The lateral flow immunoassay (LFIA) has emerged as a powerful tool for rapid screening owing to its simplicity and flexibility for detection of various biomarkers. However, conventional LFIA strips have several disadvantages, including limits in quantitative analysis and low sensitivity. Here we developed a novel surface-enhanced Raman scattering LFIA based on nonspherical gap-enhanced Raman tags (GERTs), with Raman molecules (RMs) embedded in a 1-nm gap between Au nanorod core and Au shell. Such tags have a strong and uniform SERS response, an order of magnitude higher than that of other common SERS tags such as Au nanorods, nanostars, Au nanoshells with surface-adsorbed RMs, or spherical GERTs with embedded RMs. The feasibility of the tags was demonstrated by the semiquantitative and sensitive detection of the heart disease biomarker cardiac troponin I (cTnI). GERTs were conjugated with monoclonal antibodies and used for LFIA in the same way as ordinary functionalized colloidal gold. The presence of the target antigen, cTnI, was identified by Raman microscopy mapping of the test zone. With the SERS-based LFIA, the limit of cTnI detection was about 0.1 ng/mL. This value is within the diagnostic range of cTnI in the blood serum of patients with heart infarction and is 30 times lower than that of the colorimetric LFIA test using the same antibodies and either GERTs or colloidal gold as labels. Open image in new window

We demonstrate a novel approach to the controlled loading of inorganic nanoparticles and proteins into submicron- and micron-sized porous particles. The approach is based on freezing/thawing cycles, which lead to high loading densities. The process was tested for the inclusion of Au, magnetite nanoparticles, and bovine serum albumin in biocompatible vaterite carriers of micron and submicron sizes. The amounts of loaded nanoparticles or substances were adjusted by the number of freezing/thawing cycles. Our method afforded at least a three times higher loading of magnetite nanoparticles and a four times higher loading of protein for micron vaterite particles, in comparison with conventional methods such as adsorption and coprecipitation. The capsules loaded with magnetite nanoparticles by the freezing-induced loading method moved faster in a magnetic field gradient than did the capsules loaded by adsorption or coprecipitation. Our approach allows the preparation of multicomponent nanocomposite materials with designed properties such as remote control (e.g. via the application of an electromagnetic or acoustic field) and cargo unloading. Such materials could be used as multimodal contrast agents, drug delivery systems, and sensors.

Flow cytometry nowadays is among the main working instruments in modern biology paving the way for clinics to provide early, quick, and reliable diagnostics of many blood-related diseases. The major problem for clinical applications is the detection of rare pathogenic objects in patient blood. These objects can be circulating tumor cells, very rare during the early stages of cancer development, various microorganisms and parasites in the blood during acute blood infections. All of these rare diagnostic objects can be detected and identified very rapidly to save a patient’s life. This review outlines the main techniques of visualization of rare objects in the blood flow, methods for extraction of such objects from the blood flow for further investigations and new approaches to identify the objects automatically with the modern deep learning methods.

In modern digital microscopy, deconvolution methods are widely used to eliminate a number of image defects and increase resolution. In this review, we have divided these methods into classical, deep learning-based, and optimization-based methods. The review describes the major architectures of neural networks, such as convolutional and generative adversarial networks, autoencoders, various forms of recurrent networks, and the attention mechanism used for the deconvolution problem. Special attention is paid to deep learning as the most powerful and flexible modern approach. The review describes the major architectures of neural networks used for the deconvolution problem. We describe the difficulties in their application, such as the discrepancy between the standard loss functions and the visual content and the heterogeneity of the images. Next, we examine how to deal with this by introducing new loss functions, multiscale learning, and prior knowledge of visual content. In conclusion, a review of promising directions and further development of deconvolution methods in microscopy is given.