Quenching Behavior of the Electrochemiluminescence of Ru(bpy)32+ /TPrA System by Phenols on a Smartphone-Based Sensor.

Phenolic compounds such as vanillic and p-coumaric acids are pollutants of major concern in the agro-industrial processing, thereby their effective detection in the industrial environment is essential to reduce exposure. Herein, we present the quenching effect of these compounds on the electrochemiluminescence (ECL) of the Ru(bpy)32+ /TPrA (TPrA=tri-n-propylamine) system at a disposable screen-printed carbon electrode. Transient ECL profiles are obtained from multiple video frames following 1.2 V application by a smartphone-based ECL sensor. A wide range of detection was achieved using the sensor with limit of detection of 0.26 µM and 0.68 µM for vanillic and p-coumaric acids, respectively. The estimated quenching constants determined that the quenching efficiency of vanillic acid is at least two-fold that of p-coumaric acid under the current detection conditions. The present ECL quenching approach provided an effective method to detect phenolic compounds using a low-cost, portable smartphone-based ECL sensor.

Electrochemiluminescence Mechanisms Investigated with Smartphone-Based Sensor Data Modeling, Parameter Estimation and Sensitivity Analysis.

The present study introduces a unified framework combining a mechanistic model with a genetic algorithm (GA) for the parameter estimation of electrochemiluminescence (ECL) kinetics of the Ru(bpy)32+/TPrA system occurring in a smartphone-based sensor. The framework allows a straightforward solution for simultaneous estimation of multiple parameters which can be, otherwise, time-consuming and lead to non-convergence. Model parameters are estimated by achieving a high correlation between the model prediction and the measured ECL intensity from the ECL sensor. The developed model is used to perform a sensitivity analysis (SA), which provides quantitative effects of the model parameters on the concentrations of chemical species involved in the system. The results demonstrate that the GA-based parameter estimation and the SA approaches are effective in analyzing the kinetics of the ECL mechanism. Therefore, these approaches can be incorporated as analysis tools in the ECL kinetics study with practical application in the calibration of mechanistic models for any required sensing condition.

Data-Driven Modeling of Smartphone-Based Electrochemiluminescence Sensor Data Using Artificial Intelligence.

Understanding relationships among multimodal data extracted from a smartphone-based electrochemiluminescence (ECL) sensor is crucial for the development of low-cost point-of-care diagnostic devices. In this work, artificial intelligence (AI) algorithms such as random forest (RF) and feedforward neural network (FNN) are used to quantitatively investigate the relationships between the concentration of Ru(bpy)32+ luminophore and its experimentally measured ECL and electrochemical data. A smartphone-based ECL sensor with Ru(bpy)32+/TPrA was developed using disposable screen-printed carbon electrodes. ECL images and amperograms were simultaneously obtained following 1.2-V voltage application. These multimodal data were analyzed by RF and FNN algorithms, which allowed the prediction of Ru(bpy)32+ concentration using multiple key features. High correlation (0.99 and 0.96 for RF and FNN, respectively) between actual and predicted values was achieved in the detection range between 0.02 µM and 2.5 µM. The AI approaches using RF and FNN were capable of directly inferring the concentration of Ru(bpy)32+ using easily observable key features. The results demonstrate that data-driven AI algorithms are effective in analyzing the multimodal ECL sensor data. Therefore, these AI algorithms can be an essential part of the modeling arsenal with successful application in ECL sensor data modeling.

Ca extrusion by NCX is compromised in olfactory sensory neurons of OMP mice.

BACKGROUND: The role of olfactory marker protein (OMP), a hallmark of mature olfactory sensory neurons (OSNs), has been poorly understood since its discovery. The electrophysiological and behavioral phenotypes of OMP knockout mice indicated that OMP influences olfactory signal transduction. However, the mechanism by which this occurs remained unknown. PRINCIPAL FINDINGS: We used intact olfactory epithelium obtained from WT and OMP(-/-) mice to monitor the Ca(2+) dynamics induced by the activation of cyclic nucleotide-gated channels, voltage-operated Ca(2+) channels, or Ca(2+) stores in single dendritic knobs of OSNs. Our data suggested that OMP could act to modulate the Ca(2+)-homeostasis in these neurons by influencing the activity of the plasma membrane Na(+)/Ca(2+)-exchanger (NCX). Immunohistochemistry verifies colocalization of NCX1 and OMP in the cilia and knobs of OSNs. To test the role of NCX activity, we compared the kinetics of Ca(2+) elevation by stimulating the reverse mode of NCX in both WT and OMP(-/-) mice. The resulting Ca(2+) responses indicate that OMP facilitates NCX activity and allows rapid Ca(2+) extrusion from OSN knobs. To address the mechanism by which OMP influences NCX activity in OSNs we studied protein-peptide interactions in real-time using surface plasmon resonance technology. We demonstrate the direct interaction of the XIP regulatory-peptide of NCX with calmodulin (CaM). CONCLUSIONS: Since CaM also binds to the Bex protein, an interacting protein partner of OMP, these observations strongly suggest that OMP can influence CaM efficacy and thus alters NCX activity by a series of protein-protein interactions.

Theoretical and experimental analysis of analyte transport in a fiber-optic, protein C immuno-biosensor.

Protein C (PC) is an important anticoagulant in human blood plasma, and early diagnosis of PC deficiency is critical for preventing dangerous thromboembolic complications. A fiber-optic PC immuno-biosensor has been under development in our research group for real-time PC-deficiency diagnosis. The sensor has demonstrated a good sensitivity and specificity for quantifying PC in buffered solutions. However, for plasma samples, with a limited sample reaction time, the sensor produced only 30% of the signal intensity of PC in buffer. The high plasma viscosity (1.9 cP) was speculated as the major reason for signal intensity reduction. In this investigation, the sensing performance of the fiber-optic PC biosensor is systematically characterized in terms of physical and chemical properties of the sample media. Theoretical and experimental analyses indicate that the reduced diffusion rate of PC molecules in viscous samples caused the sensing system to be more mass-transfer-limited. Convective flow of sample/reagent solutions during immunoreactions can increase the rate of the analyte mass transport from the bulk solution to the sensor surface, with reaction kinetics changing from mass-transfer-limited to reaction-limited as flow velocity increases. It was shown that PC sensor performance was significantly improved for plasma samples with convection. The effect of the flow velocity and incubation times for samples and reagents on the sensor performance was also systematically analyzed to optimize the assay protocol for PC sensing. Currently, a 6-cm-long immuno-biosensor is capable of quantifying PC in plasma (1 mL) in the heterozygous PC deficiency range (0.5 to 2.5 microg/mL) within 5 minutes, at an average signal-to-noise ratio of 50.

Sensing performance of protein C immuno-biosensor for biological samples and sensor minimization.

Protein C (PC) is an important anticoagulant and antithrombotic agent in human blood plasma. PC deficiency can result in clotting complications that interfere with oxygen and nutrient transport. A fiber-optic biosensor is being developed to provide real time diagnosis of PC deficiency. The PC sensor was tested to quantify PC level in human plasma. The signal intensity obtained from the plasma sample was 30% of the buffered sample, possibly due to the increased viscosity. The feasibility of monitoring PC level in animal cell culture broth and animal milk was tested. For the cell culture broth, 80% of signal was observed. However, the decrease was consistent over the sensing range. For whole and 1:100 diluted bovine milk, the signals were 60 and 78% of buffered sample, respectively. The biosensor length was reduced from 12.5 to 6 cm with sufficient sensitivity. To increase the sensor reusability, various elution buffers were applied after each sensing. Triethylamine elution buffer provided the best sensor regeneration capability and increased the number of assays from 2.5 to 7 times for 6 cm fibers.

Assay procedure optimization of a rapid, reusable protein C immunosensor for physiological samples.

A fiber-optic immunosensor for quantifying the protein C (PC) amount in a plasma sample is being developed to provide accurate, fast, cost-effective diagnosis of heterozygous PC deficiency (0.5-2.5 microg ml(-1)). As a progress report on the sensor development, this paper focuses on optimizations of vanous assay steps. These include: (1) the primary antibody concentration; (2) the effect of the primary antibody leaching on the sensitivity; (3) the fluorophore to secondary antibody ratio; and (4) the sample and secondary antibody incubation times. The optimal primary antibody concentration for the PC sensor was determined to be 65 microg ml(-1); its leaching was minor, stabilized within 3 days, and the sensor sensitivity change after 30 days of storage was minor; sample and secondary antibody incubation times were reduced from 10 to 5 and from 5 to 3 min, respectively, while maintaining a reasonable signal-to-noise level. The immunosensor was also tested with (5) human serum albumin and human plasma and (6) with and without convective flow. High viscosity of human plasma decreased signal intensity, but clear signal discrimination was possible in the concentration range of interest. (7) Application of convective flow increased the signal intensity by increasing PC mass transport rate to the sensor surface. The optimized PC immunosensor provides a smaller (100 microl sample chamber) and faster (10-15 min) tool for PC detection in human plasma.