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Electrochemomics Profiling Metabolic Dynamics in Biofluids.

Jianwu Wang1,2, Huarong Xia2, Chenyan Huang3

  • 1Institute for Digital Molecular Analytics and Science (IDMxS), Nanyang Technological University, 59 Nanyang Drive, Singapore 636921, Singapore.

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|February 19, 2026
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Summary
This summary is machine-generated.

We developed an electrochemomics (EC-omics) approach for disease diagnosis using biofluid analysis. This method accurately detects periodontitis from saliva, outperforming traditional biomarker analysis.

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Area of Science:

  • Electrochemistry
  • Biomolecular analysis
  • Diagnostic technology

Background:

  • Conventional electrochemical sensing relies on predefined biomarkers, facing challenges with biofluid complexity and calibration biases.
  • Significant electrochemical information at biofluid interfaces remains untapped for comprehensive metabolic profiling.

Purpose of the Study:

  • To introduce an electrochemomics (EC-omics) approach for comprehensive profiling of biomolecule dynamics in biofluids during disease onset.
  • To develop and validate a portable electrochemical platform for unbiased biofluid analysis.

Main Methods:

  • Customized a portable electrochemical profiling platform utilizing carbon nanotube/bacterial cellulose (CNT/BC) electrodes for high sensitivity and low noise.
  • Applied the EC-omics platform to analyze saliva for periodontitis diagnosis, creating an EC-omics database.
  • Integrated intelligent algorithms with the EC-omics data for disease discrimination.

Main Results:

  • The EC-omics platform accurately discriminated periodontitis in saliva samples with 93% accuracy.
  • This performance surpassed untargeted nuclear magnetic resonance (89%) and traditional periodontitis biomarkers (70%).
  • Demonstrated EC-omics feasibility in human urine and mouse serum, indicating broad applicability.

Conclusions:

  • EC-omics offers a holistic and unbiased method for analyzing biofluid electrochemical properties for disease diagnosis.
  • The developed platform shows significant potential for expanding the understanding of metabolic networks and developing novel decentralized healthcare diagnostic tools.