Identification of Panax notoginseng origin using terahertz precision spectroscopy and neural network algorithm

Panax notoginseng (P. notoginseng), a Chinese herb containing various saponins, benefits immune system in medicines development, which from Wenshan (authentic cultivation) is often counterfeited by others for large demand and limited supply. Here, we proposed a method for identifying P. notoginseng origin combining terahertz (THz) precision spectroscopy and neural network. Based on the comparative analysis of four qualitative identification methods, we chose high-performance liquid chromatography (HPLC) and THz spectroscopy to detect 252 samples from five origins. After classifications using Convolutional Neural Networks (CNNs) model, we found that the performance of THz spectra was superior to that of HPLC. The underlying mechanism is that there are clear nonlinear relations among the THz spectra and the origins due to the wide spectra and multi-parameter characteristics, which makes the accuracy of five-classification origin identification up to 97.62%. This study realizes the rapid, non-destructive and accurate identification of P. notoginseng origin, providing a practical reference for herbal medicine.

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.

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.

Biomedical applications of terahertz spectra in clinical and molecular pathology of human glioma

Gliomas are the most common type of primary tumor originating in the central nervous system of adults. Tumor histological type, pathological grade, and molecular pathology are significant prognosis and predictive factors. In this study, we were aiming to predict histological type and molecular pathological features based on terahertz time-domain spectroscopy technology. Nine gliomas with different grades, one meningioma, and one lymphoma were enrolled. There were significant differences in terahertz absorption coefficient between normal brain tissue, tumoral-periphery, and tumoral-center tissue in specific frequency bands (0.2-1.4 THz). Histological type, pathological grade, and glioma-specific biomarkers were closely related to the terahertz absorption coefficient in both tumoral-periphery and tumoral-center tissues. Interestingly, tumoral-periphery showed more obvious differences than tumoral-center tissues in almost all aspects. All the results show that the terahertz technology has potential application value in the intraoperative real-time glioma recognition and diagnosis of glioma histological and molecular pathological features.

Probing lattice vibration of alkali halide crystals by broadband terahertz spectroscopy

Terahertz spectral features of alkali halide crystals were studied with the combination of broadband terahertz time-domain spectroscopy and the solid-state-based density functional theory calculations. To understand the particular modes of the observed terahertz features of the alkali halide crystals, the resonant modes of KCl and CsCl were analyzed using face-centered cubic and body-centered cubic lattice models, respectively. The results show that the characteristic terahertz absorption peaks could be assigned to the lattice vibration of the ionic crystals. Furthermore, the terahertz responses of a series of alkali halides were recorded, and obvious absorption peaks were observed in each salt in the frequency region below 8.5 THz. What is more interestingly is that the frequencies of these observed peaks are red-shifted with the increases of the mass and radius of the ions. This correlation between the resonant frequency of the lattice vibration, the reduced atomic mass, and the equilibrium distance between the ions agrees well with the harmonic oscillator model.

Far-infrared terahertz properties of L-cysteine and its hydrochloride monohydrate

As the building blocks of proteins, amino acids serve vital metabolic functions in addition to protein synthesis and thus attract enormous interest. Here we reported the far-infrared optical properties of L-cysteine (Lcys) and its hydrochloride monohydrate (LCHM) characterized by terahertz time-domain spectroscopy. The Lcys and LCHM exhibit quite distinct characteristics in the terahertz region due to diverse collective vibrations of the molecules, which is further confirmed by the solid-state density functional theory (DFT) calculations. The presented studies indicate that the intermolecular hydrogen bonds play a critical role in the far-infrared terahertz response of Lcys and LCHM.