Label-free discrimination of single nucleotide changes in DNA by reflectometric interference Fourier transform spectroscopy

Theoretical optimization of a frequency-domain- based color polarization interference sensor via coupling with a Gaussian beam

In this paper, a Gaussian-beam-coupled color polarization interference (CCPI) sensor with an adjustable digital Gaussian filter was proposed theoretically in the frequency domain. The coupling principle of a traditional color polarization interference (CPI) sensor and a Gaussian beam was investigated, and sensitivity optimization was then implemented. The formation of a doublet is related to the shape of a CPI curve, expected wavelength, and FWHM of the Gaussian beam. Based on the calculated results, the angle between the two polarizers and the obliquity of the wave plate are the two most significant adjusting parameters. The sensitivity of a CCPI sensor is approximately twofold higher than that of a traditional CPI sensor. The sensing range is also related to the FWHM of the Gaussian beam and a larger FWHM usually means a wider sensing range. We also found that the sensitivity would reach a maximum when the incident angle is near the total internal reflection angle. Besides, a longer incident wavelength usually corresponds to a higher sensitivity and the sensitivity could reach as high as 9270 nm/refractive index unit when the resonance wavelength is 977.40 nm.

A lectin-coupled porous silicon-based biosensor: label-free optical detection of bacteria in a real-time mode

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.

Real-time and label-free detection of VKORC1 genes based on a magnetoelastic biosensor for warfarin therapy

Various thrombotic disorders have been treated with the anticoagulant warfarin. However, a small change in warfarin concentration may lead to drug adverse reactions or therapeutic failure due to its narrow therapeutic index. Therefore, the dose of warfarin must be monitored for each patient during therapy in real-time and in a sensitive and stable manner. In this work, we designed a magnetoelastic (ME) biosensor using Metglas alloy 2826 to detect VKORC1 genotypes, which is one of the most important known genetic determinants of warfarin dosing. The sensor enabled both fast responses to DNA binding and wireless transmission of signals. Specifically in the target recognition layer, the sensor introduced an avidin-biotin interaction system for signal amplification by increasing the surface load mass. The resonance frequency shift of the signal was linear to the concentration of the target in the range of 0.1 fM to 10 pM, with a detection limit (LOD) of 0.00389 fM (S/N = 3) and a sensitivity of 45.7 Hz pM^-1. Importantly, this ME-based biosensor was small and portable without the use of any optical labels, which has high potential to be applied in advanced biomedical diagnosis of nucleic acids and proteins.

Recent Progress in Optical Sensors for Biomedical Diagnostics

In recent years, several types of optical sensors have been probed for their aptitude in healthcare biosensing, making their applications in biomedical diagnostics a rapidly evolving subject. Optical sensors show versatility amongst different receptor types and even permit the integration of different detection mechanisms. Such conjugated sensing platforms facilitate the exploitation of their neoteric synergistic characteristics for sensor fabrication. This paper covers nearly 250 research articles since 2016 representing the emerging interest in rapid, reproducible and ultrasensitive assays in clinical analysis. Therefore, we present an elaborate review of biomedical diagnostics with the help of optical sensors working on varied principles such as surface plasmon resonance, localised surface plasmon resonance, evanescent wave fluorescence, bioluminescence and several others. These sensors are capable of investigating toxins, proteins, pathogens, disease biomarkers and whole cells in varied sensing media ranging from water to buffer to more complex environments such as serum, blood or urine. Hence, the recent trends discussed in this review hold enormous potential for the widespread use of optical sensors in early-stage disease prediction and point-of-care testing devices.