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Currently, many aptamers of different target molecules have been screened by systematic evolution of ligands by exponential enrichment (SELEX) method. However, since the crystal structure of aptamers is complicated to obtain, few studies focus on the quantitative structure activity relationship (QSAR) of aptamers. It is significant to develop a virtual screening method for aptamers based on the QSAR model. In this study, a piezoresistive micro-cantilever aptasensor was fabricated to quantificationally detect ricin based on a new aptamer obtained via a virtual screening method. The advanced screening method based on the established QSAR model can screen aptamers formed by site-directed mutation of bases. The results of intermolecular interactions and molecular docking displayed that the new aptamer was mainly bound to ricin A-chain, and its combination of ricin with ricin B-chain aptamer has a significant synergistic effect. Due to the aptamer with excellent performance and the good linear relationship between the relative resistance change and the vertical deformation of the micro-cantilever, the piezoresistive micro-cantilever aptasensor emerged a linear detection range from 7.5 pg mL -1 to 75 ng mL -1  (R 2  = 0.949) with the limit of detection 750 fg mL -1  (S/N ≥ 3) for ricin. Furthermore, the proposed piezoresistive micro-cantilever sensor displayed excellent analytical performance with good reproducibility and high specificity., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Three-dimensional excitation emission matrix (EEM) fluorescence spectroscopy was employed to discriminate protein samples comprising bovine serum albumin, neurotensin, ovalbumin, ricin, trypsin from bovine pancreas and trypsin from porcine pancreas. Two methods of feature extraction with and without parameterization were applied to the spectral data in order to evaluate their performance of discrimination between protein samples. The discrimination of protein samples was conducted by k-means clustering algorithm and eigenvalue extracting procedure based on principal component analysis (PCA). It was found that the method of feature extraction without parameterization performed best, correctly attributing 100% of the spectral data in the condition of two principal components (PCs) captured. Features extracted with spectral parameterization failed to separate ricin and trypsin from bovine pancreas in same condition. Without spectral parameterization, less dimensionality and unique principal components captured by PCA indicates the spectrally-resolved features of corresponding protein samples. By clustering using each spectrum at fixed excitation wavelength, excitation wavelengths matched with common intrinsic fluorophores were found to be more sensitive to the classification accuracy. Contributions of spectral features extracted from EEM to the principal components were discussed and demonstrated their feature differentiation capabilities among six protein samples. These results reveal that appropriate extraction approach of features in combination with PCA analysis could be used in discrimination of protein samples at species level as a spectroscopic diagnostic tool. Our study provides fundamental references about computational strategies when EEM are used to explore proteins in ambient environment., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022. Published by Elsevier B.V.)

A sensitive dual-readout immunosensor for fluorescence and electrochemiluminescence (ECL) detection of ricin was established, which was combined with a streptavidin-biotin signal amplification system. CdSe/ZnS quantum dots with fine fluorescence and ECL properties were used as the dual-signal function probes of the sandwich immunocomplex. Under the optimum experimental conditions, the dual signal intensity increased significantly with the rise in ricin concentration. The fluorescence intensity of the senor exhibited a good liner relationship toward the ricin concentrations with 0.1~100 ng/mL and the limit of detection (LOD) was 81.7 pg/mL; taking ECL as the detection signal, the sensor showed a linear relationship with the ricin concentrations ranging from 0.01 ng/mL to 100 ng/mL and the LOD was 5.5 pg/mL. The constructed sensor with high sensitivity had been successfully applied to the detection of ricin in complex matrices with satisfactory recoveries. The proposed immunosensor model can be extended to the analysis and detection of others target proteins.

Rapid and accurate detection of protein toxins is crucial for public health. The Raman spectra of several protein toxins, such as abrin, ricin, staphylococcal enterotoxin B (SEB), and bungarotoxin (BGT), have been studied. Multivariate scattering correction (MSC), Savitzky–Golay smoothing (SG), and wavelet transform methods (WT) were applied to preprocess Raman spectra. A principal component analysis (PCA) was used to extract spectral features, and the PCA score plots clustered four toxins with two other proteins. The k-means clustering results show that the spectra processed with MSC and MSC-SG methods have the best classification performance. Then, the two data types were classified using partial least squares discriminant analysis (PLS-DA) with an accuracy of 100%. The prediction results of the PCA and PLS-DA and the partial least squares regression model (PLSR) perform well for the fingerprint region spectra. The PLSR model demonstrates excellent classification and regression ability (accuracy = 100%, Rcv = 0.776). Four toxins were correctly classified with interference from two proteins. Classification models based on spectral feature extraction were established. This strategy shows excellent potential in toxin detection and public health protection. These models provide alternative paths for the development of rapid detection devices. [ABSTRACT FROM AUTHOR]

In this paper, a core-shell based on the Fe3O4@SiO2@Au nanoparticle amplification technique for a surface plasmon resonance (SPR) sensor is proposed. Fe3O4@SiO2@AuNPs were used not only to amplify SPR signals, but also to rapidly separate and enrich T-2 toxin via an external magnetic field. We detected T-2 toxin using the direct competition method in order to evaluate the amplification effect of Fe3O4@SiO2@AuNPs. A T-2 toxin–protein conjugate (T2-OVA) immobilized on the surface of 3-mercaptopropionic acid-modified sensing film competed with T-2 toxin to combine with the T-2 toxin antibody–Fe3O4@SiO2@AuNPs conjugates (mAb-Fe3O4@SiO2@AuNPs) as signal amplification elements. With the decrease in T-2 toxin concentration, the SPR signal gradually increased. In other words, the SPR response was inversely proportional to T-2 toxin. The results showed that there was a good linear relationship in the range of 1 ng/mL~100 ng/mL, and the limit of detection was 0.57 ng/mL. This work also provides a new possibility to improve the sensitivity of SPR biosensors in the detection of small molecules and in disease diagnosis. [ABSTRACT FROM AUTHOR]

Highlights • A new method for ricin detection was established based on TMR biosensor. • In this method, the magnetic immuno-chromatographic test strip and TMR magnetic sensitive sensor signal detection were combined effectively. • This method can overcome the interference of background color and light of complex environmental samples. • This method can realize the selective separation of complex samples and rapid quantitative detection. Abstract In this research, the magnetic immuno-chromatographic test strip and TMR magnetic sensitive sensor signal detection were combined effectively, which displayed the multiple advantages of magnetic nanoparticles (MNPs) label technique, immunochromatography (IC) and tunneling magnetoresistance (TMR) sensing technology. Through detecting magnetic field intensity generated by functionalized magnetic signal probe on immuno-chromatographic test strip, a new method for ricin detection was established based on TMR biosensor. Compared with traditional labeled biosensor based on optical signal detection, this method can overcome the interference of background color and light of complex environmental samples, and has advantages of easy operation, high sensitive, accurate quantification, good reproducibility, specificity and interference-resistance. With this method, the selective separation and rapid quantitative detection of complex samples can be achieved. The linear range of quantitative detection for ricin was 1 ng/mL–200 μg/mL, the regression equation was Y = 0.2125 X + 0.5675 (R = 0.9706, P < 0.0001, N = 12), the limit of detection (LOD) was 1 ng/mL. This method can overcome the influence of complex environmental interferents in the toxin detection, and can meet the analysis requirements for simulated samples such as water, soil, food, blood and so on. The method showed good recovery ratio and reproducibility, with the recovery ratio of 90.1%–94.2% and the RSD of 2.43%–3.35%. [ABSTRACT FROM AUTHOR]