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This research aims to investigate the ameliorative potential of baicalin on lipopolysaccharide-induced acute lung injury by the suppression of oxidative stress and inflammation via the activation of the nuclear erythroid factor 2 (Nrf2)-mediated heme oxygenase-1 (HO-1) signaling pathway. Specific pathogen-free male mice, weighing between 25 and 30 g, were divided into the following four groups of 10 mice each: the control group, LPS group, LPS + baicalin group, and baicalin group. Bronchoalveolar lavage fluid (BALF), blood, and tissue were collected on the 16th day and used for hematological (total leukocyte, macrophage, neutrophil, and lymphocyte counts in both blood and BALF, biochemical (antioxidant enzymes, MDA, Nrf2, and HO-1), and histological analyses. The protective effect of baicalin on lipopolysaccharide-induced acute lung injury is based on its antioxidative stress capabilities that are mediated partly by the Nrf2/HO-1 signaling pathway. Baicalin pretreatment significantly decreased the rise in the lung injury score; total leukocyte, neutrophil, lymphocyte, and macrophage counts; pro-inflammatory mediators, tumor necrosis factor (TNF-α), interleukins (IL-6 and IL-1β); biosynthesis of oxidative products, e.g., malondialdehyde (MDA); and restoration of antioxidative enzyme (superoxide dismutase and catalase) activities by improving the expression of nuclear Nrf2 and cytosolic HO-1 in lipopolysaccharide-induced acute lung injury. The protective effects of baicalin are partly due to its antioxidant and anti-inflammatory effects. Our findings indicate that baicalin protects against lipopolysaccharide-induced severe lung injury by enhancing antioxidant systems and significantly reducing both inflammatory cells and mediators via the Nrf2-mediated HO-1 signaling pathway.

The right ventricular longitudinal strain (RVLS) of pulmonary hypertension (PH) patients and its relationship with RV function parameters measured by echocardiography and hemodynamic parameters measured by right heart catheterization was investigated. According to the WHO functional class (FC), 66 PH patients were divided into FC I/II (group 1) and III/IV (group 2). RV function parameters were measured by echocardiographic examinations. Hemodynamic parameters were obtained by right heart catheterization. Patients in group 2 had higher systolic pulmonary artery pressure (sPAP; P

Solutions to combinatorial optimization problems frequently rely on heuristics to minimize an intractable objective function. The optimum is sought iteratively and pre-setting the number of iterations dominates in operations research applications, which implies that the quality of the solution cannot be ascertained. Deterministic bounds offer a mean of ascertaining the quality, but such bounds are available for only a limited number of heuristics and the length of the corresponding interval may be difficult to control in an application. A small, almost dormant, branch of the literature suggests using statistical principles to derive statistical bounds for the optimum. We discuss alternative approaches to derive statistical bounds. We also assess their performance by testing them on 40 test $$p$$p-median problems on facility location, taken from Beasley's OR-library, for which the optimum is known. We consider three popular heuristics for solving such location problems; simulated annealing, vertex substitution, and Lagrangian relaxation where only the last offers deterministic bounds. Moreover, we illustrate statistical bounds in the location of 71 regional delivery points of the Swedish Post. We find statistical bounds reliable and much more efficient than deterministic bounds provided that the heuristic solutions are sampled close to the optimum. Statistical bounds are also found computationally affordable.

Combinatorial optimization problems such as locating facilities frequently rely on heuristics to minimize the objective function. The optimum is often sought iteratively; a criterion is therefore necessary to be able to decide when the procedure attains such an optimum. Pre-setting the number of iterations is dominant in OR applications, however, the fact that the quality of the solution cannot be ascertained by pre-setting the number of iterations makes it less preferable. A small and, almost dormant, branch of the literature suggests usage of statistical principles to estimate the minimum and its bounds as a tool to decide upon the stopping criteria and also to evaluate the quality of the solution. In the current work we have examined the functioning of statistical bounds obtained from four different estimators using simulated annealing. P-median test problems taken from Beasley's OR-library were used for the sake of testing. Our findings show that the Weibull estimator and 2nd order Jackknife estimators are preferable and the requirement of sample size to be about 10. It should be noted that reliable statistical bounds are found to depend critically on a sample of heuristic solutions of high quality; we have therefore provided a simple statistic for checking the quality. The work finally concludes with an illustration of applying statistical bounds to the problem of locating 70 post distribution centers in a region in Sweden.

Pancreatic cancer is a highly metastatic and chemo-resistant disease. Secreted proteins involved in cell-cell interactions play an important role in changing the tumor microenvironment. Previous studies generally focus on the secretome of cancer cell line from serum-free media, due to the serious interference of fetal bovine serum (FBS). However, serum-starvation may alter expression patterns of secreted proteins. Hence, efforts to decrease the interference of serum in proteomic analysis of serum-containing media have been hampered to quantitatively measure the tumor secretion levels. Recently, the metabolic labeling, protein equalization, protein fractionation and filter-aided sample preparation (FASP) strategy (MLEFF) has been successfully used to avoid the disturbance of serum on secretome analysis. Here, this efficient method was applied for comparative secretome analysis of two hamster pancreatic cancer cells with differentially metastatic potentials, enabling the observation of 161 differentially expressed proteins, including 106 proteins that had been previously reported and detected in plasma. By integrated analysis of our data and publicly available bioinformatics resources, we found that a combination panel consisting of CDH3, PLAU, and LFNG might improve the prognosis of overall pancreatic cancer survival. These secreted proteins may serve as a potential therapeutic targets for pancreatic cancer metastasis.