A ratiometric fluorescence imprinted sensor based on N-CDs and metal-organic frameworks for visual smart detection of malathion.

Sensitive and rapid detection of pesticide residues in food is essential for human safety. A ratiometric imprinted fluorescence sensor N-CDs@Eu-MOF@MIP (BR@MIP) was constructed to sensitively detect malathion (Mal). Europium-based metal organic frameworks (Eu-MOF) were used as supporters to improve the sensitivity of the BR@MIP. N-doped carbon dots (N-CDs) were used as fluorescent source to produce fluorescent signal. A linear relationship between the concentration of Mal and the fluorescence response of the sensor was found in the Mal concentration range of 1-10 µM with a limit of detection (LOD) of 0.05 µM. Furthermore, the sensor was successfully applied for the detection of Mal in lettuce, tap water, and soil samples, with recoveries in the range of 93.0 % - 99.3 %. Additionally, smartphone-based sensors were used to detect Mal in simulated real samples. Thus, the construction of ratiometric imprinted fluorescence sensor has provided a good strategy for the detection of Mal.

Investigating the impact of attenuated fluorescence spectra on protein discrimination.

The optical remote sensing techniques are promising for the real-time detection, and identification of different types of hazardous biological materials. However, the received fluorescent spectra from a remote distance suffer from the atmospheric attenuation effect upon the spectral shape. To investigate the influence of atmospheric attenuation on characterizing, and classifying biological agents, the laboratory-measured fluorescence data of fourteen proteins combined with the atmospheric transmission factors of the MODTRAN model were conducted with different detection ranges. The multivariate analysis techniques of principal component analysis (PCA) and linear discriminant analysis (LDA), and the predictors of Random Forest and XGBoost were employed to assess the separability and distinguishability of different spectra recorded. The results showed that the spectral-shift effect on attenuated spectra varied as a function of the detection range, the atmospheric visibility, and the spectral distribution. According to the PCA and LDA analysis, the distribution of decomposed factors changed in the spectral explanatory power with the increasing attenuation effect, which was consistent with the hierarchical clustering results. Random Forest exhibited higher performance in classifying protein samples than that of XGBoost, while the two methods performed similarly in identifying harmful and harmless subgroups of proteins. Fewer subgroups decreased the sensitivity of the classification accuracy to the attenuation effect. Our analysis demonstrated that combining atmospheric transport models to build a fluorescence spectral database is essential for fast identification between spectra, and reduced classification criteria could facilitate the compatibility of spectral database and classification algorithms.

A signal-off photoelectrochemical sandwich-type immunosensor based on WO3/TiO2 Z-scheme heterojunction.

A sandwich "signal-off" type photoelectrochemical (PEC) immunosensor was fabricated based on a composite heterojunction of tungsten oxide/titanium oxide microspheres (WO3/TiO2) acting as signal amplification platform and carbon microspheres loaded by gold nanoparticles (Cs@Au NPs) utilized as the label for detecting antibody. WO3/TiO2 had excellent photoelectric performance, and the results of Mott-Schottky plots, open-circuit voltage, and electron spin resonance spectroscopy indicated that it belonged to the Z-scheme heterojunction transfer mechanism of photogenerated carriers. To achieve the sensitization of PEC immunosensor, Cs@Au NP-labeled immunocomplex can effectively reduce the photocurrent signal. The PEC immunosensors were fabricated under the optimal conditions of 1:1 WO3/TiO2 (molar ratio), 2.0 mg mL-1 WO3/TiO2, and 1.5 mg mL-1 Cs@Au NPs. Through comparison of the detection results of label-free and sandwich-type PEC immunosensors for nucleocapsid (N) protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), we found that the sensitivity of the sandwich type was 2.53 times the label-free type, and the limit of detection was 0.006 ng mL-1, i.e., 3.17 times lower than the label-free type. This demonstrates that the developed sandwich-type PEC immunosensor will have a brighter application prospect.

A novel strategy for bioaerosol rapid detection based on broad-spectrum high-efficiency magnetic enrichment and separation combined with ATP bioluminescence.

Bioaerosol detection technology represented by laser-induced fluorescence (LIF) cannot effectively detect bioaerosols in the presence of interferents such as plant-derived smoke, industrial waste gas, pollen and pollen debris which can produce strong non-biological fluorescence interference. To overcome this drawback, in this study, a novel method based on broad-spectrum high-efficiency magnetic enrichment and separation combined with adenosine triphosphate (ATP) bioluminescence was proposed for Escherichia coli (E. coli) bioaerosols rapid detection. First, E. coli bioaerosols mixed with interferents were collected. Core-shell Fe3O4@Polydopamine@Polyethyleneimine magnetic particles were used as bioaerosol enrichment materials to enrich E. coli bioaerosol sampling solutions. Subsequently, an ATP bioluminescence assay was performed to determine the concentration of E. coli. A linear relationship was observed between ATP bioluminescence intensity after enrichment and the E. coli bioaerosol concentration in the range of 870-49,098 particles per liter; the bioluminescence intensity measured after enrichment was significantly higher than that before enrichment, and this enrichment method provide a 6-fold better sensitivity in bioaerosol detection. More importantly, this method efficiently enriched and detected bioaerosols in plant-derived smoke. This method can effectively improve the sensitivity of ATP bioluminescence detection, and possesses the advantages of convenient operation and strong anti-interference ability. It also provides a foundation for the effective detection of bioaerosols mixed with interfering substances, and a reference for evaluating the sensitivity and anti-interference of LIF-based instruments.

A Novel SPR Immunosensor Based on Dual Signal Amplification Strategy for Detection of SARS-CoV-2 Nucleocapsid Protein.

Since the global outbreak of coronavirus disease 2019 (COVID-19), it has spread rapidly around the world. The nucleocapsid (N) protein is one of the most abundant SARS-CoV-2 proteins. Therefore, a sensitive and effective detection method for SARS-CoV-2 N protein is the focus of research. Here, we developed a surface plasmon resonance (SPR) biosensor based on the dual signal-amplification strategy of Au@Ag@Au nanoparticles (NPs) and graphene oxide (GO). Additionally, a sandwich immunoassay was utilized to sensitively and efficiently detect SARS-CoV-2 N protein. On the one hand, Au@Ag@Au NPs have a high refractive index and the capability to electromagnetically couple with the plasma waves propagating on the surface of gold film, which are harnessed for amplifying the SPR response signal. On the other hand, GO, which has the large specific surface area and the abundant oxygen-containing functional groups, could provide unique light absorption bands that can enhance plasmonic coupling to further amplify the SPR response signal. The proposed biosensor could efficiently detect SARS-CoV-2 N protein for 15 min and the detection limit for SARS-CoV-2 N protein was 0.083 ng/mL, with a linear range of 0.1 ng/mL~1000 ng/mL. This novel method can meet the analytical requirements of artificial saliva simulated samples, and the developed biosensor had a good anti-interference capability.

A Study of the Detection of SARS-CoV-2 by the Use of Electrochemiluminescent Biosensor Based on Asymmetric Polymerase Chain Reaction Amplification Strategy.

A new and reliable method has been constructed for detecting severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) open reading frames 1ab (ORF1ab) gene via highly sensitive electrochemiluminescence (ECL) biosensor technology based on highly efficient asymmetric polymerase chain reaction (asymmetric PCR) amplification strategy. This method uses magnetic particles coupled with biotin-labeled one complementary nucleic acid sequence of the SARS-CoV-2 ORF1ab gene as the magnetic capture probes, and [Formula: see text]-labeled amino-modified another complementary nucleic acid sequence as the luminescent probes, and then a detection model of magnetic capture probes-asymmetric PCR amplification nucleic acid products-[Formula: see text]-labeled luminescent probes is formed, which combines the advantages of highly efficient asymmetric PCR amplification strategy and highly sensitive ECL biosensor technology, enhancing the method sensitivity of detecting the SARS-CoV-2 ORF1ab gene. The method enables the rapid and sensitive detection of the ORF1ab gene and has a linear range of 1-[Formula: see text] copies/[Formula: see text], a regression equation of [Formula: see text] = [Formula: see text] + 2919.301 ([Formula: see text] = 0.9983, [Formula: see text] = 7), and a limit of detection (LOD) of 1 copy/[Formula: see text]. In summary, it can meet the analytical requirements for simulated saliva and urine samples and has the benefits of easy operation, reasonable reproducibility, high sensitivity, and anti-interference abilities, which can provide a reference for developing efficient field detection methods for SARS-CoV-2.

Impact of different classification schemes on discrimination of proteins with noise-contaminated spectra using laboratory-measured fluorescence data.

Biological agents are important to detect and identify with respect to environmental contamination and public health. Noise contamination in fluorescent spectra is one of the contributors to the uncertainties of identification. In order to investigate the noise-tolerant capability provided by laboratory-measured excitation-emission matrix (EEM) fluorescence spectra that are used as a database, fluorescence properties of four proteinaceous biotoxin samples and ten harmless protein samples were characterized by EEM fluorescence spectra, and the predicting performance of models trained by laboratory-measured fluorescence data was tested and verified from validation data with noise-contaminated spectra. By means of peak signal of noise (PSNR) as an indicator of noise levels, the potential impact of noise contaminations on the characterization and discrimination of these samples was evaluated quantitatively. Different classification schemes utilizing multivariate analysis techniques of Principal Component Analysis (PCA), Random Forest (RF), and Multi-layer Perceptron (MPL) coupled with feature descriptors of differential transform (DT), Fourier transform (FT) and wavelet transform (WT) were conducted under different PSNR values. We systematically analyzed the performance of classification schemes by the case study at 20 PSNR and by statistical analysis from 1-100 PSNR. The results show that the spectral features with EEM-WT decreased the demanding number of input variables while retaining high performances in sample classification. The spectral features with EEM-FT presented the worst performance although having the largest number of features. The distributions of feature importance and contribution were found sensitive to noise contaminations. The classification scheme of PCA prior to MPL with EEM-WT as input presented an improvement in lower PSNR. These results indicate that robust features extracted by corresponding techniques are critical to enhancing the spectral differentiation capabilities among these samples and play an important role in eliminating the noise effect. The study of classification schemes for discriminating protein samples with noise-contaminated spectra presents tremendous potential for future developments in the rapid detection and identification of proteinaceous biotoxins based on three-dimensional fluorescence spectrometry.

A novel smartphone-integrated binary-emission molecularly imprinted fluorescence sensor embedded with MIL-101(Cr) for sensitive and real-time detection of protein.

Aiming for precise, real-time, and on-site analysis of proteins, an innovative binary-emission fluorescence imprinted polymer was designed by sol-gel method after mixing MIL-101(Cr), green CdTe (g-CdTe) and red CdTe (r-CdTe) for detection of protein. In this proposal, MIL-101(Cr), as a favorable supporter, provided high surface area and porosity for imprinting sites, which ameliorated the transfer rate and the sensitivity of the nanosensor. And g-CdTe and r-CdTe were served as signal transduction for dual-emission response. Based on strengthened recognition reaction between high-affinity imprinting sites and protein, the fluorescence intensities of g-CdTe and r-CdTe yielded conspicuous two responses at 528 nm and 634 nm for protein under the excitation of 350 nm. The cytochrome c (Cyt c) and trypsin were served as model proteins to verify the generality of strategy. Given prominent merits of MIL-101(Cr), g-CdTe/r-CdTe@MIL-101(Cr)@MIP exhibited good linear range of 1-30 µM for Cyt c and 0.15-4 µM for trypsin, and the limit of detection were 0.13 µM and 0.014 µM, respectively. Significantly, an unsophisticated smartphone-based sensing device was developed by integrating g-CdTe/r-CdTe@MIL-101(Cr)@MIP with a 3D printing portable device to obtain precise on-site results. As expected, this portable platform was successfully applied for monitoring Cyt c and trypsin with a detection limit of 0.71 µM and 0.026 µM, respectively. These results indicated this dual-response molecularly imprinted fluorescence senor based on smartphone provided promising perspectives on futural on-site protein analysis.

Surface Plasmon Resonance Sensor Based on Core-Shell Fe3O4@SiO2@Au Nanoparticles Amplification Effect for Detection of T-2 Toxin.

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.

Identify the Virus-like Models for COVID-19 as Bio-Threats: Combining Phage Display, Spectral Detection and Algorithms Analysis.

The rapid identification and recognition of COVID-19 have been challenging since its outbreak. Multiple methods were developed to realize fast monitoring early to prevent and control the pandemic. In addition, it is difficult and unrealistic to apply the actual virus to study and research because of the highly infectious and pathogenic SARS-CoV-2. In this study, the virus-like models were designed and produced to replace the original virus as bio-threats. Three-dimensional excitation-emission matrix fluorescence and Raman spectroscopy were employed for differentiation and recognition among the produced bio-threats and other viruses, proteins, and bacteria. Combined with PCA and LDA analysis, the identification of the models for SARS-CoV-2 was achieved, reaching a correction of 88.9% and 96.3% after cross-validation, respectively. This idea might provide a possible pattern for detecting and controlling SARS-CoV-2 from the perspective of combining optics and algorithms, which could be applied in the early-warning system against COVID-19 or other bio-threats in the future.

CdTe QDs-sensitized TiO2 nanocomposite for magnetic-assisted photoelectrochemical immunoassay of SARS-CoV-2 nucleocapsid protein.

A sensitive, reliable, and cost-effective detection for SARS-CoV-2 was urgently needed due to the rapid spread of COVID-19. Here, a "signal-on" magnetic-assisted PEC immunosensor was constructed for the quantitative detection of SARS-CoV-2 nucleocapsid (N) protein based on Z-scheme heterojunction. Fe3O4@SiO2@Au was used to connect the capture antibody to act as a capture probe (Fe3O4@SiO2@Au/Ab1). It can extract target analytes selectively in complex samples and multiple electrode rinsing and assembly steps were avoided effectively. CdTe QDs sensitized TiO2 coated on the surface of SiO2 spheres to form Z-scheme heterojunction (SiO2@TiO2@CdTe QDs), which broadened the optical absorption range and inhibited the quick recombination of photogenerated electron/hole of the composite. With fascinating photoelectric conversion performance, SiO2@TiO2@CdTe QDs were utilized as a signal label, thus further realizing signal amplification. The migration mechanism of photogenerated electrons was further deduced by active material quenching experiment and electron spin resonance (ESR) measurement. The elaborated immunosensor can detect SARS-CoV-2 N protein in the linear range of 0.005-50 ng mL-1 with a low detection limit of 1.8 pg mL-1 (S/N = 3). The immunosensor displays extraordinary sensitivity, strong anti-interference, and high reproducibility in detecting SARS-CoV-2 N protein, which envisages its potential application in the clinical diagnosis of COVID-19.

A photoelectrochemical immunosensor based on magnetic all-solid-state Z-scheme heterojunction for SARS-CoV-2 nucleocapsid protein detection.

Rapid, convenient and accurate detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is urgently needed to timely diagnosis of coronavirus pandemic (COVID-19) and control of the epidemic. In this study, a signal-off photoelectrochemical (PEC) immunosensor was constructed for SARS-CoV-2 nucleocapsid (N) protein detection based on a magnetic all-solid-state Z-scheme heterojunction (Fe3O4@SiO2@TiO2@CdS/Au, FSTCA). Integrating the advantages of magnetic materials and all-solid-state Z-scheme heterostructures, FSTCA was implemented to ligate the capture antibody to form magnetic capture probe (FSTCA/Ab1). It can simplify the separation and washing process to improve reproducibility and stability, while allowing immune recognition to be performed in the liquid phase instead of the traditional solid-liquid interface to improve anti-interference. Besides, the heterojunction inhibited the recombination of photogenerated electron/hole (e-/h+) and promoted the light absorption to provide superior photoelectric substrate signal. The mechanism of photogenerated e-/h+ transfer of FSTCA were investigated by the electron spin resonance (ESR) spectroscopy. SiO2 spheres loaded with Au NPs utilized as an efficient signal quencher. The steric hindrance effect of SiO2@Au labeled detection antibodies (SiO2@Au-Ab2) conjugates significantly diminished light absorption and hindered the transfer of photogenerated electrons, further amplifying the signal change value. Based on the above merits, the elaborated immunosensor had a wide linear range of 10 pg mL-1-100 ng mL-1 and a low detection limit down to 2.9 pg mL-1 (S/N = 3). The fabricated PEC immunosensor demonstrated strong anti-interference, easy operation, and high sensitivity, showing enormous potential in clinical diagnosis of SARS-CoV-2.

Ricin aptamer screening based on the QSAR model and construction of piezoresistive micro-cantilever aptasensor.

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 (R2 = 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.

Performance of feature extraction method for classification and identification of proteins based on three-dimensional fluorescence spectrometry.

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.

Performance of Classification Models of Toxins Based on Raman Spectroscopy Using Machine Learning Algorithms.

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.

Trimetallic Au@Pd@Pt nanozyme-enhanced lateral flow immunoassay for the detection of SARS-CoV-2 nucleocapsid protein.

The rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seriously threatened global public health. Establishing a rapid and sensitive diagnostic test for early detection of the SARS-CoV-2 nucleocapsid protein is urgently required to defend against the pandemic. Herein, an enhanced lateral flow immunoassay (LFIA) was fabricated by trimetallic Au@Pd@Pt core-shell nanozymes for detection of the SARS-CoV-2 nucleocapsid protein. The Au@Pd@Pt nanozymes (Au@Pd@Pt NZs) synthesized via a one-pot method, with a dendrite morphology and uniform particle size, showed excellent peroxidase-like activity. Due to the perfect enzyme-like catalytic activity toward 3,3',5,5'-tetramethylbenzidine (TMB) in the presence of hydrogen peroxide (H2O2), the catalytic signal could be generated even by a tiny amount of Au@Pd@Pt NZs accumulated on the test strip. Therefore, rapid detection with higher sensitivity was achieved. The Au@Pd@Pt NZs-based LFIA provided a quantitative range of 0.05-100 ng mL-1 with a limit of detection of 0.037 ng mL-1, which was 17-fold lower than the LFIA without enhancement. The average recoveries from spiked samples were in the range of 92.5-107.9% with relative standard deviations all less than 4%, indicating the reliability and repeatability of the proposed LFIA. Additionally, the proposed LFIA could report results within 30 min using a microplate reader. In conclusion, the Au@Pd@Pt NZs-LFIA is a rapid, simple, and sensitive method for detecting the SARS-CoV-2 nucleocapsid protein.

A New Method for Abrin Detection Based on the Interaction between Target Molecules and Fluorescently Labeled Aptamers on Magnetic Microspheres.

A quantitative structure-activity relationship (QSAR) model for the structure and affinity of abrin aptamers was established. A higher affinity abrin aptamer based on the established QSAR model was screened by site-directed mutagenesis. The fluorescence quenching effect between magnetic microspheres and fluorescent molecules was studied for the first time. A new method for abrin detection based on the interaction between target molecules and fluorescently labeled aptamers on magnetic microspheres was developed, with the detection limit of 5 ng mL-1. This method can overcome the influence of complex environmental interferents in abrin detection and can meet the analysis requirements for simulated samples such as water, soil, and food.

Construction, Characterization, and Application of a Nonpathogenic Virus-like Model for SARS-CoV-2 Nucleocapsid Protein by Phage Display.

With the outbreak and spread of COVID-19, a deep investigation of SARS-CoV-2 is urgent. Direct usage of this virus for scientific research could provide reliable results and authenticity. However, it is strictly constrained and unrealistic due to its high pathogenicity and infectiousness. Considering its biosafety, different systems and technologies have been employed in immunology and biomedical studies. In this study, phage display technology was used to construct a nonpathogenic model for COVID-19 research. The nucleocapsid protein of SARS-CoV-2 was fused with the M13 phage capsid p3 protein and expressed on the M13 phages. After validation of its successful expression, its potential as the standard for qPCR quantification and affinity with antibodies were confirmed, which may show the possibility of using this nonpathogenic bacteriophage to replace the pathogenic virus in scientific research concerning SARS-CoV-2. In addition, the model was used to develop a system for the classification and identification of different samples using ATR-FTIR, which may provide an idea for the development and evaluation of virus monitoring equipment in the future.

A Highly Sensitive Fluorescence and Screen-Printed Electrodes-Electrochemiluminescence Immunosensor for Ricin Detection Based on CdSe/ZnS QDs with Dual Signal.

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.

Magnetic/fluorescent dual-modal lateral flow immunoassay based on multifunctional nanobeads for rapid and accurate SARS-CoV-2 nucleocapsid protein detection.

The SARS-CoV-2 pandemic has posed a huge challenge to rapid and accurate diagnosis of SARS-CoV-2 in the early stage of infection. In this work, we developed a novel magnetic/fluorescent dual-modal lateral flow immunoassay (LFIA) based on multifunctional nanobeads for rapid and accurate determination of SARS-CoV-2 nucleocapsid protein (NP). The multifunctional nanobeads were fabricated by using polyethyleneimine (PEI) as a mediate shell to combine superparamagnetic Fe3O4 core with dual quantum dot shells (MagDQD). The core-shell structure of MagDQD label with high loading density of quantum dots (QDs) and superior magnetic content realized LFIA with dual quantitative analysis modal from the assemblies of individual single nanoparticles. The LFIA integrated the advantages of magnetic signal and fluorescent signal, resulting excellent accuracy for quantitative analysis and high elasticity of the overall detection. In addition, magnetic signal and fluorescent signal both had high sensitivity with the limit of detection (LOD) as 0.235 ng mL-1 and 0.012 ng mL-1, respectively. The recovery rates of the methods in simulated saliva samples were 91.36%-103.60% (magnetic signal) and 94.39%-104.38% (fluorescent signal). The results indicate the method has a considerable potential to be an effective tool for diagnose SARS-CoV-2 in the early stage of infection.