As a kind of most important cancer biomarker, exosomes are getting more frequently investigated in cancer diagnosis. In this study, we proposed an SERS-based method for the screening and simultaneous multiple detection of exosomes using magnetic substrates and SERS probes. Specifically, the capturing substrates are achieved using gold shell magnetic nanobeads modified by aptamers, which can capture most kinds of exosomes by recognizing the generic surface protein CD63. Moreover, the SERS probes are made of gold nanoparticles decorated with a Raman reporter and a specific aptamer for targeting exosomes. Further, for the simultaneous detection of multiple kinds of exosomes, three kinds of SERS probes were designed using different SERS reporters. While detecting specific kinds of exosomes, the capturing substrates were mixed with these three kinds of SERS probes. When the target exosome is present, an apta-immunocomplex can be formed among the target exosomes, the substrate, and the corresponding kind of SERS probes, and the other non-specific SERS probes remain in the suspension. Hence, an SERS signal with a decreased intensity will be detected in the supernatant, indicating the presence of the target exosomes. Finally, this detection method has also been successfully employed for the detection of exosomes in real blood samples; this proves that the proposed SERS-based method is a promising tool for clinical cancer screening based on exosomes.

A surface-enhanced Raman scattering (SERS) aptasensor based on a hydrophobic assembled nanoacorn (HANA) was developed with improved reproducibility and reduced nonspecific binding effect. In the fabrication process, a hexagonal-packed gold film over nanosphere (AuFON) arrays was first obtained and used as a hydrophobic plasmonic substrate. Then, a uniform sub-3 nm molecular spacer array (containing Raman reporters) was prepared by patterning nanometric hydrophilic ultrathin patches onto the hydrophobic AuFON, in which the hydrophilic thin layer is composed of polymers and aptamers. During the sensing process, the HANA aptasensor smartly impedes the adsorption of SERS probes as Au@Ag nanocubes (Au@Ag NCs) in the absence of targets. In the presence of targets, the displacement of aptamers occurs due to the specific interaction between the targets and the aptamers, and the Au@Ag NCs can be assembled onto the hydrophilic patches on AuFON through electrostatic interactions with polymers. Thus, SERS signals of reporter molecules inside the spacer can be dramatically enhanced due to the formation of a nanoparticle-on-mirror (NPoM) array. In such a SERS aptasensor, the well-ordered distribution of SERS probes ensures excellent repeatability, while the precise subnanometer junctions guarantee high sensitivity. More importantly, since the hydrophobic surface can greatly reduce nonspecific adsorption, the tedious process of nonspecific blocking that is employed in traditional biosensors is no longer needed. Using such a SERS HANA platform, human epidermal growth factor receptor 2 (HER2) and three exosomal proteins were analyzed with high sensitivity and good reproducibility (RSD < 7%) in whole-blood samples.

The interfacial fluid thickness (IFT) concept was used to develop a harmonic-mean refractive index gradient magnitude threshold to retrieve the high refractive index gradient regions of an aerodynamically heated window. The retrieved high-gradient regions were used to reconstruct the refractive index field of the window. The numerical three-dimensional optical distortion evaluation was conducted for both the reconstructed and the original refractive index fields of the window using the ray-tracing program based on a recursive algorithm. Wave aberration results show that the methodology based on the IFT concept reduces the refractive index information required to capture the essential optical distortion of the window. The method can also be used for numerically evaluating the optical distortion of the window.

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Background: Lymph node metastasis (LNM) is difficult to precisely predict before surgery in patients with early-T-stage non-small cell lung cancer (NSCLC). This study aimed to develop machine learning (ML)-based predictive models for LNM. Methods: Clinical characteristics and imaging features were retrospectively collected from 1,102 NSCLC ≤ 2 cm patients. A total of 23 variables were included to develop predictive models for LNM by multiple ML algorithms. The models were evaluated by the receiver operating characteristic (ROC) curve for predictive performance and decision curve analysis (DCA) for clinical values. A feature selection approach was used to identify optimal predictive factors. Results: The areas under the ROC curve (AUCs) of the 8 models ranged from 0.784 to 0.899. Some ML-based models performed better than models using conventional statistical methods in both ROC curves and decision curves. The random forest classifier (RFC) model with 9 variables introduced was identified as the best predictive model. The feature selection indicated the top five predictors were tumor size, imaging density, carcinoembryonic antigen (CEA), maximal standardized uptake value (SUV max), and age. Conclusions: By incorporating clinical characteristics and radiographical features, it is feasible to develop ML-based models for the preoperative prediction of LNM in early-T-stage NSCLC, and the RFC model performed best.

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