Dorseysargent7905

Affordable point-of-care (PoC) diagnostic devices enable detection of prostate specific antigen (PSA) in resource limited settings. Despite the advancements in PoC systems, most of the reported methods for PSA detection have unsatisfactory detection limits and are based on labelled assays, requiring multiple reagent flow steps which increases both expenses and inconvenience. Circumventing these constraints, we report here the development and validation of a label free, affordable dielectrophoresis (DEP) based graphene field effect transistor (FET) sensor implemented using coplanar electrodes and integrated uniquely with a compact disc based microfluidic platform along with electronics readout for the estimation of PSA at the point of care. Design of coplanar gate electrode which has not been explored earlier is not a straightforward approach. In fact, it has been observed that there is a non-monotonic dependence of the capture of PSA molecules in the channel region of the FET with varying widths and spacings of the gate electrode. The graphene FET based PoC device with optimized coplanar gate electrode is the only label free analytical system for PSA detection requiring simple operation and achieving a detection limit of 1 pg/ml in serum with a wide dynamic range upto 4 ng/ml and appreciable selectivity against potential interferents like bovine serum albumin (BSA) and human immunoglobulin G (IgG). Further, it has been validated satisfactorily with commercially available existing systems using human serum samples. Moreover, the proposed sensing system lowers the detection limit by three orders of magnitude compared to a recent study on label free PoC device on other cancer biomarkers.Feature detection is a crucial pre-processing step for high-resolution liquid chromatography-mass spectrometry (LC-MS) data analysis. Typical practices based on thresholds or rigid mathematical assumptions can cause ineffective performance in detecting low abundance and non-ideal distributed compounds. We herein introduce a novel feature detection method based on deep learning named SeA-M2Net that considers feature detection as an image-based object detection task. By fully employing raw data directly, and integrating all related factors (e.g., LC elution, charge state, and isotope distribution) with two-dimensional pseudo color images to calculate the probability of the presence of the compound, low abundance compounds can be well preserved and observed. More importantly, SeA-M2Net, with deep multilevel and multiscale structures focuses on compound pattern detection in a learned method instead of assuming a mathematical parametric model. All parameters in SeA-M2Net are learned from data in the training procedure, thus allowing for maximum flexibility of pattern distribution deformation. The algorithm is tested on several LC-MS datasets of multiple biological samples obtained from different instruments with varied experimental settings. We demonstrate the superiority of the new approach in handling complex compound patterns (e.g., low abundance, overlapping regions, LC shifts, and missing values). Our experiments indicate that SeA-M2Net outperforms widely used detection methods in terms of detection accuracy.In the present study, a novel self-enhanced electrochemiluminescence (ECL) aptasensor which combined self-enhanced ECL composite as signal response element and aptamer as the specific recognition element was firstly proposed for the sensitive and selective detection of Hg2+. Innovatively, the luminophore Ru(bpy)32+-doped silica nanoparticles functionalized with 3-aminopropyltriethoxysilane (NH2-Ru@SiO2) and the co-reactant nitrogen doped graphene quantum dots (NGQDs) were bound together to form the self-enhanced ECL composite, NH2-Ru@SiO2-NGQDs, by electrostatic adsorption. Satisfactorily, high and stable luminous efficiency of NH2-Ru@SiO2-NGQDs was obtained benefited from the short electron transfer distance and low energy loss of self-enhanced-typal ECL composite. learn more For the fabrication of the self-enhanced ECL aptasensor for Hg2+ detection, the Hg2+ aptamer, of which one end was connected to NH2-Ru@SiO2-NGQDs, and the other end was fixed on glass carbon electrode (GCE) surface through Au-S bond, was served as a bridge. Upon the addition of Hg2+, the aptamer was bent due to the formation of thymine-Hg2+-thymine (T-Hg2+-T) specific structure, which caused the self-enhanced ECL composite was close to the GCE surface. On this basis, a linearly enhanced ECL signal was acquired with the concentration of Hg2+ in the range of 5.0 × 10-11 M - 1.0 × 10-6 M with excellent selectivity, repeatability and stability in 3 min for each assay. In addition, the proposed aptasensor showed satisfying accuracy and practicability for Hg2+ analysis in tap and canal water verified by the inductively coupled plasma-mass spectrometry (ICP-MS) method.This work reports the development of an electrical and optical biosensing for label-free detection of Aflatoxin B1 (AFB1) using gold (Au) nanobipyramids (NBPs). AuNBPs were synthesized through a two-step seed-mediated growth process followed by an exchange of capping agent from surfactant to lipoic acid. Pure and monodispersed AuNBPs of 70 nm base length were obtained and deposited on indium tin oxide (ITO)-coated glass substrate modified with self-assembled (3-Aminopropyl) triethoxysilane (APTES) film. The characterization of the obtained surfaces using spectroscopy, microscopy and diffractometry confirms the formation of AuNBPs, the conjugation to ITO electrode substrate and the immobilization of anti-AFB1 antibodies. AuNBPs modified ITO substrates were used for both electrochemical and Surface Plasmon Resonance biosensing studies. Localized Surface Plasmon Resonance (LSPR) local field enhancement was demonstrated. SPR based AFB1 detection was found to be linear in the 0.1-500 nM range with a limit of detection of 0.4 nM, whereas, impedimetric AFB1 detection was shown to be linear in the 0.1-25 nM range with a limit of detection of 0.1 nM. The practical utility of the impedimetric sensor was tested in spiked maize samples and 95-100% recovery percentage was found together with low relative standard deviation, proof of the robustness of this AFB1 sensor.