity of 0.94 (95% CI: 0.91-0.96) and overall specificity of 0.97 (95% CI: 0.95-0.98) for Raman spectroscopy. The risk of bias across all studies was high, according to PROBAST-AI development and evaluation assessment. These findings highlight the potential of spectroscopic approaches combined with AI for diagnosing arboviral infections, although further robust studies are required to support broader clinical validation.
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