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Objectives The present study aims to identify the underlying mechanisms of ferroptosis and lncRNA-miRNA-mRNA network in AP by bioinformatics tools.Methods The ferroptosis genes interacted with data obtained from the Gene Expression Omnibus to identify differentially expressed genes (DEGs). Gene ontology, KEGG pathway enrichment analysis, protein-protein interaction network construction, hub genes association analysis, and transcription factor prediction were used to select and analyze the differentially expressed genes. lncRNA-miRNA-mRNA interaction network were constructed by integrating the lncRNA-miRNA pairs and miRNA-mRNA pairs.Results We identified 38 differentially expressed genes from the datasets. The function enrichment and pathway enrichment analysis of the DEGs were mostly implicated in stress response and the MAPK signaling pathway. The lncRNA-miRNA-mRNA network contained 145 lncRNA nodes, 4 miRNA nodes, 18 mRNA nodes and 235 edges. SQSTM1, KRAS, NFE2L2, HMOX1, SLC7A11, EGFR and ATF3 were identified as hub DEGs, and hub gene-related main TF MYC was discovered to have critical positive associations with these hub genes in pancreatic tissues. Conclusion The results of this study indicated that hub genes and the lncRNA-miRNA-mRNA network may play an important role in the mechanisms of ferroptosis in AP and provide treatment targets for AP.

BACKGROUND Acute pancreatitis (AP) is a common acute abdominal disease. Rapid evaluation of the severity is important for AP prognosis and treatment. Free triiodothyronine (fT3) level is associated with the prognosis of AP patients. This study aimed to investigate the fT3 level in patients with acute pancreatitis; early warning signs of inflammation, including interleukin-6 (IL-6) and interleukin-10 (IL-10); and the correlation of fT3 level with illness severity. MATERIAL AND METHODS Enrolled AP patients (N=312) were divided into an SAP group (N=92) and a non-SAP group (N=220) according to the Revision of Atlanta classification. Blood or tissue samples and baseline clinical characteristics were recorded. The t test and chi-square test were used to evaluate differences between the 2 groups. Multivariate logistic regression analysis and receiver operating characteristic (ROC) curves were used to investigate protective factors. One-way repeated measures analysis of variance was used to evaluate the prognosis of SAP patients. RESULTS In our study, compared with APACHII score (AUC 0.829 [95% CIs 0.769-0.889]) and Ranson score (AUC 0.629 [95% CIs 0.542-0.715]), our predictive model (AUC 0.918 [95% CIs 0.875-0.961]) showed better prognostic performance in predicting poor patient outcomes. In the SAP group, changes in fT3 level were significantly associated with prognosis (P0.05). CONCLUSIONS The predictive model can improve the diagnostic accuracy and prediction of the severity of disease. FT3 level could be used as an independent risk factor to predict the mortality of SAP patients.

With the increasing complexity of CNC machine tools and other rotating machinery, it becomes more and more important to improve the reliability of such machines. It is necessary to estimate the remaining Useful Life (RUL) of the important parts such as bearings of these devices. However, the operating conditions of such parts are often very complicated, and there is a great difference between different devices. Therefore, it is difficult to use the traditional mechanism analysis method, which is not only very hard, but also generally has low prediction accuracy. In order to solve the above problems, this paper proposes a Feature-transferred Prediction Network (FTPN), which can adapt to various working conditions, combining with the neural network method in the field of artificial intelligence, and effectively realizes RUL prediction. Since the existing neural network methods were originally proposed to solve the problems in other fields, such as semantic recognition, this paper uses the feature transfer method to improve them. Firstly, the source network based on Convolutional Neural Network (CNN) is pre-trained for fault recognition, and the fault feature information extracted after training is stored in the fault feature layer. Second, CNN and Gate Recurrent Unit (GRU) are used to build target networks and fit the relationship between time series and remaining longevity. Finally, a special loss function is designed to transfer the features extracted from the source network to the target network to help the target network learn fault features and better predict the mechanical RUL. In order to verify the effectiveness of the proposed method, experiments are carried out using the public data set of accelerated life of bearings, and high prediction accuracy is obtained, which proves that the proposed method has certain generalization. The comparison with the existing methods on the same data set shows that the proposed method has a broad industrial application prospect.

In prognostic and health management, predicting the remaining useful life (RUL) of bearings is of great significance. The traditional RUL prediction methods have two disadvantages: (a) the health indicator (HI) is mainly constructed through expert experience and signal processing technology, which lacks monotonicity and generalization; and (b) in RUL prediction, the time correlation of information is emphasized, but the influence of the variation amplitude and severity of the vibration signal are ignored. Therefore, a method of bearing RUL prediction based on the multiple-feature fusion HI (MFF-HI) and weighted temporal convolution network (WTCN) is proposed in this paper. MFF-HI is constructed by an MFF depth network (MFFDN) with MISH activation function extracting and fusing the degradation information of bearing time-domain features; WTCN is established based on TCN and a new loss function time-mean square error to reduce the error of network pickup in the bearing degradation stage. The NASA IMS dataset, IEEE PHM 2012 dataset and XJTU-SY dataset are used to verify the superiority of the method. The results show that this method can accurately predict the RUL of bearings with higher prediction accuracy.

To investigate the function of gasdermin D (GSDMD) in intestinal damage of mice with severe acute pancreatitis (SAP).The healthy C57BL/6 mice were divided into four groups randomly, including normal saline (NS) group, small interfering RNA (siRNA)-NS group, SAP model group and siRNA-SAP group, with 6 mice in each group. The SAP mouse model was reproduced by intraperitoneal injection of caerulein 50 μg/kg combined with lipopolysaccharide (LPS) 10 mg/kg; the NS group was given the same amount of NS; in the siRNA-SAP group and siRNA-NS group, siRNA 50 mg/kg was injected through the tail vein three times before modeling or injection of NS. The blood of mice eyeball in each group was taken 12 hours after modeling, and serum interleukins (IL-1β, IL-18) levels were detected by enzyme linked immunosorbent assay (ELISA). The mice were sacrificed to observe the general changes in abdominal cavity, the pancreas and ileum tissues were taken to observe the pathological changes under a light microscope. The expression of long-chain non-coding RNA uc.173 (lnc uc.173) was detected by reverse transcription-polymerase chain reaction (RT-PCR). Immunohistochemical method was used to detect the expression of tight junction proteins zonula occluden-1 (ZO-1) and Occludin in intestinal mucosal epithelial cells. Western blotting was used to detect the GSDMD protein expression level in the intestinal tissue.The serum levels of IL-1β and IL-18 in the SAP model group were significantly higher than those in the NS group and the siRNA-NS group [IL-1β (ng/L): 146.66±1.40 vs. 44.48±5.76, 81.49±10.75, IL-18 (ng/L): 950.47±177.09 vs. 115.43±16.40, 84.84±21.90, all P0.05]; and the levels of IL-1β and IL-18 in the siRNA-SAP group were significantly lower than those in the SAP model group [IL-1β (ng/L): 116.26±15.54 vs. 146.66±1.40, IL-18 (ng/L): 689.96±126.08 vs. 950.47±177.09, both P0.05]. General observation showed that there were no obvious abnormalities in the abdominal cavity of the mice in the NS and siRNA-NS groups; the mice in the SAP model group and the siRNA-SAP group had different degrees of edema and congestion in the intestine; compared with the SAP model group, the abnormalities in the siRNA-SAP group was significantly reduced. Under light microscope, there were no obvious changes in the pancreas and intestinal mucosa in the NS group and the siRNA-NS group; the pancreatic tissue of the SAP model group and the siRNA-SAP group had different degrees of edema, inflammatory cell infiltration, and lobular structure damage, and the intestinal mucosa was damaged to a certain degree, and the villi were broken to varying degrees, but the damage in the siRNA-SAP group was lighter. The results of RT-PCR showed that the expression of lnc uc.173 in the intestinal tissues of the model SAP group was significantly lower than that of the NS group and the siRNA-NS group (2The systemic inflammatory response and intestinal mucosal barrier damage of SAP mice may be related to the increase of GSDMD expression in intestinal tissues. GSDMD mediates cell pyrolysis to promote the release of inflammatory factors, cause intestinal injury, and down-regulate the expression of intestinal epithelial cell tight junction proteins such as ZO-1 and Occludin, resulting in intestinal mucosal damage.

BACKGROUND Intestinal injury plays a key role in the pathogenesis of severe acute pancreatitis (SAP). In this study, we investigated the protective function of downregulated Gasdermin D (GSDMD) in intestinal damage in a mouse model of severe acute pancreatitis (SAP). MATERIAL AND METHODS Twenty-four healthy male C57BL/6 mice were randomly divided into 4 groups - the NS group, the siRNA-NS group, the SAP group, and the siRNA-SAP group - with 6 mice in each group. SAP was induced in mice by intraperitoneal injection of caerulein and lipopolysaccharide. The pathological changes of pancreatic and the intestinal mucosa and the relative gene and protein expressions in each group were compared, and the levels of GSDMD and serum IL-1s and IL-18 were evaluated after induction of the SAP model. RESULTS The mice in the SAP group were in more serious condition than those in the siRNA-SAP group, with various degrees of edema and hemorrhage in the intestinal tract. Under an optical microscope, the pathological changes of pancreatic tissue such as edema, inflammatory cell infiltration, and the damage of lobular structural were gradually increased in the SAP group and the siRNA-NS group. In addition, intestinal mucosal damage and intestinal villus breakage were found in the SAP group and the siRNA-NS group, and the latter was lighter than the former. Compared with the SAP group, the level of GSDMD protein expression in the siRNA-SAP group was lower, and the serum levels of IL-1s and IL-18 were higher in the SAP group and siRNA-SAP group (P

Intelligent diagnosis algorithms can monitor faults with industrial production of a timely manner via their powerful learning ability. Multi-sensor diagnosis systems can more comprehensively describe the state of equipment and avoid the influence of incorrect data acquisition locations, which is beneficial to fault diagnosis. The fusion of the original data is a difficult problem, and it is hard to express effective information via traditional algorithms. This paper presents an adaptive data fusion strategy based on deep learning called the convolutional neural network with atrous convolution for the adaptive fusion of multiple source data (FAC-CNN). Specifically, an adaptive-sized convolution kernel that matches the channel of data sources is constructed to capture multi-source data without tedious preprocessing, and the channel of data sources is not limited. The atrous convolution kernel is introduced to expand the field of view of the FAC-CNN and extracts fusion sequence features without repeated computation, resulting in improved stability. The 1D-CNN is added to extract features after atrous convolution. In addition, batch normalization optimizes the distribution of fusion data and the structure of the model. The parametric rectified linear unit activation function and global average pooling are also introduced to improve network performance. The proposed method is validated on an industrial fan system with non-manufacturing faults and a centrifugal pump. Compared with other fusion methods and diagnosis algorithms based on feature engineering, namely CNN, ANN, and SVM, the FAC-CNN model is found to exhibit superior performance.