川嶋 大介

Heparin concentration c in a blood extracorporeal circulation has been real-timely predicted based on the relaxation strength Δεm at relaxation frequency fm extracted by relaxation time distribution (RTD). The simulated extracorporeal circulation was conducted to optimize the number of Δεm for the prediction of c using the porcine whole blood (WB) and low-leukocyte and −platelet blood (LLPB) under the condition of the gradual increment of c from 0 to 8 U/mL with constant flow rate and blood temperature. The experimental results show that among the three relaxation strengths Δε1, Δε2 and Δε3 (in ascending order of frequency), Δε2 at f2 = 5.2 ∼ 6.2 MHz and Δε3 at f3 = 42 ∼ 50 MHz were correlated to c. The Δε3 was decreasing with increasing c in both cases, which was influenced by the plasma macromolecular concentrations, while the Δε2 was increased with increasing c in WB case but was hardly changed in LLPB case because the Δε2 is influenced by the blood cell concentrations and the shape changes of blood cell membranes. Heparin concentration c is estimated by the linear regression formula cPRE=a1(Δε2-Δε2c=0)+a2(Δε3-Δε3c=0) (a1 = -0.991, a2 = -0.123) within the mean absolute percentage error (MAPE) of 0.291.

OBJECTIVE: This study aims to enhance the identification of cancer cell types using electrical impedance spectroscopy (EIS) by introducing a novel analysis method, ECM-PCA, which integrates an equivalent circuit model with principal component analysis. METHODS: The ECM-PCA method addresses the limitations of conventional PCA and kernel PCA (kPCA) in handling non-linear and frequency-dependent data. Impedance data of four cancer cell types (DLD-1, T.Tn, U138, and U87) were acquired across a frequency range of 0.1 MHz to 300 MHz. The ECM-PCA method was applied to analyze the frequency-dependent impedance behaviour and compare its clustering performance with PCA and kPCA. RESULTS: ECM-PCA demonstrated clustering performance comparable to kPCA while capturing the frequency-dependent features of impedance spectra, which kPCA lacks. The phase angle component as the ECM-PCA input achieved the highest Calinski-Harabasz (CH) score of 935, and the method achieved an identification accuracy of 93.6% in the PC1 and PC2 plane. CONCLUSION: ECM-PCA improves the accuracy and interpretability of cancer cell type identification based on electrical impedance data. SIGNIFICANCE: This study highlights the potential of ECM-PCA in advancing cancer diagnostics through enhanced analysis of impedance spectra.

Albumin andγ-globulin concentrations in subcutaneous adipose tissues (SAT) have been quantified by multivariate regression based on admittance relaxation time distribution (mrARTD) under the fluctuated background of sodium electrolyte concentration. ThemrARTD formulatesP=Ac+Ξ(P: peak matrix of distribution function magnitudeγˆand relaxation timesτˆ,c: concentration matrix of albumincAlb,γ-globulinGloc, and sodium electrolyteNac,A: coefficient matrix of a multivariate regression model, andΞ: error matrix). ThemrARTD is implemented by two processes which are: (1) the training process ofAthrough the maximum likelihood estimation ofPand (2) the quantification process ofcAlb,Gloc, andNacthrough the model prediction. In the training process, a positive correlation is present betweencAlb,Gloc, andNactoγˆ1atτˆ1= 0.1 as well asγˆ2atτˆ2= 1.40 μs as under a fixed concentration of proteins solution into a porcine SAT (cAlb= 0.800-2.400 g/dL,Gloc= 0.400-1.200 g dl-1andNac= 0.700-0.750 g dl-1). ThemrARTD method quantifiescAlb,Gloc, andNacin SAT with an absolute error of 33.79%, 44.60%, and 2.18%, respectively.