D-penicillamine modified copper nanoparticles for fluorometric determination of histamine based on aggregation-induced emission.

A fluorometric method for the determination of histamine has been developed based on aggregation-induced emission (AIE) effect of D-penicillamine capped copper nanoparticles (DPA-CuNPs). The fluorescent DPA-CuNPs were synthesized by a one-pot method using D-penicillamine as both reducing agent and stabilizing ligand. The size, morphology and physical chemical properties of DPA-CuNPs were examined by transmission electron microscopy (TEM), fluorescence spectroscopy, fourier transform infrared spectroscopy (FTIR) and absorption spectroscopy. The DPA-CuNPs exhibit AIE effect and show intense red fluorescence (650 nm). In the presence of histamine, DPA-CuNPs are dispersed into small homogeneous particles, causing fluorescence quenching. Based on this reaction, a histamine sensor is constructed. The fluorescence of the CuNPs solution has a good linear relationship with histamine concentration in the range 0.05 µM to 5 µM and the determination limit (3σ/slope) is 30 nM. The estimated method was successfully applied to the determination of histamine in fish, pork and red wine. Graphical abstract Schematic representation of copper nanoparticles for histamine analysis. In the presence of histamine, the strong red fluorescence of copper nanoparticles is obvious decreased through interaction of copper nanoparticles and histamine.

Medical Image Segmentation Algorithm Based on Feedback Mechanism CNN.

With the development of computer vision and image segmentation technology, medical image segmentation and recognition technology has become an important part of computer-aided diagnosis. The traditional image segmentation method relies on artificial means to extract and select information such as edges, colors, and textures in the image. It not only consumes considerable energy resources and people's time but also requires certain expertise to obtain useful feature information, which no longer meets the practical application requirements of medical image segmentation and recognition. As an efficient image segmentation method, convolutional neural networks (CNNs) have been widely promoted and applied in the field of medical image segmentation. However, CNNs that rely on simple feedforward methods have not met the actual needs of the rapid development of the medical field. Thus, this paper is inspired by the feedback mechanism of the human visual cortex, and an effective feedback mechanism calculation model and operation framework is proposed, and the feedback optimization problem is presented. A new feedback convolutional neural network algorithm based on neuron screening and neuron visual information recovery is constructed. So, a medical image segmentation algorithm based on a feedback mechanism convolutional neural network is proposed. The basic idea is as follows: The model for obtaining an initial region with the segmented medical image classifies the pixel block samples in the segmented image. Then, the initial results are optimized by threshold segmentation and morphological methods to obtain accurate medical image segmentation results. Experiments show that the proposed segmentation method has not only high segmentation accuracy but also extremely high adaptive segmentation ability for various medical images. The research in this paper provides a new perspective for medical image segmentation research. It is a new attempt to explore more advanced intelligent medical image segmentation methods. It also provides technical approaches and methods for further development and improvement of adaptive medical image segmentation technology.

An extensive DeBakey type IIIb aortic dissection with massive right pleural effusion presenting as abdominal pain and acute anemia: particular case report.

We describe the case of a 79-year-old male presented with sudden onset of abdominal pain and mild breathlessness, and complicated acute progressive anemia with haemoglobin which declined from 120 g/L to 70 g/L within five days. An urgent computed tomography angiography showed acute thoracic aortic dissection, DeBakey type IIIb, a dissecting aneurysm in the proximal descending thoracic aorta starting immediately after the origin of the left subclavian artery and extending distally below the renal arteries with evidence of rupture into the right pleural cavity for massive pleural effusion. Plasma D-dimer, brain natriuretic peptide and C reactive protein level were elevated. Our case showed that D-dimer can be used as a 'rule-out' test in patients with suspected aortic dissection. A raised BNP may exert a protective role through anti-inflammatory endothelial actions in the systemic circulation.