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Wearable sensors for sweat trace metal monitoring have the challenges of effective sweat collection and the real-time recording of detection signals. The existing detection technologies are implemented by generating enough sweat through exercise, which makes detecting trace metals in sweat cumbersome. Generally, it takes around 20 min to obtain enough sweat, resulting in dallied and prolonged detection signals that cannot reflect the endogenous fluctuations of the body. To solve these problems, we prepared a multifunctional hydrogel as an electrolyte and combined it with a flexible patch electrode to realize real-time monitoring of sweat Zn2+. Such hydrogel has magnetic and porous properties, and the porous structure of hydrogel enables a fast absorption of sweat, and the magnetic property of the addition of fabricated Fe3O4 NPs not only improves the conductivity but also ensures the adjustable internal structures of the hydrogel. Such a sensing platform for sweat Zn2+ monitoring shows a satisfied linear relationship in the concentration range of 0.16–16 µg/mL via differential pulsed anodic striping voltammetry (DPASV) and successfully detects the sweat Zn2+ of four volunteers during exercise and resting, displaying a promising path for commercial application. [ABSTRACT FROM AUTHOR]

Aims/Introduction: Diabetic nephropathy (DN) is among the leading causes of end‐stage renal disease worldwide. DN pathogenesis remains largely unknown. Weighted gene co‐expression network analysis is a powerful bioinformatic tool for identifying key genes in diseases. Materials and Methods: The datasets GSE30122, GSE104948, GSE37463 and GSE47185 containing 23 DN and 23 normal glomeruli samples were obtained from the National Center for Biotechnology Information Gene Expression Omnibus database. After data pre‐processing, weighted gene co‐expression network analysis was carried out to cluster significant modules. Then, Gene Set Enrichment Analysis‐based Gene Ontology analysis and visualization of network were carried out to screen the key genes in the most significant modules. The connectivity map analysis was carried out to find the significant chemical compounds. Finally, some key genes were validated in in vivo and in vitro experiments. Results: A total of 454 upregulated and 392 downregulated genes were identified. A total of 16 modules were clustered, and the most significant modules (green, red and yellow modules) were determined. The green module was associated with extracellular matrix organization, the red module was associated with immunity reaction and the yellow module was associated with kidney development. We found several key genes in these three modules separately, and part of them were validated in vivo and in vitro successfully. We found the top 15 chemical compounds that could perturb the overall expression of key genes in DN. Conclusion: Weighted gene co‐expression network analysis was applied to DN expression profiling in combination with connectivity map analysis. Several novel key genes and chemical compounds were screened out, providing new molecular targets for DN. [ABSTRACT FROM AUTHOR]

Background: Membranous nephropathy (MN) is one of the most common pathologies of the nephrotic syndrome. MN is closely associated with the autoimmune response but its molecular mechanism remains unclear. Bioinformatic network analysis can be used to identify disease-related hub genes. Methods: The microarray data set GSE47183 of patients with MN containing 21 MN samples and 13 control samples that were obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified using the limma package. Thereafter, gene ontology (GO) enrichment was performed for DEGs using the clusterProfiler package. The protein–protein interaction (PPI) network was established through the Search Tool for the Retrieval of Interacting Genes database and visualized using Cytoscape. Finally, the hub genes were identified through the maximal clique centrality method. Result: A total of 642 DEGs were recognized, consisting of 458 upregulated genes and 184 downregulated genes. GO enrichment analysis indicates that DEGs for MN are mainly related to antigen processing and presentation. For the PPI network, we identified approximately nine hub genes. Considering data from the literature, we ultimately identified PSMB8 as a novel hub gene, which could play a significant role in the occurrence and development of MN. Conclusion: This study is the first to identify novel hub genes with transcriptome microarray data in MN using bioinformatics. The newly discovered hypothetical hub genes should be functionally tested to determine if they truly play an etiologic role in MN. [ABSTRACT FROM AUTHOR]

Potentiometric aptasensors enhanced by integrating advanced nanomaterials are of particular interest for the detection of multiplex species (e.g., proteins, bacteria, micro-organisms) due to their low cost, ease of operation, and low detection limits. However, potentiometric detection of small ionic species aptasensors is still challenging. This article describes the first example of a label-free G-quadruplex-based potentiometric aptasensing platform for the detection of Pb 2+ . Polyion oligonucleotide-labeled gold nanoparticles (AuNPs-DNA) as probes are modified on Au electrode, providing high-density negative charge on the electrode surface. These signal-amplifying probes can selectively form G-quadruplexes with the presence of Pb 2+ ions and reduce the negative charges on the electrode surface, hence achieving potentiometric detection of Pb 2+ ions with high selectivity. The AuNPs-DNA-based aptasensor shows an acceptable sensitivity over a wide range from 10 -11 to 10 -6  M with a detection limit of 8.5 pM. Furthermore, confirmed by coupled plasma mass spectrometry, the sensing platform is capable of performing effective and accurate detection of Pb 2+ level in real water samples. The presented aptasensor offers a fast, convenient, low-maintenance, and highly sensitive alternative for on-site water pollution detections., (Copyright © 2019 Elsevier B.V. All rights reserved.)

An all-solid-state potentiometric aptasensor array based on a multichannel disposable screen-printed carbon electrode (SPCE) was demonstrated for the simultaneous detection of Hg 2+ , Cd 2+ , and As 3+ by open circuit potential (OCP) technology. The potential of the channel with an internal calibration DNA sequence (IC-DNA) was employed as the internal calibration potential (ICP) to subtract the background signal generated by the detection system, providing a built-in correction methodology. As a result, the developed aptasensor array showed high sensitivity and accuracy for detecting Hg 2+ , Cd 2+ , and As 3+ without mutual interference or interference from other ions. The linear response ranged from 2.5 pM to 2.5 μM, and the detection limits for Hg 2+ , Cd 2+ , and As 3+ were 2.0, 0.62, and 0.17 pM, respectively. Furthermore, the potentiometric aptasensor array was successfully applied for the simultaneous detection of three ions in real samples. The results obtained from the developed approach agreed well with the results obtained from inductively coupled plasma mass spectrometry.

Herein we propose a multiple signal amplification strategy designed for ultrasensitive electrochemical detection of glycoproteins. This approach introduces a new type of boronate-affinity sandwich assay (BASA), which was fabricated by using gold nanoparticles combined with reduced graphene oxide (AuNPs-GO) to modify sensing surface for accelerating electron transfer, the composite of molecularly imprinted polymer (MIP) including 4-vinylphenylboronic acid (VPBA) for specific capturing glycoproteins, and SiO 2 nanoparticles carried gold nanoparticles (SiO 2 @Au) labeled with 6-ferrocenylhexanethiol (FcHT) and 4-mercaptophenylboronic acid (MPBA) (SiO 2 @Au/FcHT/MPBA) as tracing tag for binding glycoprotein and generating electrochemical signal. As a sandwich-type sensing, the SiO 2 @Au/FcHT/MPBA was captured by glycoprotein on the surface of imprinting film for further electrochemical detection in 0.1 M PBS (pH 7.4). Using horseradish peroxidase (HRP) as a model glycoprotein, the proposed approach exhibited a wide linear range from 1 pg/mL to 100 ng/mL, with a low detection limit of 0.57 pg/mL. To the best of our knowledge, this is first report of a multiple signal amplification approach based on boronate-affinity molecularly imprinted polymer and SiO 2 @Au/FcHT/MPBA, exhibiting greatly enhanced sensitivity for glycoprotein detection. Furthermore, the newly constructed BASA based glycoprotein sensor demonstrated HRP detection in real sample, such as human serum, suggesting its promising prospects in clinical diagnostics.