scholarly journals Tunable Detection Sensitivity of Opiates in Urine via a Label-Free Porous Silicon Competitive Inhibition Immunosensor

2010 ◽  
Vol 82 (2) ◽  
pp. 714-722 ◽  
Author(s):  
Lisa M. Bonanno ◽  
Lisa A. DeLouise
2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rozita Abolhasan ◽  
Balal Khalilzadeh ◽  
Hadi Yousefi ◽  
Sahar Samemaleki ◽  
Forough Chakari-Khiavi ◽  
...  

AbstractIn the present article, we developed a highly sensitive label-free electrochemical immunosensor based on NiFe-layered double hydroxides (LDH)/reduced graphene oxide (rGO)/gold nanoparticles modified glassy carbon electrode for the determination of receptor tyrosine kinase-like orphan receptor (ROR)-1. In this electrochemical immunoassay platform, NiFe-LDH/rGO was used due to great electron mobility, high specific surface area and flexible structures, while Au nanoparticles were prepared and coated on the modified electrodes to improve the detection sensitivity and ROR1 antibody immobilizing (ROR1Ab). The modification procedure was approved by using cyclic voltammetry and differential pulse voltammetry based on the response of peak current to the step by step modifications. Under optimum conditions, the experimental results showed that the immunosensor revealed a sensitive response to ROR1 in the range of 0.01–1 pg mL−1, and with a lower limit of quantification of 10 attogram/mL (10 ag mL−1). Furthermore, the designed immunosensor was applied for the analysis of ROR1 in several serum samples of chronic lymphocytic leukemia suffering patients with acceptable results, and it also exhibited good selectivity, reproducibility and stability.


2016 ◽  
Vol 80 ◽  
pp. 47-53 ◽  
Author(s):  
Nekane Reta ◽  
Andrew Michelmore ◽  
Christopher Saint ◽  
Beatriz Prieto-Simón ◽  
Nicolas H. Voelcker

2008 ◽  
Author(s):  
Xiao-yi Lü ◽  
Tao Xue ◽  
Zhen-hong Jia ◽  
Hua-wei Shao ◽  
Shi-bin Hou ◽  
...  

Small ◽  
2016 ◽  
Vol 13 (6) ◽  
pp. 1603135 ◽  
Author(s):  
Yiqiu Xia ◽  
Yi Tang ◽  
Xu Yu ◽  
Yuan Wan ◽  
Yizhu Chen ◽  
...  

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Mona Yaghoubi ◽  
Fereshteh Rahimi ◽  
Babak Negahdari ◽  
Ali Hossein Rezayan ◽  
Azizollah Shafiekhani

Abstract Accuracy and speed of detection, along with technical and instrumental simplicity, are indispensable for the bacterial detection methods. Porous silicon (PSi) has unique optical and chemical properties which makes it a good candidate for biosensing applications. On the other hand, lectins have specific carbohydrate-binding properties and are inexpensive compared to popular antibodies. We propose a lectin-conjugated PSi-based biosensor for label-free and real-time detection of Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus) by reflectometric interference Fourier transform spectroscopy (RIFTS). We modified meso-PSiO2 (10–40 nm pore diameter) with three lectins of ConA (Concanavalin A), WGA (Wheat Germ Agglutinin), and UEA (Ulex europaeus agglutinin) with various carbohydrate specificities, as bioreceptor. The results showed that ConA and WGA have the highest binding affinity for E. coli and S. aureus respectively and hence can effectively detect them. This was confirmed by 6.8% and 7.8% decrease in peak amplitude of fast Fourier transform (FFT) spectra (at 105 cells mL−1 concentration). A limit of detection (LOD) of about 103 cells mL−1 and a linear response range of 103 to 105 cells mL−1 were observed for both ConA-E. coli and WGA-S. aureus interaction platforms that are comparable to the other reports in the literature. Dissimilar response patterns among lectins can be attributed to the different bacterial cell wall structures. Further assessments were carried out by applying the biosensor for the detection of Klebsiella aerogenes and Bacillus subtilis bacteria. The overall obtained results reinforced the conjecture that the WGA and ConA have a stronger interaction with Gram-positive and Gram-negative bacteria, respectively. Therefore, it seems that specific lectins can be suggested for bacterial Gram-typing or even serotyping. These observations were confirmed by the principal component analysis (PCA) model.


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