BACKGROUND: Moxibustion, a traditional Chinese medicine practice, employs Moxa Wool, derived from Artemisia argyi. Flavonoids, the key pharmacological constituents in Moxa Wool, are known for their anti-inflammatory and analgesic properties. The purity of Moxa Wool, particularly its flavonoid content, directly influences the efficacy of moxibustion treatments. However, quantifying these bioactive flavonoids accurately and non-destructively has been a challenge.
OBJECTIVE: This study introduces terahertz spectroscopy as a non-destructive optical detection method for qualitative detection and quantitative analysis of flavonoids in Moxa Wool. By establishing a mathematical model between spectral signals and clinical efficacy, a reliable correlation between flavonoid concentration and the therapeutic effect of moxibustion can be established, providing a potential predictive model for the treatment outcomes of rheumatoid arthritis.
METHODS: We adopted terahertz spectroscopy technology and combined it with terahertz metamaterial biosensors to achieve rapid, efficient, and non-destructive testing of the quality of Moxa Wool. This method reduces the detection time from hours to minutes while lowering the sample detection limit, overcoming the limitations of traditional detection methods in pharmacological research.
METHODS: Through terahertz metamaterial biosensors, rapid detection of the purity of Moxa Wool has been achieved. A combination of molecular simulation and terahertz spectroscopy was used to quantitatively analyze the flavonoid content in different purities of Moxa Wool. To ensure accuracy, the quantitative results of flavonoids obtained by terahertz spectroscopy were validated using high-performance liquid chromatography (HPLC). In addition, moxibustion treatment was performed on rats with rheumatoid arthritis using Moxa Wool, and medical indicator information was recorded. A mathematical analysis model was established to evaluate the correlation between flavonoid content and analgesic and anti-inflammatory effects.
RESULTS: Terahertz spectroscopy analysis shows that there is a direct correlation between the flavonoid content in moxibustion and the absorption peak intensity. The maximum R2 in the model analysis is 0.98, indicating a high accuracy in predicting the purity of Moxa Wool. These results were also validated by HPLC. In a rat model, the purity of 30:1 Moxa Wool samples showed a 50 % decrease in TNF-α, IL-1β, and IL-6 levels during treatment compared to low-purity samples, significantly reducing inflammation markers and pain symptoms. Meanwhile, The PLS prediction model established a correlation between terahertz-detected flavonoid levels and treatment outcomes (PWL and IL-1β). The maximum R2 in the model is 0.91, indicating a high correlation between flavonoid levels and the anti-inflammatory and analgesic effects of moxibustion treatment.
CONCLUSIONS: This study not only demonstrates the effectiveness of terahertz spectroscopy in the pharmacological quantification of bioactive compounds but also establishes a novel predictive model for the efficacy of moxibustion in rheumatoid arthritis treatment. It underscores the potential of integrating traditional medicine insights with advanced technology to enhance therapeutic strategies in pharmacology.

Assessing crop seed phenotypic traits is essential for breeding innovations and germplasm enhancement. However, the tough outer layers of thin-shelled seeds present significant challenges for traditional methods aimed at the rapid assessment of their internal structures and quality attributes. This study explores the potential of combining terahertz (THz) time-domain spectroscopy and imaging with semantic segmentation models for the rapid and non-destructive examination of these traits. A total of 120 watermelon seed samples from three distinct varieties, were curated in this study, facilitating a comprehensive analysis of both their outer layers and inner kernels. Utilizing a transmission imaging modality, THz spectral images were acquired and subsequently reconstructed employing a correlation coefficient method. Deep learning-based SegNet and DeepLab V3+ models were employed for automatic tissue segmentation. Our research revealed that DeepLab V3+ significantly surpassed SegNet in both speed and accuracy. Specifically, DeepLab V3+ achieved a pixel accuracy of 96.69 % and an intersection over the union of 91.3 % for the outer layer, with the inner kernel results closely following. These results underscore the proficiency of DeepLab V3+ in distinguishing between the seed coat and kernel, thereby furnishing precise phenotypic trait analyses for seeds with thin shells. Moreover, this study accentuates the instrumental role of deep learning technologies in advancing agricultural research and practices.

The inadequate stability of organic-inorganic hybrid perovskites remains a significant barrier to their widespread commercial application in optoelectronic devices. Aging phenomena profoundly affect the optoelectronic performance of perovskite-based devices. In addition to enhancing perovskite stability, the real-time detection of aging status, aimed at monitoring the aging progression, holds paramount importance for both fundamental research and the commercialization of organic-inorganic hybrid perovskites. In this study, the aging status of perovskite was real-time investigated by using terahertz time-domain spectroscopy. Our analysis consistently revealed a gradual decline in the intensity of the absorption peak at 0.968 THz with increasing perovskite aging. Furthermore, a systematic discussion was conducted on the variations in intensity and position of the terahertz absorption peaks as the perovskite aged. These findings facilitate the real-time assessment of perovskite aging, providing a promising method to expedite the commercialization of perovskite-based optoelectronic devices.

Terahertz spectroscopy has unique advantages in the study of biological molecules in aqueous solutions. However, water has a strong absorption capability in the terahertz region. Reducing the amount of liquid could decrease interference with the terahertz wave, which may, however, affect the measurement accuracy. Therefore, it is particularly important to balance the amount and water content of liquid samples. In this work, a terahertz metamaterial sensor based on metallic strips is designed, fabricated, and used to detect reverse micelles. An aqueous confinement environment in reverse micelles can improve the signal-to-noise ratio of the terahertz response. Due to \"water pool\" trapped in reverse micelles, the DOPC (1,2-dioleoyl-sn-glycero-3-phosphocholine) solution and DOPC emulsion can successfully be identified in intensity by terahertz spectroscopy. Combined with the metamaterial sensor, an obvious frequency shift of 30 GHz can be achieved to distinguish the DOPC emulsion (5%) from the DOPC solution. This approach may provide a potential way for improving the sensitivity of detecting trace elements in a buffer solution, thus offering a valuable toolkit toward bioanalytical applications.

Terahertz (THz) spectroscopy has demonstrated significant potential for substance detection due to its low destructiveness and due to the abundance of molecular fingerprint absorption signatures that it contains. However, there is limited research on the fingerprint detection of substances at different temperatures. Here, we propose a THz metamaterial slit array sensor that exploits localized surface plasmons to enhance the electric field within the slit. The transmission peak frequency can be modulated via temperature adjustments. This method enables the detection of molecular absorption characteristics at multiple spectral frequency points, thereby achieving a specific and highly sensitive detection of characteristic analyte fingerprint spectra. Additionally, the sensor supports the detection of substances at multiple temperatures and sensitively identifies changes in their absorption properties as a function of temperature. Our research has employed temperature variation to achieve a highly sensitive and specific detection of trace analytes, offering a new solution for THz molecular detection.

L-Glutamic acid (L-Glu) is a basic unit of proteins and also serves as an important neurotransmitter in the central nervous system. Its structural properties are critical for biological functions and selective receptor recognition. Although this molecule has been extensively studied, the low frequency vibrational behavior that is closely related to conformational changes and the intermolecular interactions between L-Glu and its receptors are still unclear. In this study, we acquired the fingerprint spectrum of L-Glu by using air plasma terahertz (THz) time-domain spectroscopy in the 0.5-18 THz range. The low frequency vibrational characteristics of L-Glu were investigated through density functional theory (DFT) calculations. The THz responses of the ligand binding domain of the NMDAR-L-Glu complex were studied by the ONIOM method, with a focus on discussing the normal modes and interactions of ligand L-Glu and water molecules. The results illustrate that THz spectroscopy exhibits a sensitive response to the influence of L-Glu on the structure of the NMDAR. The water molecules in proteins have various strong vibration modes in the THz band, showing specificity, diversity and complexity of vibrational behavior. There is potential for influencing and regulating the structural stability of the NMDAR-L-Glu complex through water molecules.

Chirality plays an important role in medicine, biology, and chemistry. Molecules of different chirality could display dramatically different medical effects, pharmacological activities, and physiological impacts. Ibuprofen is an important anti-inflammatory drug in clinics. The anti-inflammatory effect is almost solely attributed to the (S)-(+)-Ibuprofen, while its enantiomer (R)-(-)-Ibuprofen plays a negative effect on increasing the metabolic burden. In this work, a terahertz (THz) polarization-sensitive metasurface sensor is proposed for qualitative and quantitative identification of the chiral Ibuprofen. The chirality parameters of Ibuprofen are extracted from the circular-polarized transmission coefficients. The parameters are further used to simulate the coupling mechanism between the Ibuprofen and the sensor to explain the principle of recognition. The sensitivities of (R)-(-)-Ibuprofen and (S)-(+)-Ibuprofen are found to be 1.5 THz/(mg/L) and 1.8 THz/(mg/L) for the TM polarization, respectively, and 1.7 THz/(mg/L) and 2.1 THz/(mg/L) for the TE polarization, respectively. The difference enables the chirality identification according to the different frequency shift at the same concentration. The exceptional specificity and sensitivity provide a new avenue for chiral molecular recognition.

Pyrazinamide (PZA) is a widely-used anti-tuberculosis pharmaceutical, but its poor solubility prompts us to optimize pharmaceutical performance. Cocrystallization is a promising technique to improve physiochemical properties of active pharmaceutical ingredient (API) by connecting it with cocrystal former (CCF) via intermolecular interactions. Even though a series of alkyl dicarboxylic acids are employed to form cocrystal structures, systematic understanding on the role of intermolecular interactions is still missing. Therefore, terahertz (THz) spectroscopy and quantum chemical calculation are combined to elucidate the behavior of ubiquitous supramolecular synthons, such as hetero-synthons of acid-pyrazine, acid-amide and homo-synthon of amide-amide, from energy\'s view. Potential energy is calculated to differentiate the stability within polymorphs of PZA-MA cocrystal and free energy is evaluated to compare the solubility of PZA-CCF cocrystals respectively. With regard to vibrational energy, THz spectral fingerprints are theoretically assigned to specific vibrations and attributed to the flexibility deformation of supramolecular synthons based on oscillation theory, where stretching and twisting modes dominate the collective vibrational behavior. It provides a promising tool to evaluate cocrystal performance from its driving force and insightful guidance to discover new pharmaceutical cocrystals.

Quasi-bound state in the continuum (QBIC) can effectively enhance the interaction of terahertz (THz) wave with matter due to the tunable high-Q property, which has a strong potential application in the detection of low-concentration biological samples in the THz band. In this paper, a novel THz metamaterial sensor with a double-chain-separated resonant cavity structure based on QBIC is designed and fabricated. The process of excitation of the QBIC mode is verified and the structural parameters are optimized after considering the ohmic loss by simulations. The simulated refractive index sensitivity of the sensor is up to 544 GHz/RIU, much higher than those of recently reported THz metamaterial sensors. The sensitivity of the proposed metamaterial sensor is confirmed in an experiment by detecting low-concentration lithium citrate (LC) and bovine serum albumin (BSA) solutions. The limits of detection (LoDs) are obtained to be 0.0025 mg/mL (12 μM) for LC and 0.03125 mg/mL (0.47 μM) for BSA, respectively, both of which excel over most of the reported results in previous studies. These results indicate that the proposed THz metamaterial sensor has excellent sensing performances and can well be applied to the detection of low-concentration biological samples.

Terahertz time-domain spectroscopy (THz-TDS) is an emerging optical technique that has potential applications in the characterization of (bio)materials. However, the complicated extraction of optical parameters from multi-layered and optically thin samples is a barrier towards its acceptance by applied scientists. Therefore, the aim of this work is to provide a straightforward approach for the extraction of the THz absorption coefficient and index of refraction profiles of aqueous thin films in a window-sample-window configuration, which is ubiquitous in many laboratories (i.e., sample in a cuvette). A numerical approach-based methodology that accounts for multiple layers, Fabry-Pérot effect, and sample thickness is elaborated which involves an optical interference model based on a tri-layer structure and a simple thickness estimation technique. This method was validated on water samples where a good agreement was found with the THz optical parameters of water reported in the literature, while the use of a commercial software resulted in erroneous optical parameters estimates when used without due regard to its limitations. A case study was then performed to demonstrate the ability of the proposed method to characterize agarose hydrogels with varying degree of sulfation. It was demonstrated that THz-TDS can provide insight into the hydration state of the agarose hydrogels, including the relative number of the hydrogen bonds between the hydroxyl moieties of water and the polysaccharide network which is perturbed by the presence of sulfate. The trend in the index of refraction profiles suggested microstructural differences between the agarose hydrogels, which were confirmed by visualizing the agarose network morphology using cryo-SEM imaging.