Terahertz (THz) biosensors as an advanced approach for non-destructive, label-free, and long-term monitoring of cellular states, hold tremendous potential in early disease screening and cellular drug sensitivity testing. This paper presents a THz metasurface composed of two semi-circular split-ring resonators based on quasi-bound states in the continuum (Q-BIC) mechanism. The localized field enhancement of the Q-BIC structure can significantly boost strong light-matter interactions, enabling highly sensitive detection of biological analytes. The sensitivity of the presented biosensor reaches as high as 517 GHz/RIU. Via resonance frequency shifts, we achieve ultrasensitive distinguishment between different concentrations of cancer cells (HepG2) and normal cells (293T). The results demonstrate that, without the need for additional factors, the frequency shift of the Q-BIC resonance can successfully identify different types of biological cells of varying cell concentrations. The experimental sensitivity reaches up to 456.64 kHz/(cell mL^-1), with a detection limit of 5×10³ cells/mL, confirming the feasibility of the designed structure. The QBIC-based THz biosensor exhibits significant potential in distinguishing different biological cells and detecting low-concentration cells, paving a new path for ultra-sensitive biomedical detection.

The prevalence of low-dose CT (LDCT) in lung cancer screening has gradually increased, and more and more lung ground glass nodules (GGNs) have been detected. So far, a consensus has been reached on the treatment of single pulmonary ground glass nodules, and there have been many guidelines that can be widely accepted. However, at present, more than half of the patients have more than one nodule when pulmonary ground glass nodules are found, which means that different treatment methods for nodules may have different effects on the prognosis or quality of life of patients. This article reviews the research progress in the diagnosis and treatment strategies of pulmonary multiple lesions manifested as GGNs.

The method based on conventional index and UV-vision has been widely applied in the field of water quality abnormality detection. This paper presents a qualitative analysis approach to detect the water contamination events with unknown pollutants. Fluorescence spectra were used as water quality monitoring tools, and the detection method of unknown contaminants in water based on alternating trilinear decomposition (ATLD) is proposed to analyze the excitation and emission spectra of the samples. The Delaunay triangulation interpolation method was used to make the pretreatment of three-dimensional fluorescence spectra data, in order to estimate the effect of Rayleigh and Raman scattering; ATLD model was applied to establish the model of normal water sample, and the residual matrix was obtained by subtracting the measured matrix from the model matrix; the residual sum of squares obtained from the residual matrix and threshold was used to make qualitative discrimination of test samples and distinguish drinking water samples and organic pollutant samples. The results of the study indicate that ATLD modeling with three-dimensional fluorescence spectra can provide a tool for detecting unknown organic pollutants in water qualitatively. The method based on fluorescence spectra can be complementary to the method based on conventional index and UV-vision.

Objective: The causal associations of circulating lipids with Barrett’s Esophagus (BE) and Esophageal Cancer (EC) has been a topic of debate. This study sought to elucidate the causality between circulating lipids and the risk of BE and EC. Methods: We conducted two-sample Mendelian randomization (MR) analyses using single nucleotide polymorphisms (SNPs) of circulating lipids (n = 94,595 − 431,167 individuals), BE (218,792 individuals), and EC (190,190 individuals) obtained from the publicly available IEU OpenGWAS database. The robustness and reliability of the results were ensured by employing inverse-variance weighted (IVW), weighted median, MR-Egger, and MR-PRESSO methods. The presence of horizontal pleiotropy, heterogeneities, and stability of instrumental variables were assessed through MR-Egger intercept test, Cochran’s Q test, and leave-one-out sensitivity analysis. Additionally, bidirectional MR and multivariable MR (MVMR) were performed to explore reverse causality and adjust for known confounders, respectively. Results: None of the testing methods revealed statistically significant horizontal pleiotropy, directional pleiotropy, or heterogeneity. Univariate MR analyses using IVW indicated a robust causal relationship between increased triglycerides and BE (odds ratio [OR] = 1.79, p-value = 0.009), while no significant association with EC was observed. Inverse MR analysis indicated no evidence of reverse causality in the aforementioned outcomes. In MVMR analyses, elevated triglycerides (TRG) were significantly and positively associated with BE risk (OR = 1.79, p-value = 0.041). Conclusion: This MR study suggested that genetically increased triglycerides were closely related to an elevated risk of BE, potentially serving as a biomarker for the diagnosis of BE in the future.

Terahertz (THz) spectroscopy has unique sensing capabilities for biological cells due to its high temporal resolution and label-free characteristics. By combining THz technologies with the local enhancement effects of the electric field induced by the metasurface, high sensitivity detection of biological analytes can be achieved. In this work, an ultrasensitive THz metasurface biosensor based on Parity-time (PT) symmetry is proposed. Consisting of a cut wire and a pair of split ring resonators, the exceptional point structure in PT symmetry can realize a balance between the gain and the loss, which leads to a high detection sensitivity. The simulation shows that the proposed biosensor can reach a sensitivity as high as 584 GHz/RIU at the exceptional point with polarization-insensitive stability across a ±35° angle range. Experimentally, the biosensor achieves a sensitivity up to 1030.51 kHz/ (cell/mL-1) in detecting different biological cells. Principal component analysis is used to reduce the dimensionality of features composed of frequency shifts and peak amplitudes. A random forest model is then used to classify the processed features and achieves a 98.9% identification accuracy. The proposed biosensor demonstrates capabilities of highly sensitive detection of cancer cells, providing an effective and rapid method for early cancer screening, grading, and staging.