Label-free detection of living cervical cells based on microfluidic device with terahertz spectroscopy

Siamese deep learning video flow cytometry for automatic and label-free clinical cervical cancer cell analysis

Automatic and label-free screening methods may help to reduce cervical cancer mortality rates, especially in developing regions. The latest advances of deep learning in the biomedical optics field provide a more automatic approach to solving clinical dilemmas. However, existing deep learning methods face challenges, such as the requirement of manually annotated training sets for clinical sample analysis. Here, we develop Siamese deep learning video flow cytometry for the analysis of clinical cervical cancer cell samples in a smear-free manner. High-content light scattering images of label-free single cells are obtained via the video flow cytometer. Siamese deep learning, a self-supervised method, is built to introduce cell lineage cells into an analysis of clinical cells, which utilizes generated similarity metrics as label annotations for clinical cells. Compared with other deep learning methods, Siamese deep learning achieves a higher accuracy of up to 87.11%, with about 5.62% improvement for label-free clinical cervical cancer cell classification. The Siamese deep learning video flow cytometry demonstrated here is promising for automatic, label-free analysis of many types of cells from clinical samples without cell smears.

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.

Spatial and spectral characteristics in realizations of broadband terahertz spectroscopy on a subwavelength scale

In this work, we take aim at the fundamental challenge for realizations of broadband terahertz (THz) spectroscopy on a subwavelength scale. We introduce apertured THz microjets in this effort to resolve the fundamental limits of spatial resolution and spectral bandwidth. The THz microjets are formed as intense foci at the rear of engineered (microcomposite) spheres and are coupled through subwavelength (circular) apertures. Such coupling enables effective transmission of THz power through samples with broad spectral bandwidths and fine spatial resolutions. We show that the apertures function as high-pass filters, with their diameter d enabling strong transmission above a cutoff frequency f_c. Our theoretical and experimental results reveal that the values for d and f_c are prescribed by a fixed spatial-spectral product df_c, whereby reductions in d (to improve the spatial resolution) can raise f_c into the targeted spectrum (at the expense of spectral bandwidth). We use this understanding to demonstrate broadband (0.3-0.7 THz) THz spectroscopy of lactose at the subwavelength (365 µm) scale. These results for apertured THz microjets represent a 20-fold improvement in spatial resolution over analogous apertured THz plane waves. Overall, our findings show promise for studies of carcinogenesis, pathogenesis, and the like.

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.

Quantitative analysis of aqueous biomolecular mixtures by THz spectroscopy based on high-power LiNbO3 radiation source

The rapid and accurate identification of the types and contents of early pathological markers by THz technology are of particular importance for the prevention and treatment of major diseases. Nevertheless, these markers usually contain interference from water and other non-target molecules, resulting in low signal-to-noise ratio (SNR) and making identification and quantitative analysis difficult. Here, based on THz spectroscopy from a high-power THz source radiated by LiNbO₃, we perform quantitative and real-time THz detection of mixtures (α-lactose monohydrate and 4-aminobenzoic acid) in liquids. The results demonstrate that the absorption spectra of the aqueous biomolecular mixtures exhibit an accumulation of THz features of each pure product, i.e., the amplitude of the absorption peaks is proportional to the mixing ratio, while the corresponding absorption baseline increases with decreasing concentration. Furthermore, the content of the target substance can be calculated from the linear relationship between the absorption spectra of pure and mixed samples. This technology will support the future application of THz-TDS in early disease diagnosis under complex states and environments.