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Full Hematocrit-Viscosity Curve Identification Using Three-Dataset Krieger-Dougherty Regression.

Yang Jun Kang1

  • 1Department of Mechanical Engineering, Chosun University, 10, Chosundae 1-gil, Dong-gu, Gwangju 61452, Republic of Korea.

Biosensors
|April 27, 2026
PubMed
Summary
This summary is machine-generated.

A new method simplifies blood viscosity analysis by reconstructing the full hematocrit-viscosity curve using just three data points. This approach offers an efficient and reliable alternative to traditional multi-measurement techniques for blood rheology studies.

Keywords:
K-D regression modelblood separationblood viscositycoflowing streams methodfull hematocrit–viscosity curvehematocritmicro-hemocytometermicrofluidic chipthree hematocrit–viscosity datasets

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Area of Science:

  • Biomedical Engineering
  • Rheology
  • Hematology

Background:

  • Blood viscosity is crucial for understanding blood flow dynamics.
  • The relationship between hematocrit and blood viscosity is complex and vital for physiological and pathological states.
  • Current methods for determining the full hematocrit-viscosity curve are time-consuming, requiring numerous measurements.

Purpose of the Study:

  • To develop a simplified method for reconstructing the complete hematocrit-viscosity curve.
  • To validate a three-point Krieger-Dougherty (K-D) regression model for this purpose.
  • To offer an efficient alternative to conventional multi-measurement techniques.

Main Methods:

  • Preparation of blood samples: suspended, RBC-rich, and RBC-depleted blood.
  • Measurement of hematocrit using a micro-hemocytometer.
  • Measurement of blood viscosity using the coflowing streams method.
  • Application of a three-dataset K-D regression model: μ=μ0(1-ϕ/ϕm)⁻ᵅ.

Main Results:

  • The reconstructed hematocrit-viscosity curves closely matched experimental data.
  • The proposed three-point method achieved a lower root-mean-square error compared to conventional methods.
  • The K-D model's exponent was sensitive to the midpoint dataset, while μ0 was influenced by the suspending medium.

Conclusions:

  • The proposed three-dataset K-D regression method is a simple, efficient, and reliable approach for estimating the full hematocrit-viscosity curve.
  • This method reduces the number of required measurements for blood rheology analysis.
  • The findings contribute to a better understanding of blood rheological properties.