Explore the vast range of titles available.

The manufacture, modification, and application of surface-functionalized Fe 3 O 4 nanoparticles in the extraction and isolation of antibiotics are reviewed in this paper. Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) can be used to characterize the morphological features of Fe 3 O 4 nanoparticles, while Fourier Transform Infrared Spectrometer (FTIR) and X-ray diffractometry (XRD) can be used to determine the crystalline shapes and distinctive functional groups of these particles. We gathered a lot of data to investigate the effects of experimental parameters like pH, adsorbent dosage, contact time, and ionic strength on the extraction of antibiotics from Fe 3 O 4 magnetic nanoparticles. We additionally talked about the mechanism of action of magnetic nanoparticles for the extraction of separated antibiotics and how they are utilized in practical detection methods. This work concluded with an overview of the potential for environmental friendliness and dispersion of functionalized Fe 3 O 4 nanoparticles, as well as potential challenges and directions.

Recombinase polymerase amplification (RPA) has emerged as a rapid, efficient, and highly sensitive method for nucleic acid amplification, thus becoming a focal point of research in the field of virus detection. This paper provides an overview of RPA, emphasizing its unique double-stranded DNA synthesis mechanism, rapid amplification efficiency, and capability to operate at room temperature, among other advantages. In addition, strategies and case studies of RPA in combination with other technologies are detailed to explore the advantages and potential of these integrated approaches for virus detection. Finally, the development prospect of RPA technology is prospected.

This paper presents a method for the protected geographical indication discrimination of Ophiopogon japonicus from Zhejiang and elsewhere using near-infrared (NIR) spectroscopy combined with chemometrics. A total of 3657 Ophiopogon japonicus samples from five major production areas in China were analyzed by NIR spectroscopy, and divided into 2127 from Zhejiang and 1530 from other areas (‘non-Zhejiang’). Principal component analysis (PCA) was selected to screen outliers and eliminate them. Monte Carlo cross validation (MCCV) was introduced to divide the training set and test set according to a ratio of 3:7. The raw spectra were preprocessed by nine single and partial combination methods such as the standard normal variable (SNV) and derivative, and then modeled by partial least squares regression (PLSR), a support vector machine (SVM), and soft independent modeling of class analogies (SIMCA). The effects of different pretreatment and chemometrics methods on the model are discussed. The results showed that the three pattern recognition methods were effective in geographical origin tracing, and selecting the appropriate preprocessing method could improve the traceability accuracy. The accuracy of PLSR after the standard normal variable was better, with R2 reaching 0.9979, while that of the second derivative was the lowest with an R2 of 0.9656. After the SNV pretreatment, the accuracy of the training set and test set of SVM reached the highest values, which were 99.73% and 98.40%, respectively. The accuracy of SIMCA pretreated with SNV and MSC was the highest for the origin traceability of Ophiopogon japonicus, which could reach 100%. The distance between the two classification models of SIMCA-SNV and SIMCA-MSC is greater than 3, indicating that the SIMCA model has good performance.

Rice cultivation is one of the most significant human-created sources of methane gas. How to accurately measure the methane concentration produced by rice cultivation has become a major problem. The price of the automatic gas sampler used as a national standard for methane detection (HJ 38-2017) is higher than that of gas chromatography, which greatly increases the difficulty of methane detection in the laboratory. This study established a novel methane detection method based on manual injection and split pattern by changing the parameters of the national standard method without adding any additional automatic gas samplers. The standard curve and correlation coefficient obtained from the parallel determination of methane standard gas were y = 2.4192x + 0.1294 and 0.9998, respectively. Relative standard deviation (RSD, <2.82%), recycle rate (99.67–102.02%), limit of detection (LOD, 0.0567 ppm) and limit of quantification (LOQ, 0.189 ppm) of this manual injection method are satisfying, demonstrating that a gas chromatography-flame ionization detector (GC-FID), based on manual injection at a split ratio (SR) of 5:1, could be an effective and accurate method for methane detection. Methane gases produced by three kinds of low-methane rice treated with oxantel pamoate acid, fumaric acid and alcohol, were also collected and detected using the proposed manual injection approach Good peak shapes were obtained, indicating that this approach could also be used for quantification of methane concentration.

A smartphone colorimetric sensor based on the Pt@Au nanozyme was successfully developed for the visual and quantitative detection of omethoate in fruit and vegetables. The anti-omethoate antibody was conjugated on the surface of the Pt@Au nanozyme as a catalytic functional signal probe, and coating antigen conjugated on the surface of magnetic polystyrene microspheres (MPMs) was used as a separation capture probe. In the sensing system, when the catalytic functional signal probe was combined with a separation capture probe containing no omethoate, the visible blue color appeared with the addition of tetramethylbenzidine (TMB) chromogenic solution, and the maximum B value of the sensing system was obtained via the smartphone. With increasing concentrations of omethoate, the visualization of the sensing system decreased, and the B-value obtained via the smartphone dropped. Under optimal detection conditions, the omethoate could be detected in a linear range of 0.5–50 μg/L (R2 = 0.9965), with a detection limit of 0.01 μg/L. The accuracy and reliability of the detection results of this colorimetric sensor were successfully confirmed by enzyme linked immunosorbent assay (ELISA) and gas chromatography. This colorimetric sensor provides a technical reference and potential strategy for the immunoassay of hazard factors in resource-scarce laboratories.

Soybean agglutinin (SBA) is a primary antinutritional factor in soybeans that can inhibit the growth of humans and mammals, disrupt the intestinal environment, and cause pathological changes. Therefore, detecting and monitoring SBA in foods is essential for safeguarding human health. In this paper, M13 phage-displayed nanobodies against SBA were isolated from a naive nanobody library. An M13 phage-displayed nanobody-based competitive enzyme-linked immunosorbent assay (P-cELISA) was then established for SBA analysis using biotinylated anti-M13 phage antibody (biotin-anti-M13) and streptavidin poly-HRP conjugate (SA-poly-HRP). The biotin-anti-M13@SA-poly-HRP probe can easily amplify the detection signal without the chemical modifications of phage-displayed nanobodies. The established P-cELISA presented a linear detection range of 0.56–250.23 ng/mL and a limit of detection (LOD) of 0.20 ng/mL, which was 12.6-fold more sensitive than the traditional phage-ELISA. Moreover, the developed method showed good specificity for SBA and acceptable recoveries (78.21–121.11%) in spiked wheat flour, albumen powder, and whole milk powder. This study proposes that P-cELISA based on biotin-anti-M13@SA-poly-HRP may provide a convenient and effective strategy for the sensitive detection of SBA.

In this study, a quantum-dot-bead (QB)-based fluorescence-linked immunosorbent assay (FLISA) using nanobodies was established for sensitive determination of the Cry2A toxin in cereal. QBs were used as the fluorescent probe and conjugated with a Cry2A polyclonal antibody. An anti-Cry2A nanobody P2 was expressed and used as the capture antibody. The results revealed that the low detection limit of the developed QB-FLISA was 0.41 ng/mL, which had a 19-times higher sensitivity than the traditional colorimetric ELISA. The proposed assay exhibited a high specificity for the Cry2A toxin, and it had no evident cross-reactions with other Cry toxins. The recoveries of Cry2A from the spiked cereal sample ranged from 86.6–117.3%, with a coefficient of variation lower than 9%. Moreover, sample analysis results of the QB-FLISA and commercial ELISA kit correlated well with each other. These results indicated that the developed QB-FLISA provides a potential approach for the sensitive determination of the Cry2A toxin in cereals.

This paper presents a method for the protected geographical indication discrimination of Ophiopogon japonicus from Zhejiang and elsewhere using near-infrared (NIR) spectroscopy combined with chemometrics. A total of 3657 Ophiopogon japonicus samples from five major production areas in China were analyzed by NIR spectroscopy, and divided into 2127 from Zhejiang and 1530 from other areas (‘non-Zhejiang’). Principal component analysis (PCA) was selected to screen outliers and eliminate them. Monte Carlo cross validation (MCCV) was introduced to divide the training set and test set according to a ratio of 3:7. The raw spectra were preprocessed by nine single and partial combination methods such as the standard normal variable (SNV) and derivative, and then modeled by partial least squares regression (PLSR), a support vector machine (SVM), and soft independent modeling of class analogies (SIMCA). The effects of different pretreatment and chemometrics methods on the model are discussed. The results showed that the three pattern recognition methods were effective in geographical origin tracing, and selecting the appropriate preprocessing method could improve the traceability accuracy. The accuracy of PLSR after the standard normal variable was better, with R[sup.2] reaching 0.9979, while that of the second derivative was the lowest with an R[sup.2] of 0.9656. After the SNV pretreatment, the accuracy of the training set and test set of SVM reached the highest values, which were 99.73% and 98.40%, respectively. The accuracy of SIMCA pretreated with SNV and MSC was the highest for the origin traceability of Ophiopogon japonicus, which could reach 100%. The distance between the two classification models of SIMCA-SNV and SIMCA-MSC is greater than 3, indicating that the SIMCA model has good performance.

The manufacture, modification, and application of surface-functionalized Fe O nanoparticles in the extraction and isolation of antibiotics are reviewed in this paper. Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) can be used to characterize the morphological features of Fe O nanoparticles, while Fourier Transform Infrared Spectrometer (FTIR) and X-ray diffractometry (XRD) can be used to determine the crystalline shapes and distinctive functional groups of these particles. We gathered a lot of data to investigate the effects of experimental parameters like pH, adsorbent dosage, contact time, and ionic strength on the extraction of antibiotics from Fe O magnetic nanoparticles. We additionally talked about the mechanism of action of magnetic nanoparticles for the extraction of separated antibiotics and how they are utilized in practical detection methods. This work concluded with an overview of the potential for environmental friendliness and dispersion of functionalized Fe O nanoparticles, as well as potential challenges and directions.

Female panicles (FPs) play an important role in the formation of yields in maize. From 40 days after sowing to the tasseling stage for summer maize, FPs are developing and sensitive to drought. However, it remains unclear how FPs respond to drought stress during FP development. In this study, FP differentiation was observed at 20 and 30 days after drought (DAD) and agronomic trait changes of maize ears were determined across three treatments, including well-watered (CK), light drought (LD), and moderate drought (MD) treatments at 20, 25, and 30 DAD. RNA-sequencing was then used to identify differentially expressed genes (DEGs) in FPs at 30 DAD. Spikelets and florets were suppressed in LD and MD treatments, suggesting that drought slows FP development and thus decreases yields. Transcriptome analysis indicated that 40, 876, and 887 DEGs were detected in LD/CK, MD/CK, and MD/LD comparisons. KEGG pathway analysis showed that ‘biosynthesis of other secondary metabolites’ and ‘carbohydrate metabolism’ were involved in the LD response, whereas ‘starch and sucrose metabolism’ and ‘plant hormone signal transduction’ played important roles in the MD response. In addition, a series of molecular cues related to development and growth were screened for their drought stress responses.