Hui Chen

To explore the potential of new information transmission windows, this work presents a broadband plasmonic filter based on gold-deposited silicon photonic crystal fiber (PCF) operating in mid-infrared regime numerically, using the finite element method (FEM). The simulation results indicate that the interaction between the high-refractive-index pure silicon material and the gold layer can cause a shift of the resonance central point to the mid-infrared band, which provides the prerequisite for mid-infrared filtering. When the cladding holes’ diameter is 1.3 µm, the inner holes’ diameter is 1.04 µm, the diameter of the holes located on both sides of the core region is 2.08 µm, the gold-coated holes’ diameter is 2.08 µm, the lattice constant is 2 µm, and the gold thickness is 50 nm, this PCF can operate in the mid-infrared band near the central wavelength of 3 µm. The 1 mm long PCF polarizer exhibits a maximum extinction ratio (ER) of −43.5 dB at 3 µm and a broad operating bandwidth of greater than 820 nm with ER better than −20 dB. Additionally, it also possesses high fabrication feasibility. This in-fiber polarization filter, characterized by its comprehensive performance and ease of fabrication, aids in exploring the development potential of high-speed and large-capacity modern communication networks within new optical bands and contributes to new photonic computing and sensing.

Exosomes are critical biomarkers for early cancer diagnosis and prognosis due to their rich biological information.

Lung cancer exhibits strong heterogeneity, and its early diagnosis and precise subtyping are of great importance, as they can increase the ability to deliver personalized medicines by tailoring therapy regimens. Tissue biopsy, albeit the gold standard, is invasive, costly and provides limited information about the tumor and its molecular landscape. Exosomes, as promising biomarkers for lung cancer, are a heterogeneous collection of membranous vesicles containing tumor-specific information for liquid biopsy to identify lung cancer subtypes. However, the small size, complex structure, and heterogeneous molecular features of exosomes pose significant challenges for their effective isolation and analysis. Herein, we report a deep learning-driven microfluidic chip with surface-enhanced Raman scattering (SERS) readout to characterize the differences in exosomes for the early diagnosis and molecular subtyping of non-small cell lung cancer (NSCLC). This integration comprises a processing unit for exosome capture and enrichment using polystyrene microspheres (PS) binding gold nanocubes (AuNCs) and anti-CD-9 antibody (denoted as PACD), and an optical sensing unit to trap the PACD and detect SERS signals from these exosomes. This system achieved a maximum trapping efficiency of 85%, and could distinguish three different NSCLC cell lines from the normal cell line with an overall accuracy of 97.88% and an area under the curve (AUC) of over 0.95 for each category. This work highlights the combined power of deep learning, SERS, and microfluidics in realizing the capture, detection, and analysis of exosomes from biological matrices, which may pave the way for clinical exosome-based cancer diagnosis and prognostication in the future.

Lung cancer with heterogeneity has a high mortality rate due to its late-stage detection and chemotherapy resistance. Liquid biopsy that discriminates tumor-related biomarkers in body fluids has emerged as an attractive technique for early-stage and accurate diagnosis. Exosomes, carrying membrane and cytosolic information from original tumor cells, impart themselves endogeneity and heterogeneity, which offer extensive and unique advantages in the field of liquid biopsy for cancer differential diagnosis. Herein, we demonstrate a Gramian angular summation field and MobileNet V2 (GASF-MobileNet)-assisted surface-enhanced Raman spectroscopy (SERS) technique for analyzing exosomes, aimed at precise diagnosis of lung cancer. Specifically, a composite substrate was synthesized for SERS detection of exosomes based on Ti₃C₂Tx Mxene and the array of gold-silver core-shell nanocubes (MGS), that combines sensitivity and signal stability. The employment of MXene facilitates the non-selective capture and enrichment of exosomes. To overcome the issue of potentially overlooking spatial features in spectral data analysis, 1-D spectra were first transformed into 2-D images through GASF. By using transformed images as the input data, a deep learning model based on the MobileNet V2 framework extracted spectral features from higher dimensions, which identified different non-small cell lung cancer (NSCLC) cell lines with an overall accuracy of 95.23%. Moreover, the area under the curve (AUC) for each category exceeded 0.95, demonstrating the great potential of integrating label-free SERS with deep learning for precise lung cancer differential diagnosis. This approach allows routine cancer management, and meanwhile, its non-specific analysis of SERS signatures is anticipated to be expanded to other cancers.

Since presently Alzheimer's disease (AD) is incurable, early diagnosis of AD is crucial. Aβ 1–42 and tau-441 proteins are promising core biomarkers for early diagnosis and early therapeutic intervention in AD. Here we constructed a surface-enhanced Raman spectroscopy (SERS) biosensor for highly sensitive quantitative detection of Aβ 1–42 and tau proteins by preparing gold nanocube (AuNC) superlattices through evaporation self-assembly. The results showed that the method has a wide response range (0.1–10 000 ng mL⁻¹ and 0.01–1000 ng mL⁻¹, respectively) and high sensitivity. The detection limits of Aβ1–42 and tau protein were 0.0416 ng mL⁻¹ and 0.0087 ng mL⁻¹, respectively. In addition, the method was able to rapidly and simultaneously detect the two biomarkers in serum, which showed the feasibility of the method in complex biological environments. The detection of Aβ 1–42 and tau protein has great potential for the accurate prediction and early diagnosis of Alzheimer's disease.

Kidney disease is highly prevalent and may result in severe clinical outcomes. Serum creatinine (Scr) and blood urea nitrogen (BUN) are the most widely used biomarkers for kidney function assessment, yet when measured alone, the result can be affected by a variety of parameters such as age, gender, protein consumption, etc. Measuring Scr and BUN simultaneously can eliminate most of the external influences and greatly improve the assessment of kidney function. In this study, a real-time kidney function assessment system based on dual biomarker detection was proposed. Scr and BUN were determined using surface-enhanced Raman scattering (SERS) within the concentration range of 10⁻¹ to 10⁻⁶ M and 0.28 to 100 mg dl⁻¹, respectively. A one-dimensional convolutional neural network (1D-CNN) model was employed to quantitatively analyze the concentration of biomarkers from the SERS spectral measurements. Moreover, we simulated a variety of kidney health conditions with 16 groups of mixed Scr and BUN in serum. The proposed CNN-assisted SERS method was used to quantify both biomarkers and provide diagnostic results. The Au core-Ag shell nanoprobes provided ultra-sensitive SERS detection and the CNN model achieved excellent regression results with an R² of 0.9871 in the testing dataset. The system demonstrated a rapid and robust evaluation for the assessment of kidney function, providing a promising idea for medical diagnosis with the help of spectroscopy and deep learning methods.

Chlorpyrifos (CPF) and 2,4-dichlorophenoxyacetic acid (2,4-D) are insecticides and herbicides which has been widely used on farms. However, CPF and 2,4-D residues on corps can bring high risks to human health. Accurate detection of pesticide residues is important for controlling health risks caused by CPF and 2,4-D. Therefore, we developed a fast, sensitive, economical, and lossless surface-enhanced Raman spectroscopy (SERS)-based method for pesticide detection. It can rapidly and simultaneously determine the CPF and 2,4-D mixed pesticide residues on an apple surface at a minimum of 0.001 mg L−1 concentration, which is far below the pesticide residue standard in China and the EU. The limits of detection reach down to 1.28 × 10−9 mol L−1 for CPF and 2.47 × 10−10 mol L−1 for 2,4-D. The limits of quantification are 4.27 × 10−9 mol L−1 and 8.23 × 10−10 mol L−1 for CPF and 2,4-D. This method has a great potential for the accurate detection of pesticide residues, and may be applied to other fields of agricultural products and food industry.