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Objective To investigate the gut microbiome composition and changes and its association with disease severity in patients with acute pancreatitis (AP). Methods This study was a prospective cross-sectional analysis. The subjects of the study were AP patients in Peking Union Medical College Hospital from June 2018 to January 2022 and healthy volunteers. The clinical data and stool samples of the two groups were collected, the 16S rRNA of the gut microbiome was DNA sequenced, and bioinformatic analysis was performed. The differences in gut microbiome between the two groups were compared, and the correlation between the intestinal flora and the severity of AP was analyzed by receiver operating characteristic(ROC) curve. Results A total of 60 AP patients and 20 healthy volunteers were enrolled. Among the AP patients, 20 were mild AP, 20 were moderately severe AP, and 20 were severe AP. During hospitalization, 22 cases were transferred to ICU, while 38 cases were not. In α diversity analysis, the Shannon index of AP patients was significant decreased compared to healthy volunteers (P < 0.05). β diversity of the two groups was significantly different. At the phylum, family, genus and species levels, there were also significant differences in the microbiome composition between the two groups. Linear discriminant analysis effect size analysis revealed that g_Escherichia-Shigella, g_Enterococcus, and f_Enterococcaceae were dominant in AP patients while g_Blautia, and g_Bifidobacterium were dominant in healthy volunteers. Function analysis found that multiple amino acid biosynthesis pathways were blocked in gut microbiome of AP patients, and potential pathogenicity and migration ability of gut microbiome increased significantly. In subgroup analysis, g_Enterococcus was increased and g_Bacteroidaceae was decreased in ICU patients compared to non-ICU patients. Based on the probability of disease index, the ROC curve showed that the area under the curve for identifying AP patients and AP patients transferred to ICU were 0.996 and 0.743. Conclusions The pathogenic bacteria increased and the beneficial bacteria decreased in the gut microbiome of AP patients. Changes in gut microbiota are related to the severity of AP disease and therefore have the potential to be used as novel biomarkers for AP.

Chronic pancreatitis is a progressive fibroinflammatory disorder with no currently satisfactory treatment. Emerging evidence suggests an association between gut microbial dysbiosis and chronic pancreatitis. Although direct causative evidence is lacking, it is hypothesized that the gut microbiota may play a pivotal role in modulating pancreatic function via the gut–pancreas axis. Thus, modulating the gut microbiota through the administration of probiotics or prebiotics may alleviate pancreatic disorders. In this review, we first propose the potential mechanisms by which specific probiotics or prebiotics may ameliorate chronic pancreatitis, including the alleviation of small intestinal bacterial overgrowth (SIBO), the facilitation of short-chain fatty acids' (SCFAs) production, and the activation of glucagon-like peptide-1 receptors (GLP-1Rs) in the pancreas. Since there are currently no probiotics or prebiotics used for the treatment of chronic pancreatitis, we discuss research in other disease models that have used probiotics or prebiotics to modulate pancreatic endocrine and exocrine functions and prevent pancreatic fibrosis. This provides indirect evidence for their potential application in the treatment of chronic pancreatitis. We anticipate that this research will stimulate further investigation into the gut–pancreas axis and the potential therapeutic value of probiotics and prebiotics in chronic pancreatitis. [ABSTRACT FROM AUTHOR]

In order to investigate the pollution emission of freight diesel motor vehicles in logistics parks, the article selected three logistics parks A, B and C in Dongxihu District of Wuhan City as the research object, analyzed the activity level of diesel motor vehicles based on the on-site survey and the data obtained from Wuhan Motor Vehicle Supervisory Platform, and carried out the field monitoring of NOx and PM2.5 in the entrance and exit sections of the logistics parks. The results show that the trucks entering and leaving the logistics park are mainly heavy-duty diesel vehicles, accounting for over 78.1%; the proportion of diesel motor vehicles of National + National emission standard types is more than 50%, but there are still a small number of diesel motor vehicles of National standard. The activity level of diesel motor vehicles in each logistics park has no direct correlation with the area of the logistics park, and the changes in traffic flow on weekdays and non-weekdays show different trends from the traffic volume on urban roads. The total emissions of NOx, PM2.5, PM10, CO, and HC from the three logistics parks during August were 36.958 tons, 0.529 tons, 0.586 tons, 16.588 tons, and 1.157 tons, respectively, and the monitored concentrations of NO2 and PM2.5 at the cross-sections into and out of the logistics parks in August and November were 1.24-2.41 times higher than those of the neighboring street air-quality monitoring stations, and 2.70-2.82 times higher than that of the neighboring -street air-quality monitoring stations, respectively, which shows that diesel motor vehicle exhaust emissions from the Logistics Park have a considerable impact on the quality of the air environ ment in the neighboring areas, and the type of diesel motor vehicle structure in the Logistics Park is also associated with the green transformation. [ABSTRACT FROM AUTHOR]

We study the distributionally robust linearized stable tail adjusted return ratio (DRLSTARR) portfolio optimization problem, in which the objective is to maximize the worst-case linearized stable tail adjusted return ratio (LSTARR) performance measure under data-driven Wasserstein ambiguity. We consider two types of imperfectly known uncertainties, named uncertain probabilities and continuum of realizations, associated with the losses of assets. We account for two typical combinatorial trading constraints, called buy-in threshold and diversification constraints, to reflect stock market restrictions. Leveraging conic duality theory to tackle the distributionally robust worst-case expectation, the proposed problems are reformulated into mixed-integer linear programming problems. We carry out a series of empirical tests to illustrate the scalability and effectiveness of the proposed solution framework, and to evaluate the performance of the DRLSTARR-constructed portfolios. The cross-validation results obtained using a rolling-horizon procedure show the superior out-of-sample performance of the DRLSTARR portfolios under an uncertain continuum of realizations. [ABSTRACT FROM AUTHOR]