Intervalley Scattering Induced Terahertz Field Enhancement in Graphene Metasurface.

Terahertz (THz) field enhancement has significant applications in high-resolution imaging, next-generation wireless communications, and networking. In this work, we experimentally demonstrate a graphene metasurface for THz field enhancement that is based on the intervalley scattering theory. Each meta-atom of the metasurface is composed of one split-ring resonator (SRR) embedded in one graphene patch. The experimental results show that, by electrically adjusting the conductivity of the graphene patch, the THz field through the entire sample is enhanced by 23 times and the transmission amplitude at 0.47 THz decreases 8.4 dB. Moreover, the maximum phase difference at 0.43 THz reaches 88°. The experiment shows good agreement with simulation. This study paves a way for exploring THz-matter interactions and nonlinear optics.

Polarization insensitive flexible ultra-broadband terahertz metamaterial absorber.

We propose a polarization insensitive, flexible ultra-broadband terahertz (THz) metamaterial absorber. It consists of a chromium composite resonator on the top, a polyimide (PI) dielectric layer in the middle, and a chromium substrate. The simulation results show that the absorption achieves more than 90% ultra-wideband absorption in the range of 1.92-4.34 THz. The broadband absorption is produced by the combination of electric dipole resonance and magnetic resonance, as well as impedance matching with free space. Due to the rotational symmetry of the unit structure, the absorber is insensitive to polarization of the THz wave and has a larger range of incident angles. The total thickness of the absorber is only 13.4 µm, showing highly flexible and excellent high-temperature resistance characteristics. Therefore, it has potential applications in THz wave stealth and electromagnetic shielding.

Tunable slow light device based on a graphene metasurface.

Slow light devices have significant applications in memory, switching, and quantum optics. However, the design and fabrication of slow light devices with large tunable group delay are still challenging. Here, a graphene-based slow light device that can electrically modulate the group delay of terahertz (THz) waves is proposed and experimentally demonstrated. The unit cell of the device consists of a U-shaped metal resonator and an Ω-shaped metal resonator, with three graphene ribbons embedded between the two resonators. Under electrical stimuli, a relatively high amplitude modulation depth of 74% is achieved and the maximum transmission amplitude is as high as 0.7 at the transmission peak of 0.6 THz. Most importantly, the maximum group delay variation reaches 5 ps at 0.76 THz and the maximum group delay amplitude is as high as 8.8 ps. The experiment shows good agreement with simulation. This study paves a new way for developing novel switchable nanophotonic devices and slow light devices.

2.5D MFFAU-Net: a convolutional neural network for kidney segmentation.

BACKGROUND: Kidney tumors have become increasingly prevalent among adults and are now considered one of the most common types of tumors. Accurate segmentation of kidney tumors can help physicians assess tumor complexity and aggressiveness before surgery. However, segmenting kidney tumors manually can be difficult because of their heterogeneity. METHODS: This paper proposes a 2.5D MFFAU-Net (multi-level Feature Fusion Attention U-Net) to segment kidneys, tumors and cysts. First, we propose a 2.5D model for learning to combine and represent a given slice in 2D slices, thereby introducing 3D information to balance memory consumption and model complexity. Then, we propose a ResConv architecture in MFFAU-Net and use the high-level and low-level feature in the model. Finally, we use multi-level information to analyze the spatial features between slices to segment kidneys and tumors. RESULTS: The 2.5D MFFAU-Net was evaluated on KiTS19 and KiTS21 kidney datasets and demonstrated an average dice score of 0.924 and 0.875, respectively, and an average Surface dice (SD) score of 0.794 in KiTS21. CONCLUSION: The 2.5D MFFAU-Net model can effectively segment kidney tumors, and the results are comparable to those obtained with high-performance 3D CNN models, and have the potential to serve as a point of reference in clinical practice.

Specific detection of n-propanol gas via terahertz metasurface sensor modified by molecularly imprinted polymer.

As an organic substance, n-propanol gas has been paid attention to in environmental monitoring and exhalation of lung cancer patient. In this paper a rapid detection method for n-propanol gas is developed based on molecularly imprinted polymers (MIP) and terahertz (THz) metasurface sensors. We first prepared a MIP suitable for detecting the n-propanol gas. And then the n-propanol MIP was modified to the THz metasurface sensor for detecting the n-propanol gas. Since the MIP adsorbed with n-propanol changes the dielectric environment of the sensor, the resonance frequency of the sensor also change. So we based on the n-propanol concentration was analyzed according to the change in resonance frequency. The experimental results showed that the sensor can effectively detect the n-propanol concentration in the range of 50-500 ppm (parts per million). In addition, we also verified the specificity and repeatability of the sensor. This work provides a new idea and method for the sensitive and specific detection of n-propanol gas.

Detection of biomarkers using terahertz metasurface sensors and machine learning.

To achieve classification and concentration detection of cancer biomarkers, we propose a method that combines terahertz (THz) spectroscopy, metasurface sensors, and machine learning. A metasurface sensor suitable for biomarker detection was designed and fabricated with five resonance frequencies in the range of 0.3-0.9 THz. We collected biomarkers of five types and nine concentrations at 100 sets of time-domain spectra per concentration. The spectrum is processed by noise reduction and fast Fourier transform to obtain the frequency-domain spectrum. Five machine learning algorithms are used to analyze time- and frequency-domain spectra and ascertain which algorithm is more suitable for the classification of the biomarker THz spectrum. Experimental results show that random forest can better distinguish five biomarkers with an accuracy of 0.984 for the time-domain spectrum. For the frequency-domain spectrum, the support vector machine performs better, with an accuracy of 0.989. For biomarkers at different concentrations, we used linear regression to fit the relationship between biomarker concentration and frequency shift. Experimental results show that machine learning can distinguish different biomarker species and their concentrations by the THz spectrum. This work provides an idea and data processing method for the application of THz technology in biomedical detection.

Terahertz phase modulator based on a metal-VO2 reconfigurable metasurface.

Actively controlling the phase of a terahertz (THz) wave is of great significance for beaming, tunable focusing, and holography. We present a THz phase modulator based on an electrically triggered vanadium dioxide (V O 2) reconfigurable metasurface. The unit cell of the device consists of two split-ring resonators embedded with a V O 2 ribbon. By electrically triggering the insulator-to-metal transition of V O 2, the resonance mode and resonance intensity of the unit cell can be dynamically controlled. The simulation results show that the structure can achieve a phase shift of about 360° in the range of 1.03-1.13 THz, and the reflection amplitude can reach 80%. The device has potential applications in THz imaging, radar, broadband wireless communications, and array phase control.

A Novel TP53 Gene Mutation Sustains Non-Small Cell Lung Cancer through Mitophagy.

Lung cancer is the leading cause of cancer death in the world. In particular, non-small-cell lung cancer (NSCLC) represents the majority of the lung cancer population. Advances in DNA sequencing technologies have significantly contributed to revealing the roles, functions and mechanisms of gene mutations. However, the driver mutations that cause cancers and their pathologies remain to be explored. Here, we performed next-generation sequencing (NGS) on tumor tissues isolated from 314 Chinese NSCLC patients and established the mutational landscape in NSCLC. Among 656 mutations, we identified TP53-p.Glu358Val as a driver mutation in lung cancer and found that it activates mitophagy to sustain cancer cell growth. In support of this finding, mice subcutaneously implanted with NSCLC cells expressing TP53-p.Glu358Val developed larger tumors compared to wild-type cells. The pharmaceutical inhibition of autophagy/mitophagy selectively suppresses the cell proliferation of TP53-null or TP53-p.Glu358Val-expressing lung cancer cells. Together, our study characterizes a new TP53 mutation identified from Chinese lung cancer patients and uncovers its roles in regulating mitophagy, providing a new insight into NSCLC treatment.

Analysis of reaction between vitamin B6 and bovine serum albumin based on a terahertz metamaterial sensor.

A four-peak terahertz metamaterial sensor was used to detect the reaction between different concentrations of vitamin B6 (VB6) and bovine serum albumin (BSA), which achieves a concentration range (0.015-0.125 mg/µl) of VB6 and a maximum binding concentration (0.05 mg/µl) of VB6 and 0.0875 mg/µl BSA. To understand the combination of VB6 and BSA, the reactants between VB (VB1, VB3, and VB5) with the same concentration (0.05 mg/µl) and a BSA solution with a concentration of 0.0875 mg/µl were carried on the surface of the sensor. Experimental results show that the reactants cause the four resonance peaks of the sensor to produce the coincident redshift, which is the same as the order of their binding coefficients determined by the fluorescence method. The experimental process indicates that it is feasible to use terahertz metamaterials to detect the reaction process of organic matter.

Terahertz metamaterial biosensor for diagnosis of hepatocellular carcinoma at early stage.

We propose a method for diagnosis of cirrhosis and hepatocellular carcinoma (HCC) by using a terahertz (THz) metamaterial (MM) biosensor. The biosensor has a resonance frequency at about 0.801 THz and can measure the concentration of alpha-fetoprotein (AFP) in serum. The sensitivity of the sensor is 124 GHz/refractive index unit (RIU), and the quality-factor (Q) is 6.913, respectively. When the surface of the biosensor is covered with healthy serum (AFP≤7ng/mL), the maximum resonance frequency shift is 50 GHz. However, when it is covered with serum from patients with cirrhosis and early HCC (AFP>7ng/mL), the resonance frequency shift is more than 59 GHz. Positive correlation exists between the frequency shift of the biosensor and serum levels of the AFP in the HCC patients. This study provides a method for quick diagnosis and prediction of cirrhosis and HCC.

Performance Optimization of Surface Plasmon Resonance Imaging Sensor Network Based on the Multi-Objective Optimization Algorithm.

In this work, we report performance optimization of a wireless sensor network (WSN) based on the plain silver surface plasmon resonance imaging (SPRi) sensor. At the sensor node level, we established the refractive index-thickness models for both gold and silver in the sensor and calculated the depth-width ratio (DWR) and penetration depth (PD) values of the sensor of different gold and silver thicknesses by the Jones transfer matrix and Kriging interpolation. We optimized the DWR and PD simultaneously by using the multi-objective optimization genetic algorithm (MOGA). In the following performance optimization of WSN, we simultaneously optimized the transmission success rate and information dimension with the number of nodes and transmission failure rate of the sensor node as variables by the same algorithm. By calculating the information dimension and the transmission success rate of each Pareto optimal solution, we obtained the number of nodes and transmission failure probability of the node available for practical deployment of WSN. The above results indicate that the Pareto optimal solution set obtained from MOGA can help to provide the best solution for the optimization of some certain performance parameters and also assist us in making the trade-off decision in the structure design and network deployment if optimal values of all the performance parameters can be obtained simultaneously.

Based on lapatinib innovative near-infrared fluorescent probes targeting HER1/HER2 for in vivo tumors imaging.

Human epidermal growth factor receptors HER1 and HER2, overexpressed in non-small cell lung cancer, colorectal cancer, liver cancer, are key regulators of tumor cells proliferation, invasion and survival. Many antibody- or peptide-based fluorescent probes targeted to HER1/HER2 are under active clinical evaluation. However, the effective small-molecule near-infrared (NIR) fluorescent probes are still lacking. Herein, with the strategy of drug repurposing, we developed a series of HER1/HER2-targeted probes YQ-H (01-07) composed of fluorophore (a cyanine dye, MPA), linker unit and targeted unit (lapatinib, LAP). The synthesized probes were evaluated in vitro and in vivo tumor specificity/affinity. Specially, the probe YQ-H-06 exhibited optimal pharmacokinetic property and tumor/normal tissue ratio (T/N) in tumor-bearing mice. Furthermore, we evaluated the targeting capability of YQ-H-06 in orthotopic colorectal cancer and orthotopic hepatic carcinoma mice. It was indicated that YQ-H-06 had the characteristic of great biosafety, favorable pH and chemical stability, as well as provided excellent tumor contrast in orthotopic murine tumor models. The NIR fluorescent probe YQ-H-06 will shed light on tumor detection and fluorescence-guided surgery.

Detection of cancer biomarkers CA125 and CA199 via terahertz metasurface immunosensor.

The cancer biomarkers including AFP, CEA, CA199 and CA125, are of great importance in the diagnosis, prognostic prediction and recurrence monitoring of malignancies. However, in clinical practical applications, most tumor cancer biomarkers are lack of sensitivity and specificity. In this study, we propose a terahertz (THz) metasurface (MS) immunosensor coupled with gold nanoparticles (AuNPs), which have good biocompatibility and high specific surface area for biomarkers. Firstly, we added AuNPs to the surface of the sensor. And then, the surface is modified with Anti-CA125 or Anti-CA199 to improve the sensitivity and specificity to the target antigen. The biosensor was fabricated using a surface micromachining process and characterized by a THz-time-domain spectroscopy (TDS) system. The sensitivity of the resonance frequency of the sensor to the refractive index was 65 GHz/RIU (refractive index unit). The detection performance of the THz immunosensor was also verified with different concentrations of CA125 and CA199. The experimental results showed that the frequency shift of the resonance peak was linearly related to the concentration of CA125 and CA199. The detection limits for both CA125 and CA199 are 0.01 U/ml, which is better than that of other common methods. Finally, serum samples were collected and detected to explore whether this method is suitable for clinical detection. The results are consistent with the results of antigen recognition. This study proves that the practicability of the THz immunosensor, which potentially provides important techniques and equipment for improving the sensitivity and specificity of cancer biomarkers.

Kidney Tumor Segmentation Based on FR2PAttU-Net Model.

The incidence rate of kidney tumors increases year by year, especially for some incidental small tumors. It is challenging for doctors to segment kidney tumors from kidney CT images. Therefore, this paper proposes a deep learning model based on FR2PAttU-Net to help doctors process many CT images quickly and efficiently and save medical resources. FR2PAttU-Net is not a new CNN structure but focuses on improving the segmentation effect of kidney tumors, even when the kidney tumors are not clear. Firstly, we use the R2Att network in the "U" structure of the original U-Net, add parallel convolution, and construct FR2PAttU-Net model, to increase the width of the model, improve the adaptability of the model to the features of different scales of the image, and avoid the failure of network deepening to learn valuable features. Then, we use the fuzzy set enhancement algorithm to enhance the input image and construct the FR2PAttU-Net model to make the image obtain more prominent features to adapt to the model. Finally, we used the KiTS19 data set and took the size of the kidney tumor as the category judgment standard to enhance the small sample data set to balance the sample data set. We tested the segmentation effect of the model at different convolution and depths, and we got scored a 0.948 kidney Dice and a 0.911 tumor Dice results in a 0.930 composite score, showing a good segmentation effect.

Exhaled metabolic markers and relevant dysregulated pathways of lung cancer: a pilot study.

INTRODUCTION: The clinical application of lung cancer detection based on breath test is still challenging due to lack of predictive molecular markers in exhaled breath. This study explored potential lung cancer biomarkers and their related pathways using a typical process for metabolomics investigation. MATERIAL AND METHODS: Breath samples from 60 lung cancer patients and 176 healthy people were analyzed by GC-MS. The original data were GC-MS peak intensity removing background signal. Differential metabolites were selected after univariate statistical analysis and multivariate statistical analysis based on OPLS-DA and Spearman rank correlation analysis. A multivariate PLS-DA model was established based on differential metabolites for pattern recognition. Subsequently, pathway enrichment analysis was performed on differential metabolites. RESULTS: The discriminant capability was assessed by ROC curve of whom the average AUC and average accuracy in 100-fold cross validations were 0.871 and 0.787, respectively. Eight potential biomarkers were involved in a total of 18 metabolic pathways. Among them, 11 metabolic pathways have p-value smaller than .1. DISCUSSION: Some pathways among them are related to risk factors or therapies of lung cancer. However, more of them are dysregulated pathways of lung cancer reported in studies based on genome or transcriptome data. CONCLUSION: We believe that it opens the possibility of using metabolomics methods to analyze data of exhaled breath and promotes involvement of knowledge dataset to cover more volatile metabolites. CLINICAL SIGNIFICANCE: Although a series of related research reported diagnostic models with highly sensitive and specific prediction, the clinical application of lung cancer detection based on breath test is still challenging due to disease heterogeneity and lack of predictive molecular markers in exhaled breath. This study may promote the clinical application of this technique which is suitable for large-scale screening thanks to its low-cost and non-invasiveness. As a result, the mortality of lung cancer may be decreased in future.Key messagesIn the present study, 11 pathways involving 8 potential biomarkers were discovered to be dysregulated pathways of lung cancer.We found that it is possible to apply metabolomics methods in analysis of data from breath test, which is meaningful to discover convinced volatile markers with definite pathological and histological significance.

Surface-Functionalized Terahertz Metamaterial Biosensor Used for the Detection of Exosomes in Patients.

Owing to their stability in bodily fluids, exosomes have attracted increased attention as colorectal cancer (CRC) biomarkers for early diagnosis. To validate the potential of the plasma exosomes as a novel biomarker for the monitoring of CRC, we demonstrated a terahertz (THz) metamaterials (MMs) biosensor for the detection of exosomes in this work. The biosensor with two resonant frequencies is designed using full wave electromagnetic simulation software based on the finite integration time domain (FITD) method and fabricated by a surface micromachining process. The biosensor surface is first modified using Au nanoparticles (AuNPs), and then, anti-KRAS and anti-CD147, which are specific to the exosomes, are modified on the AuNPs assembled with HS-poly(ethylene glycol)-COOH (HS-PEG-COOH). Exosomes used in the experiment are extracted via the instructions in the exosomes isolation and purification kit and identified by using transmission electron microscopy (TEM), Western blot (WB), and nanoparticle tracking analysis (NTA). The biosensor covered with plasma-derived exosomes of CRC patients has a different resonance frequency shift compared to that with healthy-control-derived exosomes. This study proposes an emerging and quick method for diagnosing the CRC.

Azimuth-Resolved Circular Dichroism of Metamaterials.

Chiral optical metamaterials have attracted a great deal of attention due to their intriguing properties with respect to fundamental research and practical applications. For metamaterials with achiral structures, the system composed of metamaterials and obliquely incident light has extrinsic chirality and can produce circular dichroism (CD) effect. However, there have been few studies on the azimuth-dependent CD spectra of achiral metamaterials that have greatly improved our understanding of optical phenomena caused by external chirality. In this work, we experimentally studied the azimuth-dependent CD that originated from the extrinsic chirality of the metamaterials in an asymmetric-U shape and a U-bar-shape gold unit structure, separately. We explain the origin of the CD in the coupling of the macro-electric dipole and magnetic dipole, and the simulation results are in good agreement with the experiment. Our results provide a possible way to build an on-chip azimuth sensor based on azimuth-dependent CD spectra of metamaterials.

Segmentation of kidney mass using AgDenseU-Net 2.5D model.

The Kidney and Kidney Tumor Segmentation Challenge 2021 (KiTS21) released a kidney CT dataset with 300 patients. Unlike KiTS19, KiTS21 provided a cyst category. Therefore, the segmentation of kidneys, tumors, and cysts will be able to assess the complexity and aggressiveness of kidney mass. Deep learning models can save medical resources, but 3D models still have some disadvantages, such as the high cost of computing resources. This paper proposes a scheme that saves computing resources and achieves the segmentation of kidney mass in two steps. First, we preprocess the kidney volume data using the automatic down-sampling method of 3D images, reducing the volume while preserving the feature information. Second, we finely segment kidneys, tumors, and cysts using the AgDenseU-Net (Attention gate DenseU-Net) 2.5D model. KiTS21 proposed using Hierarchical Evaluation Classes (HECs) to compute a metric for the superset: the HEC of kidney considers kidneys, tumors, and cysts as the foreground to compute segmentation performance; the HEC of kidney mass considers both tumor and cyst as the foreground classes; the HEC of tumor considers tumor as the foreground only. For KiTS21, our model achieved a dice score of 0.971 for the kidney, 0.883 for the mass, and 0.815 for the tumor. In addition, we also tested segmentation results without HECs, and our model achieved a dice score of 0.950 for the kidney, 0.878 for the tumor, and 0.746 for the cyst. The results demonstrate that the method proposed in this paper can be used as a reference for kidney tumor segmentation.

Ultra-broadband terahertz bandpass filter with dynamically tunable attenuation based on a graphene-metal hybrid metasurface.

We propose an ultra-broadband terahertz bandpass filter with dynamically tunable attenuation based on a graphene-metal hybrid metasurface. The metasurface unit cell is composed of two metal stripes enclosed with a graphene rectangular ring. Results show that when the metasurface is normally illuminated by a terahertz wave polarized along the metal stripes, it can act as an ultra-broadband bandpass filter over the spectral range from 1.49 THz to 4.05 THz, corresponding to a fractional bandwidth of 92%. Remarkably, high transmittance above 90% covering the range from 1.98 THz to 3.95 THz can be achieved. By changing the Fermi level of graphene, we find that the attenuation within the passband can be dynamically tuned from 2% to 66%. We expect that the proposed ultra-broadband terahertz bandpass filter with tunable attenuation will find applications in terahertz communication and detection and sensing systems.

Application of terahertz spectroscopy combined with density functional theory to analysis of intermolecular weak interactions for coumarin and 6-methylcoumarin.

The terahertz (THz) absorption spectra of coumarin and 6-methylcoumarin have been investigated by terahertz time-domain spectroscopy (THz-TDS) system in the frequency range from 0.4 to 2.8 THz. Density functional theory (DFT) calculations, both with and without London force dispersion corrections, have been used for the assignment of the experimental THz spectra. To thoroughly interpret the spectrum information, we used potential energy distribution (PED) method to assign the vibrational modes of the absorption peaks, and identify the origin of the absorption peaks by electrostatic potential (ESP) and van der Waals (vdW) potential distribution analysis method. The results show that absorption peaks both for coumarin and 6-methylcoumarin are caused by electrostatic interaction in the lower frequency range, while vdW interaction in the higher frequency. Moreover, the potential energy distribution of electrostatic and vdW between them is basically the same, and it led to the similarity of THz spectra between coumarin and 6-MC. This work has demonstrated that using THz spectroscopy combined with DFT calculations is an effective way to analysis of intermolecular weak interactions and biomolecules with similar structures.