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Related Experiment Video

Updated: Jul 3, 2026

Flow Virometry to Analyze Antigenic Spectra of Virions and Extracellular Vesicles
06:28

Flow Virometry to Analyze Antigenic Spectra of Virions and Extracellular Vesicles

Published on: January 25, 2017

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Combining flow virometry with tree-based machine learning models for rapid virus particle estimation in different

Yevhen Myshkevych1, Ibrahima N'Doye2, Julie Sanchez Medina3

  • 1Environmental Science and Engineering Program, Division of Biological and Environmental Science and Engineering, King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia; KAUST Center of Excellence on Smart Health, King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia.

Water Research
|June 7, 2025
PubMed
Summary

Flow virometry (FVM) and machine learning (ML) accurately estimate virus particle (VP) concentrations in wastewater. This combined approach enhances public health monitoring by optimizing VP enumeration in various water matrices.

Keywords:
Flow virometryMachine learningVirus enumerationWater reuse

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Area of Science:

  • Environmental microbiology
  • Analytical chemistry
  • Computational biology

Background:

  • Accurate enumeration of virus particles (VPs) is crucial for wastewater treatment efficiency and public health protection.
  • Existing methods for VP detection can be labor-intensive and may not be suitable for diverse wastewater matrices.

Purpose of the Study:

  • Optimize flow virometry (FVM) for wastewater analysis.
  • Correlate FVM data with specific virus genera concentrations.
  • Develop machine learning (ML) models to predict total VP concentration in wastewater.

Main Methods:

  • Tested various sample preprocessing techniques for FVM, including staining dyes, surfactants, and fixation.
  • Performed Spearman's rank correlation between FVM and qPCR data for five virus genera.
  • Developed and compared extreme gradient-boosting (XGB) and random forest (RF) ML models using physiochemical water parameters to predict VP concentration.

Main Results:

  • Optimized FVM protocol demonstrated enhanced detection sensitivity in wastewater.
  • FVM data showed positive correlations with concentrations of five target virus genera (Spearman's rho: 0.21–0.44, p < 0.01).
  • The XGB model significantly outperformed the RF model, achieving a 23% lower root mean square error in estimating total VP concentration across different wastewater treatment stages.

Conclusions:

  • Flow virometry (FVM), when optimized and combined with machine learning (ML), provides a robust method for virus particle enumeration in wastewater.
  • ML models, particularly XGB, can accurately estimate total VP concentrations using readily available water quality parameters.
  • This integrated approach enhances wastewater monitoring capabilities, contributing to improved public health risk assessment.