Anti-GSR (aa 413-522) polyclonal antibody

This paper presents the research results of the copolymer structure and properties of 2-hydroxyethylmethacrylate (HEMA) with polyvinylpyrrolidone (PVP) and their hydrogels, obtained by block polymerization in the presence of iron sulfate (II). By the methods of chemical analysis, IR spectroscopy, Thermogravimetric (TG) and Differential Thermal Analysis (DTA), the course of grafted copolymerization of HEMA on PVP with the formation of a cross-linked copolymer was confirmed. The results received by scanning electron microscopy showed that due to the copolymerization of HEMA with PVP, macroporous hydrogels with a pore size of 10-30 mu m were obtained. The peculiarities of the structure formation of the obtained copolymers depending on the initial composition formulation were established and their structural parameters were investigated: PVP grafting efficiency, PVP content in copolymer, molecular weight of internodal fragment of polymer network, crosslinking degree, and crosslinking density. The interrelation of sorption-diffusion, physical-mechanical and thermophysical properties along with the structure of the obtained materials was proved. It was shown that with the increasing PVP content in the original composition, the efficiency of its grafting and crosslinking density of the polymer network decreased, but the surface hardness, heat resistance, sorption capacity of copolymers in the dry state, as well as ion permeability and elasticity in the swollen state increased, while their tensile strength deteriorated. It is proved that by changing the original composition formulation it is possible to change the structure and hence the properties of the copolymers in the desired direction.

Background and Purpose Pretreatment prediction of the response to neoadjuvant chemoradiotherapy (NCRT) helps to determine the subsequent plans for the patients with locally advanced rectal cancer (LARC). If the good responders (GR) and non-good responders (non-GR) can be accurately predicted, they can choose to intensify the neoadjuvant chemoradiotherapy to decrease the risk of tumor progression during NCRT and increase the chance of organ preservation. Compared with radiomics methods, deep learning (DL) may adaptively extract features from the images without the need of feature definition. However, DL suffers from limited training samples and signal discrepancy among different scanners. This study aims to construct a DL model to predict GRs by training apparent diffusion coefficient (ADC) images from different scanners. Methods The study retrospectively recruited 700 participants, chronologically divided into a training group (n = 500) and a test group (n = 200). Deep convolutional neural networks were constructed to classify GRs and non-GRs. The networks were designed with a max-pooling layer parallelized by a center-cropping layer to extract features from both the macro and micro scale. ADC images and T2-weighted images were collected at 1.5 Tesla and 3.0 Tesla. The networks were trained by the image patches delineated by radiologists in ADC images and T2-weighted images, respectively. Pathological results were used as the ground truth. The deep learning models were evaluated on the test group and compared with the prediction by mean ADC value. Results Area under curve (AUC) of receiver operating characteristic (ROC) is 0.851 (95% CI: 0.789-0.914) for DL model with ADC images (DL_ADC), significantly larger (P = 0.018, Z = 2.367) than that of mean ADC with AUC = 0.723 (95% CI: 0.637-0.809). The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of DL_ADC model are 94.3%, 68.3%, 87.4% and 83.7%, respectively. The DL model with T2-weighted images (DL_T2) produces an AUC of 0.721 (95% CI: 0.640-0.802), significantly (P = 0.000, Z = 3.554) lower than that of DL_ADC model. Conclusion Deep learning model reveals the potential of pretreatment apparent diffusion coefficient images for the prediction of good responders to neoadjuvant chemoradiotherapy.