https://www.selleckchem.com/products/ph-797804.html Anastomotic leak in hollow digestive organs is 1.36 times more common in carriers of homozygous CC genotype of the MMP-2 gene and twice less common in minor homozygotes of TT (5.9% vs. 10%, p>0.05). It is statistically significant that in the group of patients with anastomotic leak in hollow digestive organs the GG variant of the TIMP-2 gene was detected 1.6 times more often. Carriers of minor homozygotes of AA genotype in the group with suture failure were not detected, while a similar genotype in the control group was found in 10% (p<0.05). 0.05). It is statistically significant that in the group of patients with anastomotic leak in hollow digestive organs the GG variant of the TIMP-2 gene was detected 1.6 times more often. Carriers of minor homozygotes of AA genotype in the group with suture failure were not detected, while a similar genotype in the control group was found in 10% (p less then 0.05).Multivariate time series (MTSs) are widely found in many important application fields, for example, medicine, multimedia, manufacturing, action recognition, and speech recognition. The accurate classification of MTS has become an important research topic. Traditional MTS classification methods do not explicitly model the temporal difference information of time series, which is, in fact, important and reflects the dynamic evolution information. In this article, the difference-guided representation learning network (DGRL-Net) is proposed to guide the representation learning of time series by dynamic evolution information. The DGRL-Net consists of a difference-guided layer and a multiscale convolutional layer. First, in the difference-guided layer, we propose a difference gating LSTM to model the time dependency and dynamic evolution of the time series to obtain feature representations of both raw and difference series. Then, these two representations are used as two input channels of the multiscale convolutional layer t