Terahertz plasmonics: The rise of toroidal metadevices towards immunobiosensings

The terahertz electromagnetically induced transparency-like metamaterials for sensitive biosensors in the detection of cancer cells.

With the non-ionizing, non-invasive, high penetration, high resolution and spectral fingerprinting features of terahertz (THz) wave, THz spectroscopy has great potential for the qualitative and quantitative identification of key substances in biomedical field, such as the early diagnosis of cancer, the accurate boundary determination of pathological tissue and non-destructive detection of superficial tissue. However, biological samples usually contain various of substances (such as water, proteins, fat and fiber), resulting in the signal-to-noise ratio (SNR) for the absorption peaks of target substances are very small and then the target substances are hard to be identified. Here, we present recent works for the SNR improvement of THz signal. These works include the usage of attenuated total reflection (ATR) spectroscopy, the fabrication of sample-sensitive metamaterials, the utilization of different agents (including contrast agents, optical clearing agents and aptamers), the application of reconstruction algorithms and the optimization of THz spectroscopy system. These methods have been proven to be effective theoretically, but only few of them have been applied into actual usage. We also analyze the reasons and summarize the advantages and disadvantages of each method. At last, we present the prospective application of THz spectroscopy in biomedical field.

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Terahertz spectroscopy in biomedical field: a review on signal-to-noise ratio improvement

A sickle-shaped metamaterial (SSM) based biochemical sensor with multiple resonances was investigated in the terahertz frequency range. The electromagnetic responses of SSM were found to be four resonances, namely dipolar, quadrupolar, octupolar and hexadecapolar plasmon resonances. They were generated from the interactions between SSM and perpendicularly incident terahertz waves. The sensing performances of SSM-based biochemical sensors were evaluated by changing ambient environments and analyte varieties. The highest values of sensitivity and figure of merit (FOM) for SSM covered with analyte thin-films were 471 GHz/RIU (refraction index unit) and 94 RIU−1, respectively. In order to further investigate the biosensing ability of the proposed SSM device, dielectric hemispheres and microfluidic chips were adopted to imitate dry and hydrous biological specimens, respectively. The results show that the sensing abilities of SSM-based biochemical sensors could be enhanced by increasing either the number of hemispheres or the channel width of the microfluidic chip. The highest sensitivity was 405 GHz/RIU for SSM integrated with microfluidic chips. Finally, three more realistic models were simulated to imitate real sensing situations, and the corresponding highest sensitivity was 502 GHz/RIU. The proposed SSM device paves the way to possible uses in biochemical sensing applications.

https://www.mdpi.com/2079-4991/10/6/1038/pdf

Terahertz Metamaterial with Multiple Resonances for Biosensing Application.

A biosensor based on electromagnetically induced transparent (EIT) metamaterials (MMs) is proposed owing to the low loss and high Q-factor. The theoretical sensitivity of the biosensor based on EIT-like MMs were evaluated up to 248.8 GHz/RIU (RIU, Refractive Index Unit). In experiments, the cancer cells A549, as an analyte, are cultured on EIT-like MMs surface. The results show that when the cell concentration increases from 0.5 × 105 to 5 × 105 cells/ml, the frequency shift Δf could change from 24 to 50 GHz. Moreover, the coupled oscillators model is applied to explain the effect of the refractive index of analyte in simulations and the cell concentration in experiments on the EIT-like MMs. The fitting results exhibit that the refractive index of analyte and cell concentration significantly affect the radiative damping of the bright mode resonator γ1. The proposed EIT-like MMs biosensors show great potentials for cell measurement because any change that results in the lineshape variation in EIT-like MMs can only be attributed to the change of external dielectric environment due to the suppression of radiative losses.

Electromagnetically induced transparency-like metamaterials for detection of lung cancer cells

The apoptosis of cancer cells was experimentally measured by terahertz (THz) biosensors based on the metamaterials (MMs). The non-bianisotropic resonance with an electric field of up to 106 V/m was exhibited at 0.85 THz, where the magnetic dipoles were cancelled in the unit cell. The simulate results show the dependence of the frequency shift on the occupying ratio and refractive index of analytes. The theoretical sensitivity was calculated to 182 GHz/RIU. The experimental results imply that the resonant frequency would red shift with the increase of the concentration of cancer cells. Furthermore, the apoptosis of cancer cells HSC3 under the effect of drug concentration from 1 to 15 μM and drug action time from 24 to 72 hours were also studied by the biosensors, respectively. It shows that the trend agrees with the results measured by the biological CCK-8 kits method. Our proposed MMs-based biosensors may supply a novel viewpoint on cell apoptosis from a physical perspective and be a valuable complementary reference for biological study.

Sensitive detection of cancer cell apoptosis based on the non-bianisotropic metamaterials biosensors in terahertz frequency

Imbalanced data classification: A KNN and generative adversarial networks-based hybrid approach for intrusion detection

Imbalanced sample distribution is usually the main reason for the performance degradation of machine learning algorithms. Based on this, this study proposes a hybrid framework (RGAN-EL) combining generative adversarial networks and ensemble learning method to improve the classification performance of imbalanced data. Firstly, we propose a training sample selection strategy based on roulette wheel selection method to make GAN pay more attention to the class overlapping area when fitting the sample distribution. Secondly, we design two kinds of generator training loss, and propose a noise sample filtering method to improve the quality of generated samples. Then, minority class samples are oversampled using the improved RGAN to obtain a balanced training sample set. Finally, combined with the ensemble learning strategy, the final training and prediction are carried out. We conducted experiments on 41 real imbalanced data sets using two evaluation indexes: F1-score and AUC. Specifically, we compare RGAN-EL with six typical ensemble learning; RGAN is compared with three typical GAN models. The experimental results show that RGAN-EL is significantly better than the other six ensemble learning methods, and RGAN is greatly improved compared with three classical GAN models. 

Hongwei Ding

Papers

Sensitive detection of cancer cell apoptosis based on the non-bianisotropic metamaterials biosensors in terahertz frequency

Imbalanced data classification: A KNN and generative adversarial networks-based hybrid approach for intrusion detection

RGAN-EL: A GAN and ensemble learning-based hybrid approach for imbalanced data classification

RVGAN-TL: A generative adversarial networks and transfer learning-based hybrid approach for imbalanced data classification

Effects of biodegradable mulching films on soil hydrothermal conditions and yield of drip-irrigated cotton (Gossypium hirsutum L.)