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Abstract Background Depressive symptoms are frequently observed in patients with primary biliary cholangitis (PBC). The role of depressive symptoms on cirrhosis has not been fully noticed in PBC. We aimed to establish a risk model for cirrhosis that took depressive symptoms into account. Methods Depressive symptoms were assessed by the 17‐item Hamilton Depression Rating Scale (HAMD‐17). HAMD‐17 score was analyzed in relation to clinical parameters. Least absolute shrinkage and selection operator (Lasso)‐logistic regression and decision tree models were used to explore the effect of depressive symptoms on cirrhosis. Results The rate of depressive symptom in patients with PBC (n = 162) was higher than in healthy controls (n = 180) (52.5% vs. 16.1%; p

Background and objectivesLactose intolerance and coeliac disease are common clinical nutrient malabsorption disorders, with an unclear pathogenesis and limited therapeutic options. It is widely believed that the gut microbiota plays an important role in many digestive disorders, but its role in lactose intolerance and coeliac disease is not yet clear. This study aimed to investigate the correlation between gut microbiota and lactose intolerance and coeliac disease.Materials and methodsThis study utilized the genome-wide association study database to investigate the association between gut microbiota and lactose intolerance and coeliac disease using Mendelian randomization (MR). The robustness of our findings was confirmed through subsequent analyses including Cochrane’s Q statistic, MR-Egger Intercept Regression, MR-PRESSO Global Test and Leave-one-out methods.ResultsBy employing the inverse variance weighted method, we identified that family Veillonellaceae, genus Oxalobacter and Senegalimassilia were protective against lactose intolerance, whereas genus Anaerotruncus, Eubacterium rectale group and Ruminococcus2 were found to be risk factors for lactose intolerance. Regarding coeliac disease, class Bacilli and Gammaproteobacteria, family FamilyXIII and Veillonellaceae, genus Eisenbergiella, Lachnoclostridium, RuminococcaceaeUCG014 and Ruminococcus2 were identified as protective factors, while class Betaproteobacteria, genus Eubacterium xylanophilum group and Blautia were risk factors. Furthermore, reverse the MR analysis did not reveal any evidence of a causal relationship between lactose intolerance or coeliac disease and the bacteria identified in our study.ConclusionThis study provides novel insights into exploring the role of gut microbiota in lactose intolerance and coeliac disease; however, further experiments investigations are required to elucidate the specific underlying mechanisms.

The influence of low-frequency electromagnetic casting (LFEC) and Sc addition as variables on the microstructure, properties, and deformation behavior of 7A36 aluminum alloy has been systematically examined. The results have predicted that the LFEC could make the grains of the as-cast alloy finer by electromagnetic stirring and forced convection. By the addition of Sc, the average diameters of the equiaxed grains have been reduced and the intensity of the fiber texture has become weakened. The fine and continuous η′ precipitates on the grain boundaries (GBs) instead of the coarse large-sized discontinuous η′ precipitates, which results in a more uniform distribution of plastic deformation and which is beneficial for improving plasticity. By adding Sc and applying LFEC, the large size η′ internal grain precipitated phase decreased slightly. The fine nano-sized Al3(Sc, Zr) phase particles were dispersed in the matrix. As the dislocation movement had to travel less distance, that's why this process consumed less energy. These features also reduced the strain difference within the grain and near the grain boundary, which result in the increase of EL. The excellent mechanical properties of 7A36 aluminum as extruded plates were obtained when it was aged at 120 °C for 8 h by Sc micro-alloying and applying LFEC. The yield strength (YS) and ultimate tensile strength (UTS) remained at 738.3 MPa and 767.0 MPa, respectively, while the elongation (EL) to failure 6.5% is increased by 160% than the value of direct chill casting.

The primary task in calculating the tobacco shred blending ratio is identifying the four tobacco shred types: expanded tobacco silk, cut stem, tobacco silk, and reconstituted tobacco shred. The classification precision directly affects the subsequent determination of tobacco shred components. However, the tobacco shred types, especially expanded tobacco silk and tobacco silk, have no apparent differences in macro-scale characteristics. The tobacco shreds have small size and irregular shape characteristics, creating significant challenges in their recognition and classification based on machine vision. This study provides a complete set of solutions aimed at this problem for screening tobacco shred samples, taking images, image preprocessing, establishing datasets, and identifying types. A block threshold binarization method is used for image preprocessing. Parameter setting and method performance are researched to obtain the maximum number of complete samples with acceptable execution time. ResNet50 is used as the primary classification and recognition network structure. By increasing the multi-scale structure and optimizing the number of blocks and loss function, a new tobacco shred image classification method is proposed based on the MS-X-ResNet (Multi-Scale-X-ResNet) network. Specifically, the MS-ResNet network is obtained by fusing the multi-scale Stage 3 low-dimensional and Stage 4 high-dimensional features to reduce the overfitting risk. The number of blocks in Stages 1–4 are adjusted from the original 3:4:6:3 to 3:4:N:3 (A-ResNet) and 3:3:N:3 (B-ResNet) to obtain the X-ResNet network, which improves the model’s classification performance with lower complexity. The focal loss function is selected to reduce the impact of identification difficulty for different sample types on the network and improve its performance. The experimental results show that the final classification accuracy of the network on a tobacco shred dataset is 96.56%. The image recognition of a single tobacco shred requires 103 ms, achieving high classification accuracy and efficiency. The image preprocessing and deep learning algorithms for tobacco shred classification and identification proposed in this study provide a new implementation approach for the actual production and quality detection of tobacco and a new way for online real-time type identification of other agricultural products.