Fluorometric Assay of Tyrosinase and Atrazine Based on the Use of Carbon Dots and the Inhibition of Tyrosinase Activity.

Sensitive and convenient strategy of tyrosinase (TYR) and its inhibitor atrazine is in pressing demand for essential research as well as pragmatic application. In this work, an exquisite label-free fluorometric assay with high sensitivity, convenience and efficiency was described for detecting TYR and the herbicide atrazine on the basis of fluorescent nitrogen-doped carbon dots (CDs). The CDs were prepared via one-pot hydrothermal reaction starting from citric acid and diethylenetriamine. TYR catalyzed the oxidation of dopamine to dopaquinone derivative which could quench the fluorescence of CDs through a fluorescence resonance energy transfer (FRET) process. Thus, a sensitive and selective quantitative evaluation of TYR can be constructed on the basis of the relationship between the fluorescence of CDs and TYR activity. Atrazine, a typical inhibitor of TYR, inhibited the catalytic activity of TYR, leading to the reduced dopaquinone and the fluorescence was retained. The strategy covered a broad linear range of 0.1-150 U/mL and 4.0-80.0 nM for TYR and atrazine respectively with a low detection limit of 0.02 U/mL and 2.4 nM/mL. It is also demonstrated that the assay can be applied to detect TYR and atrazine in spiked complex real samples, which provides infinite potential in application of disease monitoring along with environmental analysis.

Rolling circle transcription and CRISPR/Cas12a-assisted versatile bicyclic cascade amplification assay for sensitive uracil-DNA glycosylase detection.

Uracil-DNA glycosylase (UDG) is pivotal in maintaining genome integrity and aberrant expressed UDG is highly relevant to numerous diseases. Sensitive and accurate detecting UDG is critically significant for early clinical diagnosis. In this research, we demonstrated a sensitive UDG fluorescent assay based on rolling circle transcription (RCT)/CRISPR/Cas12a-assisted bicyclic cascade amplification strategy. Target UDG catalyzed to remove uracil base of DNA dumbbell-shape substrate probe (SubUDG) to produce an apurinic/apyrimidinic (AP) site, at which SubUDG was cleaved by apurinic/apyrimidinic endonuclease (APE1) subsequently. The exposed 5'-PO4 was ligated with the free 3'-OH terminus to form an enclosed DNA dumbbell-shape substrate probe (E-SubUDG). E-SubUDG functioned as a template can actuate T7 RNA polymerase-mediated RCT signal amplification, generating multitudes of crRNA repeats. The resultant Cas12a/crRNA/activator ternary complex activated the activity of Cas12a, causing a significantly enhanced fluorescence output. In this bicyclic cascade strategy, target UDG was amplified via RCT and CRISPR/Cas12a, and the whole reaction was completed without complex procedures. This method enabled sensitive and specific monitor UDG down to 0.0005 U/mL, screen corresponding inhibitors, and analyze endogenous UDG in A549 cells at single-cell level. Importantly, this assay can be extended to analyze other DNA glycosylase (hAAG and Fpg) by altering the recognition site in DNA substrates probe rationally, thereby offering a potent tool for DNA glycosylase-associated clinical diagnosis and biomedical research.

Spherical Hydrogel Sensor Based on PB@Fe-COF@Au Nanoparticles with Triplet Peroxidase-like Activity and Multiple Capture Sites for Effective Detection of Organophosphorus Pesticides.

The residues of organophosphorus pesticides (OPs) have drawn worldwide increasing attention because of their potential fatal effects on human health and ecological systems. It is of great significance to develop an efficient and portable method for in-field detection of OPs. Herein, a novel core-shell nanocomposite of prussian blue@Fe-covalent organic framework@Au (PB@Fe-COF@Au) was constructed. Fe2+ and Fe3+ in PB nanoparticle (PBNP) cores, Fe-porphyrin in COF shells, and AuNPs grown on shells all acted as peroxidase-like catalytic active sites, enabling PB@Fe-COF@Au to possess triplet peroxidase-like activity. A colorimetric, affordable, sensitive, and selective strategy was designed to detect OPs. Compared with previous reports, this sensor realized a wider linear range for chlorpyrifos of 10-800 ng mL-1 with a relatively lower detection limit of 0.61 ng mL-1, which was attributed to the overlapping triple catalytic sites of PB@Fe-COF@Au and triple response sites to OPs. The assay was successfully employed to detect chlorpyrifos in food and environmental samples. Moreover, to meet the demand of in-field detection for OPs, a spherical hydrogel method based on PB@Fe-COF@Au with visual, portable, and equipment-free features was fabricated. This work provides a new pathway to design and apply effective nanozymes for on-site monitoring of pesticides.

Glucose and pH responsive fluorescence detection system based on simple synthesis of silicon-coated perovskite quantum dots.

Perovskite quantum dots (PQDs) are extremely unstable in ambient air due to their inherent structural instability, which limits the wide application of PQDs. In this work, silicon-coated CsPbBr3 PQDs (CsPbBr3@SiO2) was synthesized via a simple method. The SiO2 coating effectively isolated PQDs from water and oxygen in the environment, which were the main elements that destroyed the structure stability of PQDs. The synthesized CsPbBr3@SiO2 can be stored in water for more than 2 months and posessed wonderful dispersibility in aqueous solution. The fluorescence intensity remained unchanged within 7 days and only decreased by 11.9 % within 2 months. We found that CsPbBr3@SiO2 was extremely sensitive to environmental pH, and the fluorescence intensity decreased with the reduction of pH. In addition, an excellent linear relationship with pH value in the range of 1.0 âˆ¼ 5.0 was achieved. As we all known that glucose can be catalyzed by glucose oxidase to produce gluconic acid and hydrogen peroxide, in which a good deal of protons were produced and the pH was gradually lowered. Since CsPbBr3@SiO2 was stable to water and oxygen, and sensitive to ambient pH, we applied CsPbBr3@SiO2 to the detection of glucose. CsPbBr3@SiO2 showed fantastic selectivity and sensitivity to glucose, and the detection limit can even reach 18.5 µM. Furthermore, CsPbBr3@SiO2 was successfully applied to the detection of glucose in the human serum with satisfactory performance.

Machine learning-based multiparametric traditional multislice computed tomography radiomics for improving the discrimination of parotid neoplasms.

Characterization of parotid tumors is important for treatment planning and prognosis, and parotid tumor discrimination has recently been developed at the molecular level. The aim of the present study was to establish a machine learning (ML) predictive model based on multiparametric traditional multislice CT (MSCT) radiomic and clinical data analysis to improve the accuracy of differentiation among pleomorphic adenoma (PA), Warthin tumor (WT) and parotid carcinoma (PCa). A total of 345 patients (200 with WT, 91 with PA and 54 with PCa) with pathologically confirmed parotid tumors were retrospectively enrolled from five independent institutions between January 2010 and May 2019. A total of 273 patients recruited from institutions 1, 2 and 3 were randomly assigned to the training model; the independent validation set consisted of 72 patients treated at institutions 1, 4 and 5. Data were investigated using a linear discriminant analysis-based ML classifier. Feature selection and dimension reduction were conducted using reproducibility testing and a wrapper method. The diagnostic accuracy of the predictive model was compared with histopathological findings as reference results. This classifier achieved a satisfactory performance for the discrimination of PA, WT and PCa, with a total accuracy of 82.1% in the training cohort and 80.5% in the validation cohort. In conclusion, ML-based multiparametric traditional MSCT radiomics can improve the accuracy of differentiation among PA, WT and PCa. The findings of the present study should be validated by multicenter prospective studies using completely independent external data.