Au–Ag assembled on silica nanoprobes for visual semiquantitative detection of prostate-specific antigen

Background Blood prostate-specific antigen (PSA) levels are widely used as diagnostic biomarkers for prostate cancer. Lateral-flow immunoassay (LFIA)-based PSA detection can overcome the limitations associated with other methods. LFIAbased PSA detection in clinical samples enables prognosis and early diagnosis owing to the use of high-performance signal reporters. Results Here, a semiquantitative LFIA platform for PSA detection in blood was developed using Au–Ag nanoparticles (NPs) assembled on silica NPs (SiO2@Au–Ag NPs) that served as signal reporters. Synthesized SiO2@Au–Ag NPs exhibited a high absorbance at a wide wavelength range (400–800 nm), with a high scattering on nitrocellulose membrane test strips. In LFIA, the color intensity of the test line on the test strip differed depending on the PSA concentration (0.30–10.00 ng/mL), and bands for the test line on the test strip could be used as a standard. When clinical samples were assessed using this LFIA, a visual test line with particular color intensity observed on the test strip enabled the early diagnosis and prognosis of patients with prostate cancer based on PSA detection. In addition, the relative standard deviation of reproducibility was 1.41%, indicating high reproducibility, and the signal reporter showed good stability for 10 days. Conclusion These characteristics of the signal reporter demonstrated the reliability of the LFIA platform for PSA detection, suggesting potential applications in clinical sample analysis.This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF2020R1F1A1072702). This study was also supported by the WTU Joint Research Grant of Konkuk University in 2017 (2017-A019-0334)

Tcf7l2 plays crucial roles in forebrain development through regulation of thalamic and habenular neuron identity and connectivity

The thalamus acts as a central integrator for processing and relaying sensory and motor information to and from the cerebral cortex, and the habenula plays pivotal roles in emotive decision making by modulating dopaminergic and serotonergic circuits. These neural compartments are derived from a common developmental progenitor domain, called prosomere 2, in the caudal forebrain. Thalamic and habenular neurons exhibit distinct molecular profile, neurochemical identity, and axonal circuitry. However, the mechanisms of how their progenitors in prosomere 2 give rise to these two populations of neurons and contribute to the forebrain circuitry remains unclear. In this study, we discovered a previously unrecognized role for Tcf7l2, a transcription factor known as the canonical Wnt nuclear effector and diabetes risk-conferring gene, in establishing neuronal identity and circuits of the caudal forebrain. Using genetic and chemical axon tracers, we showed that efferent axons of the thalamus, known as the thalamocortical axons (TCAs), failed to elongate normally and strayed from their normal course to inappropriate locations in the absence of Tcf7l2. Further experiments with thalamic explants revealed that the pathfinding defects of Tcf7l2-deficient TCAs were associated at least in part with downregulation of guidance receptors Robo1 and Robo2 expression. Moreover, the fasciculus retroflexus, the main habenular output tract, was missing in embryos lacking Tcf7l2. These axonal defects may result from dysregulation of Nrp2 guidance receptor. Strikingly, loss of Tcf7l2 caused a post-mitotic identity switch between thalamic and habenular neurons. Despite normal acquisition of progenitor identity in prosomere 2, Tcf7l2-deficient thalamic neurons adopted a molecular profile of a neighboring forebrain derivative, the habenula. Conversely, habenular neurons failed to maintain their normal post-mitotic neuronal identity and acquired a subset of thalamic neuronal features in the absence of Tcf7l2. Our findings suggest a unique role for Tcf7l2 in generating distinct neuronal phenotypes from homogeneous progenitor population, and provide a better understanding of the mechanism underlying neuronal specification, differentiation, and connectivity of the developing caudal forebrain

Attention-based solubility prediction of polysulfide and electrolyte analysis for lithium–sulfur batteries

Abstract During the continuous charge and discharge process in lithium-sulfur batteries, one of the next-generation batteries, polysulfides are generated in the battery’s electrolyte, and impact its performance in terms of power and capacity by involving the process. The amount of polysulfides in the electrolyte could be estimated by the change of the Gibbs free energy of the electrolyte, $$\Delta _{mix}\textrm{G}$$ Δ mix G in the presence of polysulfide. However, obtaining $$\Delta _{mix}\textrm{G}$$ Δ mix G of the diverse mixtures of components in the electrolyte is a complex and expensive task that shows itself as a bottleneck in optimization of electrolytes. In this work, we present a machine-learning approach for predicting $$\Delta _{mix}\textrm{G}$$ Δ mix G of electrolytes. The proposed architecture utilizes (1) an attention-based model (Attentive FP), a contrastive learning model (MolCLR) or morgan fingerprints to represent chemical components, and (2) transformers to account for the interactions between chemicals in the electrolyte. This architecture was not only capable of predicting electrolyte properties, including those of chemicals not used during training, but also providing insights into chemical interactions within electrolytes. It revealed that interactions with other chemicals relate to the logP and molecular weight of the chemicals

Highly Bright Silica-Coated InP/ZnS Quantum Dot-Embedded Silica Nanoparticles as Biocompatible Nanoprobes

Quantum dots (QDs) have outstanding optical properties such as strong fluorescence, excellent photostability, broad absorption spectra, and narrow emission bands, which make them useful for bioimaging. However, cadmium (Cd)-based QDs, which have been widely studied, have potential toxicity problems. Cd-free QDs have also been studied, but their weak photoluminescence (PL) intensity makes their practical use in bioimaging challenging. In this study, Cd-free QD nanoprobes for bioimaging were fabricated by densely embedding multiple indium phosphide/zinc sulfide (InP/ZnS) QDs onto silica templates and coating them with a silica shell. The fabricated silica-coated InP/ZnS QD-embedded silica nanoparticles (SiO2@InP QDs@SiO2 NPs) exhibited hydrophilic properties because of the surface silica shell. The quantum yield (QY), maximum emission peak wavelength, and full-width half-maximum (FWHM) of the final fabricated SiO2@InP QDs@SiO2 NPs were 6.61%, 527.01 nm, and 44.62 nm, respectively. Moreover, the brightness of the particles could be easily controlled by adjusting the amount of InP/ZnS QDs in the SiO2@InP QDs@SiO2 NPs. When SiO2@InP QDs@SiO2 NPs were administered to tumor syngeneic mice, the fluorescence signal was prominently detected in the tumor because of the preferential distribution of the SiO2@InP QDs@SiO2 NPs, demonstrating their applicability in bioimaging with NPs. Thus, SiO2@InP QDs@SiO2 NPs have the potential to successfully replace Cd-based QDs as highly bright and biocompatible fluorescent nanoprobes

Lateral Flow Immunoassay with Quantum-Dot-Embedded Silica Nanoparticles for Prostate-Specific Antigen Detection

Prostate cancer can be detected early by testing the presence of prostate-specific antigen (PSA) in the blood. Lateral flow immunoassay (LFIA) has been used because it is cost effective and easy to use and also has a rapid sample-to-answer process. Quantum dots (QDs) with very bright fluorescence have been previously used to improve the detection sensitivity of LFIAs. In the current study, a highly sensitive LFIA kit was devised using QD-embedded silica nanoparticles. In the present study, only a smartphone and a computer software program, ImageJ, were used, because the developed system had high sensitivity by using very bright nanoprobes. The limit of PSA detection of the developed LFIA system was 0.138 ng/mL. The area under the curve of this system was calculated as 0.852. The system did not show any false-negative result when 47 human serum samples were analyzed; it only detected PSA and did not detect alpha-fetoprotein and newborn calf serum in the samples. Additionally, fluorescence was maintained on the strip for 10 d after the test. With its high sensitivity and convenience, the devised LFIA kit can be used for the diagnosis of prostate cancer

Silver-Assembled Silica Nanoparticles in Lateral Flow Immunoassay for Visual Inspection of Prostate-Specific Antigen

Prostate-specific antigen (PSA) is the best-known biomarker for early diagnosis of prostate cancer. For prostate cancer in particular, the threshold level of PSA <4.0 ng/mL in clinical samples is an important indicator. Quick and easy visual detection of the PSA level greatly helps in early detection and treatment of prostate cancer and reducing mortality. In this study, we developed optimized silica-coated silver-assembled silica nanoparticles (SiO2@Ag@SiO2 NPs) that were applied to a visual lateral flow immunoassay (LFIA) platform for PSA detection. During synthesis, the ratio of silica NPs to silver nitrate changed, and as the synthesized NPs exhibited distinct UV spectra and colors, most optimized SiO2@Ag@SiO2 NPs showed the potential for early prostate cancer diagnosis. The PSA detection limit of our LFIA platform was 1.1 ng/mL. By applying each SiO2@Ag@SiO2 NP to the visual LFIA platform, optimized SiO2@Ag@SiO2 NPs were selected in the test strip, and clinical samples from prostate cancer patients were successfully detected as the boundaries of non-specific binding were clearly seen and the level of PSA was <4 ng/mL, thus providing an avenue for quick prostate cancer diagnosis and early treatment