Diagnosis of hepatocellular carcinoma based on a terahertz signal and VMD-CWSE

Study on the detection method of biological characteristics of hepatoma cells based on terahertz time-domain spectroscopy

Liver cancer usually has a high degree of malignancy and its early symptoms are hidden, therefore, it is of significant research value to develop early-stage detection methods of liver cancer for pathological screening. In this paper, a biometric detection method for living human hepatocytes based on terahertz time-domain spectroscopy was proposed. The difference in terahertz response between normal and cancer cells was analyzed, including five characteristic parameters in the response, namely refractive index, absorption coefficient, dielectric constant, dielectric loss and dielectric loss tangent. Based on class separability and variable correlation, absorption coefficient and dielectric loss were selected to better characterize cellular properties. Maximum information coefficient and principal component analysis were employed for feature extraction, and a cell classification model of support vector machine was constructed. The results showed that the algorithm based on parameter feature fusion can achieve an accuracy of 91.6% for human hepatoma cell lines and one normal cell line. This work provides a promising solution for the qualitative evaluation of living cells in liquid environment.

Specific detection of n-propanol gas via terahertz metasurface sensor modified by molecularly imprinted polymer

As an organic substance, n-propanol gas has been paid attention to in environmental monitoring and exhalation of lung cancer patient. In this paper a rapid detection method for n-propanol gas is developed based on molecularly imprinted polymers (MIP) and terahertz (THz) metasurface sensors. We first prepared a MIP suitable for detecting the n-propanol gas. And then the n-propanol MIP was modified to the THz metasurface sensor for detecting the n-propanol gas. Since the MIP adsorbed with n-propanol changes the dielectric environment of the sensor, the resonance frequency of the sensor also change. So we based on the n-propanol concentration was analyzed according to the change in resonance frequency. The experimental results showed that the sensor can effectively detect the n-propanol concentration in the range of 50-500 ppm (parts per million). In addition, we also verified the specificity and repeatability of the sensor. This work provides a new idea and method for the sensitive and specific detection of n-propanol gas.

A new technology for rapid determination of isomers of hydroxybenzoic acid by terahertz spectroscopy

This study investigated the feasibility of using terahertz (THz) technology for the rapid identification of isomers. The time-domain spectra of 2-hydroxybenzoic acid (2-HA), 3-hydroxybenzoic acid (3-HA), and 4-hydroxybenzoic acid (4-HA) were measured by a THz time-domain spectroscopy system (THz-TDS) in the range of 0.3-1.8 THz. Aiming at the isomer classification problem, a THz spectral data classification model based on a variational mode decomposition-particle swarm optimization-support vector machine (VMD-PSO-SVM) method was proposed. Empirical mode decomposition (EMD) and variational mode decomposition (VMD) were used to extract the first eight intrinsic mode functions (IMFs) of the time-domain signal. Principal component analysis (PCA) was used to extract the first 80 principal components of each modal component as the classification feature vector. The particle swarm optimization (PSO) and support vector machine (SVM) algorithms were used to construct 2-, 3-, and 4-HA classification models. We found that the prediction accuracy of the VMD-PSO-SVM model was significantly higher than that of EMD-PSO-SVM model regardless of the modal components. For both EMD and VMD, with the increase in the IMF number, the corresponding classification recognition accuracy tended to decrease. The results showed that the rapid identification model of hydroxybenzoic acid isomers based on THz spectroscopy and SVM was effective and feasible, providing an accurate and rapid method for the chemical synthesis and quality monitoring of biomedicine.

A novel strategy regarding geometric product for liquids discrimination based on THz reflection spectroscopy

A novel expression of geometric product that associated with the geometric relationship from geometric algebra constructed by the vectorized refractive index and absorption coefficient in THz region is proposed, which could provide a new insight into the THz properties of materials. From the novel expression, the candidate characteristic parameters are extracted for liquids discrimination and present the abundant second order correlation information of optical parameters with the consideration of dimension rising. Three groups of liquids, containing C-reactive protein calibrators and alpha fetoprotein calibrators, were selected as examples to validate the feasibility of the proposed strategy. Comparing with the traditional THz parameters, including refractive index, absorption coefficient, and complex permittivity, the novel approach exhibits notable superiority for differentiation with the evaluation of statistical differences and effect sizes. The proposed geometric product expression could have a large potential on promoting the substance identification in some applications of THz technology.

Detection of cancer biomarkers CA125 and CA199 via terahertz metasurface immunosensor

The cancer biomarkers including AFP, CEA, CA199 and CA125, are of great importance in the diagnosis, prognostic prediction and recurrence monitoring of malignancies. However, in clinical practical applications, most tumor cancer biomarkers are lack of sensitivity and specificity. In this study, we propose a terahertz (THz) metasurface (MS) immunosensor coupled with gold nanoparticles (AuNPs), which have good biocompatibility and high specific surface area for biomarkers. Firstly, we added AuNPs to the surface of the sensor. And then, the surface is modified with Anti-CA125 or Anti-CA199 to improve the sensitivity and specificity to the target antigen. The biosensor was fabricated using a surface micromachining process and characterized by a THz-time-domain spectroscopy (TDS) system. The sensitivity of the resonance frequency of the sensor to the refractive index was 65 GHz/RIU (refractive index unit). The detection performance of the THz immunosensor was also verified with different concentrations of CA125 and CA199. The experimental results showed that the frequency shift of the resonance peak was linearly related to the concentration of CA125 and CA199. The detection limits for both CA125 and CA199 are 0.01 U/ml, which is better than that of other common methods. Finally, serum samples were collected and detected to explore whether this method is suitable for clinical detection. The results are consistent with the results of antigen recognition. This study proves that the practicability of the THz immunosensor, which potentially provides important techniques and equipment for improving the sensitivity and specificity of cancer biomarkers.

Malignant and benign thyroid nodule differentiation through the analysis of blood plasma with terahertz spectroscopy

The liquid and lyophilized blood plasma of patients with benign or malignant thyroid nodules and healthy individuals were studied by terahertz (THz) time-domain spectroscopy and machine learning. The blood plasma samples from malignant nodule patients were shown to have higher absorption. The glucose concentration and miRNA-146b level were correlated with the sample's absorption at 1 THz. A two-stage ensemble algorithm was proposed for the THz spectra analysis. The first stage was based on the Support Vector Machine with a linear kernel to separate healthy and thyroid nodule participants. The second stage included additional data preprocessing by Ornstein-Uhlenbeck kernel Principal Component Analysis to separate benign and malignant thyroid nodule participants. Thus, the distinction of malignant and benign thyroid nodule patients through their lyophilized blood plasma analysis by terahertz time-domain spectroscopy and machine learning was demonstrated.