Analytical protein microarrays offering highly parallel analysis can become an invaluable tool for a wide range of immunodiagnostic applications. Here we describe factors that influence the sensitivity of a competitive immunomicroarray that quantifies small molecules; in this case, the pesticides dichlobenil metabolite 2,6-dichlorobenzamide (BAM) and atrazine. Free pesticide concentrations in solution are quantified by the competitive binding of fluorescence-conjugated monoclonal antibodies to either surface-immobilized pesticide hapten-protein conjugates or pesticides in solution. We investigated the influence of antibody labeling techniques, microarray substrates, and spotting and incubation buffers. The results showed that microarrays immobilized on EasySpot or in-house fabricated agarose substrates printed with Genetix Amine Spotting Solution resulted in optimum results when the arrays were incubated with the sample/antibodies diluted in a Tris buffer supplemented with 0.05% each bovine serum albumin (BSA) and Tween 20. Furthermore, the application of directly labeled primary antibodies allowed for better sensitivity compared to secondary polyclonal antibody quantification.
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PLoS One
January 2025
School of Electronics Engineering (SENSE), Vellore Institute of Technology, Vellore, Tamil Nadu, India.
In recent years, the utilization of motor imagery (MI) signals derived from electroencephalography (EEG) has shown promising applications in controlling various devices such as wheelchairs, assistive technologies, and driverless vehicles. However, decoding EEG signals poses significant challenges due to their complexity, dynamic nature, and low signal-to-noise ratio (SNR). Traditional EEG pattern recognition algorithms typically involve two key steps: feature extraction and feature classification, both crucial for accurate operation.
View Article and Find Full Text PDFJ Sports Med Phys Fitness
January 2025
ASD Luiss SportLab, Rome, Italy.
Background: Assessing player readiness is crucial in elite basketball. This study aims to provide a practical method for monitoring player readiness through the handgrip test and identify associations with wellness scales.
Methods: Fifteen players (age: 25.
Anal Chem
January 2025
Key Laboratory of Green Chemistry & Technology of Ministry of Education, College of Chemistry, Sichuan University, Chengdu 610064, Sichuan, China.
Isothermal nucleic acid amplification techniques are promising alternatives to polymerase chain reaction (PCR) for amplifying and detecting nucleic acids under resource-limited conditions. While many isothermal amplification strategies, such as recombinase polymerase amplification (RPA), offer comparable sensitivity to PCR, they often lack the specificity and robustness for discriminating single nucleotide variants (SNVs), mainly due to the uncontrolled production of massive amplicons. Herein, we introduce a mismatch-guided DNA assembly (MGDA) approach capable of discriminating SNVs in the presence of high concentrations of wild-type (WT) interferences.
View Article and Find Full Text PDFAn ultrasensitive refractive index (RI) sensing technology based on an enhanced Vernier effect is proposed, which integrates a polymer Fabry-Perot interferometer (FPI) with an open cavity FPI on the tip of a seven-core optical fiber. Interference spectra of the polymer FPI and the open cavity FPI shift to opposite directions as the ambient RI changes, thus leading to the enhanced Vernier effect. Investigations of RI sensitivity and temperature dependence of the proposed fiber sensors are carried out.
View Article and Find Full Text PDFSci Rep
January 2025
School of Computer Science, Hunan First Normal University, Changsha, 410205, China.
Retinal blood vessels are the only blood vessels in the human body that can be observed non-invasively. Changes in vessel morphology are closely associated with hypertension, diabetes, cardiovascular disease and other systemic diseases, and computers can help doctors identify these changes by automatically segmenting blood vessels in fundus images. If we train a highly accurate segmentation model on one dataset (source domain) and apply it to another dataset (target domain) with a different data distribution, the segmentation accuracy will drop sharply, which is called the domain shift problem.
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