Peroxidase-like activity of bimetal Cu-Zn oxide mesoporous nanospheres for the determination of o-aminophenol.

To enhance the peroxidase-like performance and its application in detection of toxic o-aminophenol (o-AP), a kind of bimetal Cu-Zn oxide-based mesoporous nanosphere (Cu2/3Zn1/3O PNPs) was constructed under microwave-radiation conditions. Its mesoporous microstructure and peroxidase-like catalytic activity were investigated in detail. The results showed that Cu2/3Zn1/3O PNPs possessed a high specific surface area of 34.89 m2g-1 and a well-distributed mesoporous size of approximate 6.07 nm, which endowed the superior peroxidase-like performance. The material catalyzes the oxidization of 3,3',5,5'-tetramethylbenzidine (TMB) with Km/Vmax of 0.104 mM/3.79 × 10-8 M·s-1 in the presence of H2O2. Especially o-AP could exclusively deteriorate the characteristic UV-Vis absorbance intensity at 653 nm (A653) of the Cu2/3Zn1/3O PNPs-TMB-H2O2 system with obvious color change from blue to colorless. Under the optimal conditions, the effect of some interfering substances was low and the limit of detection (LOD) for o-AP was 1.65 × 10-8 mol/L (S/N = 3). When applied to the colorimetric detection of o-AP in practice, the recovery was between 96.1 and 107.2% with R.S.D. less than 2.04%. The mechanism of synergic-enhancement peroxidase-mimic activity of Cu2/3Zn1/3O PNPs and its exclusive colorimetric response to o-AP were proposed as well.

Ag nanozyme strengthened by folic acid: Superior peroxidase-mimicking activity and application for visual monitoring of dopamine.

Dopamine (DA) is an important neurotransmitter; however, any excess or deficiency of DA will cause several diseases in humans. To monitor DA efficiently and conveniently, a Ag nanozyme strengthened by bioactive folic acid (FA@AgNPs) was developed by homogeneous redox assembly. After the microstructure and performance were characterized in detail, it was noted that the proposed FA@AgNPs possessed superior peroxidase-like activity due to the ultra-small Ag nanoparticles and multiple amino, hydroxyl, and aromatic rings in FA. FA@AgNPs accelerated the oxidation of 3,3',5,5'-tetramethylbenzidine (TMB) with a low Michaelis constant (Km) and high maximal reaction rate (Vmax). Importantly, the characteristic absorbance intensity of FA@AgNPs-TMB-H2O2 at 652 nm (A652) was exclusively deteriorated in the presence of trace DA, accompanied by a visual color change from blue to colorless. Under the optimized conditions (pH 4.0, 300 µL 1.5 mM TMB, 300 µL 1.0 M H2O2 and incubated for 30 min at room temperature), there expressed an excellent linear relationship between lgA0/A652 and cDA from 1.0 ×10-8 to 6.67×10-6 mol/L with a low limit detection of 7.1×10-10 mol/L (S/N=3). When applied for monitoring of DA in real fruit juice and pharmaceutical samples, the recovery was between 96.6% and 104.9%, with RSD less than 2.2%. The enhanced peroxidase-like activity of the FA@AgNP system and its selective recognition mechanism for DA are also proposed.

Lesion Segmentation in Ultrasound Using Semi-Pixel-Wise Cycle Generative Adversarial Nets.

Breast cancer is the most common invasive cancer with the highest cancer occurrence in females. Handheld ultrasound is one of the most efficient ways to identify and diagnose the breast cancer. The area and the shape information of a lesion is very helpful for clinicians to make diagnostic decisions. In this study we propose a new deep-learning scheme, semi-pixel-wise cycle generative adversarial net (SPCGAN) for segmenting the lesion in 2D ultrasound. The method takes the advantage of a fully convolutional neural network (FCN) and a generative adversarial net to segment a lesion by using prior knowledge. We compared the proposed method to a fully connected neural network and the level set segmentation method on a test dataset consisting of 32 malignant lesions and 109 benign lesions. Our proposed method achieved a Dice similarity coefficient (DSC) of 0.92 while FCN and the level set achieved 0.90 and 0.79 respectively. Particularly, for malignant lesions, our method increases the DSC (0.90) of the fully connected neural network to 0.93 significantly (p 0.001). The results show that our SPCGAN can obtain robust segmentation results. The framework of SPCGAN is particularly effective when sufficient training samples are not available compared to FCN. Our proposed method may be used to relieve the radiologists' burden for annotation.