Inspecting human colon adenocarcinoma cell lines by using terahertz time-domain reflection spectroscopy

Ensemble learning prediction framework for EGFR amplification status of glioma based on terahertz spectral features

Epidermal growth factor receptor (EGFR) plays a pivotal role in the initiation and progression of gliomas. In particular, in glioblastoma, EGFR amplification emerges as a catalyst for invasion, proliferation, and resistance to radiotherapy and chemotherapy. Current approaches are not capable of providing rapid diagnostic results of molecular pathology. In this study, we propose a terahertz spectroscopic approach for predicting the EGFR amplification status of gliomas for the first time. A machine learning model was constructed using the terahertz response of the measured glioma tissues, including the absorption coefficient, refractive index, and dielectric loss tangent. The novelty of our model is the integration of three classical base classifiers, i.e., support vector machine, random forest, and extreme gradient boosting. The ensemble learning method combines the advantages of various base classifiers, this model has more generalization ability. The effectiveness of the proposed method was validated by applying an individual test set. The optimal performance of the integrated algorithm was verified with an area under the curve (AUC) maximum of 85.8 %. This signifies a significant stride toward more effective and rapid diagnostic tools for guiding postoperative therapy in gliomas.

Mechanisms of drug resistance in nutrient-depleted colorectal cancer cells: insights into lysosomal and mitochondrial drug sequestration

This Review delves into the mechanisms behind drug resistance in colorectal cancer (CRC), particularly examining the role of nutrient depletion and its contribution to multidrug resistance (MDR). The study highlights metabolic adaptations of cancer cells as well as metabolic adaptations of cancer cells under low nutrient availability, including shifts in glycolysis and lipid metabolism. It emphasizes the significance of MDR1 and its encoded efflux transporter, P-glycoprotein (P-gp/B1), in mediating drug resistance and how pathways such as HIF1α, AKT, and mTOR influence the expression of P-gp/B1 under limited nutrient availability. Additionally, the Review explores the dual roles of autophagy in drug sensitivity and resistance under nutrient limited conditions. It further investigates the involvement of lysosomes and mitochondria, focusing on their roles in drug sequestration and the challenges posed by lysosomal entrapment facilitated by non-enzymatic processes and ABC transporters like P-gp/B1. Finally, the Review underscores the importance of understanding the interplay between drug sequestration, lysosomal functions, nutrient depletion, and MDR1 gene modulation. It suggests innovative strategies, including structural modifications and nanotechnology, as promising approaches to overcoming drug resistance in cancer therapy.

Statistical Analysis of Gastric Cancer Cells Response to Broadband Terahertz Radiation with and without Contrast Nanoparticles

The paper describes the statistical analysis of the response of gastric cancer cells and normal cells to broadband terahertz radiation up to 4 THz, both with and without the use of nanostructured contrast agents. The THz spectroscopy analysis was comparatively performed under the ATR procedure and transmission measurement procedure. The statistical analysis was conducted towards multiple pairwise comparisons, including a support medium (without cells) versus a support medium with nanoparticles, normal cells versus normal cells with nanoparticles, and, respectively, tumor cells versus tumor cells with nanoparticles. When generally comparing the ATR procedure and transmission measurement procedure for a broader frequency domain, the differentiation between normal and tumor cells in the presence of contrast agents is superior when using the ATR procedure. THz contrast enhancement by using contrast agents derived from MRI-related contrast agents leads to only limited benefits and only for narrow THz frequency ranges, a disadvantage for THz medical imaging.

A Novel Optical Fiber Terahertz Biosensor Based on Anti-Resonance for The Rapid and Nondestructive Detection of Tumor Cells

The sensitive and accurate detection of tumor cells is essential for successful cancer therapy and improving cancer survival rates. However, current tumor cell detection technologies have some limitations for clinical applications due to their complexity, low specificity, and high cost. Herein, we describe the design of a terahertz anti-resonance hollow core fiber (THz AR-HCF) biosensor that can be used for tumor cell detection. Through simulation and experimental comparisons, the low-loss property of the THz AR-HCF was verified, and the most suitable fiber out of multiple THz AR-HCFs was selected for biosensing applications. By measuring different cell numbers and different types of tumor cells, a good linear relationship between THz transmittance and the numbers of cells between 10 and 106 was found. Meanwhile, different types of tumor cells can be distinguished by comparing THz transmission spectra, indicating that the biosensor has high sensitivity and specificity for tumor cell detection. The biosensor only required a small amount of sample (as low as 100 μL), and it enables label-free and nondestructive quantitative detection. Our flow cytometry results showed that the cell viability was as high as 98.5 ± 0.26% after the whole assay process, and there was no statistically significant difference compared with the negative control. This study demonstrates that the proposed THz AR-HCF biosensor has great potential for the highly sensitive, label-free, and nondestructive detection of circulating tumor cells in clinical samples.

Prediction of IDH mutation status of glioma based on terahertz spectral data

Gliomas are the most common type of primary tumor in the central nervous system in adults. Isocitrate dehydrogenase (IDH) mutation status is an important molecular biomarker for adult diffuse gliomas. In this study, we were aiming to predict IDH mutation status based on terahertz time-domain spectroscopy technology. Ninety-two frozen sections of glioma tissue from nine patients were included, and terahertz spectroscopy data were obtained. Through Least Absolute Shrinkage and Selection Operator (LASSO), Principal component analysis (PCA), and Random forest (RF) algorithms, a predictive model for predicting IDH mutation status in gliomas was established based on the terahertz spectroscopy dataset with an AUC of 0.844. These results indicate that gliomas with different IDH mutation status have different terahertz spectral features, and the use of terahertz spectroscopy can establish a predictive model of IDH mutation status, providing a new way for glioma research.

Revolutionary approaches for cancer diagnosis by terahertz-based spectroscopy and imaging

Most tumors are easily missed and misdiagnosed due to the lack of specific clinical signs and symptoms in the early stage. Thus, an accurate, rapid and reliable early tumor detection method is highly desirable. The application of terahertz (THz) spectroscopy and imaging in biomedicine has made remarkable progress in the past two decades, which addresses the shortcomings of existing technologies and provides an alternative for early tumor diagnosis. Although issues such as size mismatch and strong absorption of THz waves by water have set hurdles for cancer diagnosis by THz technology, innovative materials and biosensors in recent years have led to possibilities for new THz biosensing and imaging methods. In this article, we reviewed the issues that need to be solved before THz technology is used for tumor-related biological sample detection and clinical auxiliary diagnosis. We focused on the recent research progress of THz technology, with an emphasis on biosensing and imaging. Finally, the application of THz spectroscopy and imaging for tumor diagnosis in clinical practice and the main challenges in this process were also mentioned. Collectively, THz-based spectroscopy and imaging reviewed here is envisioned as a cutting-edge approach for cancer diagnosis.

Label-free study on the effect of a bioactive constituent on glioma cells in vitro using terahertz ATR spectroscopy

In this work, we report that the effect of bioactive constituent on living glioma cells can be evaluated using terahertz time-domain attenuated total reflection (THz TD-ATR) spectroscopy in a label-free, non-invasive, and fast manner. The measured THz absorption coefficient of human glioma cells (U87) in cell culture media increases with ginsenoside Rg3 (G-Rg3) concentration in the range from 0 to 50 µM, which can be interpreted as that G-Rg3 deteriorated the cellular state. This is supported either by the cell growth inhibition rate measured using a conventional cell viability test kit or by the cellular morphological changes observed with fluorescence microscopy. These results verify the effectiveness of using the THz TD-ATR spectroscopy to detect the action of G-Rg3 on glioma cells in vitro. The demonstrated technique thus opens a new route to assessing the efficacy of bioactive constituents on cells or helping screen cell-targeted drugs.

Quantitative Analysis of Industrial Solid Waste Based on Terahertz Spectroscopy

Industrial solid waste refers to the solid waste that is produced in industrial production activities. Without correct treatment and let-off, industrial solid waste may cause environmental pollution due to a variety of pollutants and toxic substances that are contained in it. Conventional detection methods for identifying harmful substances are high performance liquid chromatography (HPLC) and gas chromatography-mass spectrometry (GC-MS), which are complicated, time-consuming, and highly demanding for the testing environment. Here, we propose a method for the quantitative analysis of harmful components in industrial solid waste by using terahertz (THz) spectroscopy combined with chemometrics. Pyrazinamide, benazepril, cefprozil, and bisphenol A are four usual hazardous components in industrial solid waste. By comparing with the Raman method, the THz method shows a much higher accuracy for their concentration analysis (90.3–99.8% vs. 11.7–86.9%). In addition, the quantitative analysis of mixtures was conducted, and the resulting prediction accuracy rate was above 95%. This work has high application value for the rapid, accurate, and low-cost detection of industrial solid waste.

Deep Learning Classification of Breast Cancer Tissue from Terahertz Imaging Through Wavelet Synchro-Squeezed Transformation and Transfer Learning

Terahertz imaging and spectroscopy is an exciting technology that has the potential to provide insights in medical imaging. Prior research has leveraged statistical inference to classify tissue regions from terahertz images. To date, these approaches have shown that the segmentation problem is challenging for images of fresh tissue and for tumors that have invaded muscular regions. Artificial intelligence, particularly machine learning and deep learning, has been shown to improve performance in some medical imaging challenges. This paper builds on that literature by modifying a set of deep learning approaches to the challenge of classifying tissue regions of images captured by terahertz imaging and spectroscopy of freshly excised murine xenograft tissue. Our approach is to preprocess the images through a wavelet synchronous-squeezed transformation (WSST) to convert time-sequential terahertz data of each THz pixel to a spectrogram. Spectrograms are used as input tensors to a deep convolution neural network for pixel-wise classification. Based on the classification result of each pixel, a cancer tissue segmentation map is achieved. In experimentation, we adopt leave-one-sample-out cross-validation strategy, and evaluate our chosen networks and results using multiple metrics such as accuracy, precision, intersection, and size. The results from this experimentation demonstrate improvement in classification accuracy compared to statistical methods, an improvement to segmentation between muscle and cancerous regions in xenograft tumors, and identify areas to improve the imaging and classification methodology.

Characterization and discrimination of human colorectal cancer cells using terahertz spectroscopy

Terahertz technology has been widely used in biomedical research. Herein, terahertz time-domain attenuated total reflection (THz TD-ATR) spectroscopy was employed to characterize and discriminate human cancer cell lines (DLD-1 and HT-29). Terahertz responses of the cell lines were measured and Savitzky-Golay algorithm was applied to smooth the spectra of refractive index, absorption coefficient and dielectric loss tangent in terahertz regime. Principal component analysis (PCA) was then adopted for feature extraction and cell characterization. Based on the processed data, cancer cell lines were discriminated by applying random forests (RF) method to analyze three characteristic parameters separately and the results from them were compared. Results indicate that absorption coefficient was the most sensitive parameter for cancer cell discrimination. Our study suggests great potential for human cancer cell recognition and provides experimental basis for liquid biopsy.

Breast Cancer Detection with Low-dimension Ordered Orthogonal Projection in Terahertz Imaging

This paper proposes a new dimension reduction algorithm based on low-dimension ordered orthogonal projection (LOOP), which is used for cancer detection with terahertz (THz) images of freshly excised human breast cancer tissues. A THz image can be represented by a data cube with each pixel containing a high dimension spectrum vector covering several THz frequencies, where each frequency represents a different dimension in the vector. The proposed algorithm projects the high-dimension spectrum vector of each pixel within the THz image into a low-dimension subspace that contains the majority of the unique features embedded in the image. The low-dimension subspace is constructed by sequentially identifying its orthonormal basis vectors, such that each newly chosen basis vector represents the most unique information not contained by existing basis vectors. A multivariate Gaussian mixture model is used to represent the statistical distributions of the low-dimension feature vectors obtained from the proposed dimension reduction algorithm. The model parameters are iteratively learned by using unsupervised learning methods such as Markov chain Monte Carlo or expectation maximization, and the results are used to classify the various regions within a tumor sample. Experiment results demonstrate that the proposed method achieves apparent performance improvement in human breast cancer tissue over existing approaches such as one-dimension Markov chain Monte Carlo. The results confirm that the dimension reduction algorithm presented in this paper is a promising technique for breast cancer detection with THz images, and the classification results present a good correlation with respect to the histopathology results of the analyzed samples.

Design and Simulation of a Multi-Sheet Beam Terahertz Radiation Source Based on Carbon-Nanotube Cold Cathode

Carbon nanotube (CNT) cold cathodes are proving to be compelling candidates for miniaturized terahertz (THz) vacuum electronic devices (VEDs) owning to their superior field-emission (FE) characteristics. Here, we report on the development of a multi-sheet beam CNT cold cathode electron optical system with concurrently high beam current and high current density. The microscopic FE characteristics of the CNT film emitter is captured through the development of an empirically derived macroscopic simulation model which is used to provide representative emission performance. Through parametrically optimized macroscale simulations, a five-sheet-beam triode electron gun has been designed, and has been shown to emit up to 95 mA at 3.2 kV. Through careful engineering of the electron gun geometric parameters, a low-voltage compact THz radiation source operating in high-order TM5,1 mode is investigated to improve output power and suppress mode competition. Particle in cell (PIC) simulations show the average output power is 33 W at 0.1 THz, and the beam–wave interaction efficiency is approximately 10%.