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Automated Baseline-Correction and Signal-Detection Algorithms with Web-Based Implementation for Thermal Liquid Biopsy

Karl C Reger1, Gabriela Schneider2,3, Keegan T Line1

  • 1Department of Mathematics and Statistics, Northern Arizona University, Flagstaff, AZ 86011, USA.

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Summary
This summary is machine-generated.

Automated algorithms for thermal liquid biopsy (TLB) streamline data processing. These tools improve disease detection by automating baseline correction and signal detection in blood plasma and other biofluids.

Keywords:
TLB profilebaseline-correction algorithmblood plasmadifferential scanning calorimetry (DSC)signal-detection algorithmthermal liquid biopsy (TLB)urineweb application

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Area of Science:

  • Biophysics
  • Analytical Chemistry
  • Biotechnology

Background:

  • Differential scanning calorimetry (DSC) analysis of blood plasma, termed thermal liquid biopsy (TLB), shows promise for disease detection.
  • Clinical adoption of TLB is limited by laborious data processing, especially baseline correction.

Purpose of the Study:

  • To develop and validate automated algorithms for critical bottlenecks in TLB data processing.
  • To create an open-source R Shiny web application, ThermogramForge, for an end-to-end TLB workflow.

Main Methods:

  • Developed a rolling-variance based algorithm for automated baseline correction in TLB.
  • Implemented an auto-regressive integrated moving average (ARIMA)-based stationarity testing algorithm for signal detection.
  • Integrated both algorithms into the open-source ThermogramForge R Shiny web application.

Main Results:

  • Automated baseline correction achieved quality comparable to manual processing for plasma TLB.
  • A signal-detection algorithm was developed to screen for interpretable thermal features, improving robustness for low-signal biofluids like urine.
  • The signal-detection algorithm demonstrated high classification accuracy for informative TLB profiles and a low false-positive rate for noise.

Conclusions:

  • Automated baseline-correction and signal-detection algorithms significantly reduce TLB analysis time.
  • The ThermogramForge web application facilitates efficient and reproducible TLB research.
  • These advancements support wider clinical adoption of TLB for disease detection and monitoring.