Effect of PPARγ agonist (rosiglitazone) on the secretion of Th2 cytokine in asthma mice

AbstractObjectiveTo explore the effect of PPARγ agonist (rosiglitazone) on the secretion of Th2 cytokines and the proportion of immune cell subsets in asthma mice.MethodsOvalbumin (OVA)-sensitized mice were used to build asthma models. Those mice were divided into the normal control group, model group and rosiglitazone group. Differences of the changes in lung histopathology of mice in the three groups were observed through hematoxylin and eosin (HE) strain, and the numbers of the total cells, eosinophils and neutrophils in BALF of mice in the three groups were compared. ELISA and real-time PCR were employed to detect the protein levels of interleukin (IL)-5, IL-13, IL-4 and IL-10 and mRNA level, respectively. Flow cytometry number was implied to analyze the proportion of immune cell subsets in peripheral blood of mice.ResultsCompared with the mice in the control group, and mice of the model group, the infiltration of inflammatory cells in BALF increased, bronchial smooth muscle became thickened, a large amount of collagen deposited, the secretion of Th2 cytokine increased significantly, the ratio of regulatory T cells (Treg) decreased, the ratio of T17 cells rose distinctly; while in mice of the rosiglitazone group, the changes of their lung histopathology were improved obviously, the number of infiltration of inflammatory cells declined, the thickened smooth muscle relieved, the deposition of collagen decreased, the secretion of Th2 cytokine was inhibited, the ratio of Treg went up, and the increased of the ratio of T17 cells was inhibited but still not return to normal level.ConclusionsRosiglitazone can regulate the proportion of Treg and Th17 cells and inhibit the secretion of Th2 cytokines, which inhibit the airway inflammatory response for asthma mice effectively

Dynamic diffusion tensor imaging reveals structural changes in the bilateral pyramidal tracts after brain stem hemorrhage in rats

Background and Purpose: Few studies have concentrated on pyramidal tract (PY) changes after brain stem hemorrhage (BSH). In this study, we used a diffusion tensor imaging (DTI) technique and histologic identification to investigate longitudinal PY changes on both the contralateral and ipsilateral sides after experimental BSH. Methods: BSH was induced in 61 Sprague-Dawley rats by infusing 30 μl of autogenous tail blood into each rat’s right pons. DTI and motor function examinations were performed repeatedly on days 1, 3, 7, 14, and 28 after surgery. Fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity were measured in the bilateral PYs. The axon and myelin injury in the PY were evaluated by histologic study. Results: As compared with normal controls, the bilateral PYs in rats with induced BSH showed an early decrease and a late increase in fractional anisotropy and an early increase and a late decrease in mean diffusivity. A progressive decrease in axial diffusivity with dramatic axon loss from day 1 to day 28 after BSH was found bilaterally. The bilateral PYs showed an early increase and a late decrease in radial diffusivity. Early myelin injury and late repair were also detected pathologically in the bilateral PYs of rats with BSH. Thus, the early motor function deficits of rats with BSH began to improve on day 14 and had almost completely disappeared by day 28. Conclusions: DTI revealed dynamic changes in the bilateral PYs after BSH, which was confirmed by histologic findings and which correlated with motor function alteration. These findings support the idea that quantitative DTI can track structural changes in the bilateral PYs and that DTI may serve as a noninvasive tool to predict the prognoses of patients with BSH

The Minimum Variation Timescales of X-ray bursts from SGR J1935+2154

The minimum variation timescale (MVT) of soft gamma-ray repeaters can be an important probe to estimate the emission region in pulsar-like models, as well as the Lorentz factor and radius of the possible relativistic jet in gamma-ray burst (GRB)-like models, thus revealing their progenitors and physical mechanisms. In this work, we systematically study the MVTs of hundreds of X-ray bursts (XRBs) from SGR J1935+2154 observed by {\it Insight}-HXMT, GECAM and Fermi/GBM from July 2014 to Jan 2022 through the Bayesian Block algorithm. We find that the MVTs peak at $\sim$ 2 ms, corresponding to a light travel time size of about 600 km, which supports the magnetospheric origin in pulsar-like models. The shock radius and the Lorentz factor of the jet are also constrained in GRB-like models. Interestingly, the MVT of the XRB associated with FRB 200428 is $\sim$ 70 ms, which is longer than that of most bursts and implies its special radiation mechanism. Besides, the median of MVTs is 7 ms, shorter than the median MVTs of 40 ms and 480 ms for short GRBs or long GRBs, respectively. However, the MVT is independent of duration, similar to GRBs. Finally, we investigate the energy dependence of MVT and suggest that there is a marginal evidence for a power-law relationship like GRBs but the rate of variation is at least about an order of magnitude smaller. These features may provide an approach to identify bursts with a magnetar origin.Comment: accepted for publication in ApJ

Baseline serum uric acid level is associated with progression-free survival, disease control rate, and safety in postoperative patients with colorectal cancer treated by FOLFOX, FOLFIRI, or XELOX

BackgroundHigh serum uric acid (SUA) levels increase the risk of overall cancer morbidity and mortality, particularly for digestive malignancies. Nevertheless, the correlation between SUA level and clinical outcomes of the postoperative patients with colorectal cancer (CRC) treated by chemotherapy is unclear. This study aimed at exploring the relationship between baseline SUA level and progression-free survival (PFS), disease control rate (DCR), and safety in postoperative CRC patients receiving chemotherapy.Patients and MethodsWe conducted a retrospective study to evaluate the relationship between baseline SUA level and PFS, DCR, and incidence of serious adverse events of 736 postoperative CRC patients treated with FOLFOX, FOLFIRI or XELOX at our center.ResultsData from our center suggested that high baseline SUA level is linked to poor PFS in non-metastatic CRC patients using FOLFOX (HR=2.59, 95%CI: 1.29-11.31, p=0.018) and in male patients using FOLFIRI (HR=3.77, 95%CI: 1.57-39.49, p=0.012). In patients treated by FOLFIRI, a high SUA is also linked to a low DCR (p=0.035). In patients using FOLFOX, high baseline SUA level is also linked to a high incidence of neutropenia (p=0.0037). For patients using XELOX, there is no significant correlation between SUA level and PFS, effectiveness, or safety.ConclusionsThese findings imply that a high SUA level is a promising biomarker associated with poor PFS, DCR and safety of postoperative CRC patients when treated with FOLFOX or FOLFIRI

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Highly Accurate Prediction of Protein-Protein Interactions via Incorporating Evolutionary Information and Physicochemical Characteristics

Protein-protein interactions (PPIs) occur at almost all levels of cell functions and play crucial roles in various cellular processes. Thus, identification of PPIs is critical for deciphering the molecular mechanisms and further providing insight into biological processes. Although a variety of high-throughput experimental techniques have been developed to identify PPIs, existing PPI pairs by experimental approaches only cover a small fraction of the whole PPI networks, and further, those approaches hold inherent disadvantages, such as being time-consuming, expensive, and having high false positive rate. Therefore, it is urgent and imperative to develop automatic in silico approaches to predict PPIs efficiently and accurately. In this article, we propose a novel mixture of physicochemical and evolutionary-based feature extraction method for predicting PPIs using our newly developed discriminative vector machine (DVM) classifier. The improvements of the proposed method mainly consist in introducing an effective feature extraction method that can capture discriminative features from the evolutionary-based information and physicochemical characteristics, and then a powerful and robust DVM classifier is employed. To the best of our knowledge, it is the first time that DVM model is applied to the field of bioinformatics. When applying the proposed method to the Yeast and Helicobacter pylori (H. pylori) datasets, we obtain excellent prediction accuracies of 94.35% and 90.61%, respectively. The computational results indicate that our method is effective and robust for predicting PPIs, and can be taken as a useful supplementary tool to the traditional experimental methods for future proteomics research

Pressure fluctuation prediction in pump mode using large eddy simulation and unsteady Reynolds-averaged Navier–Stokes in a pump–turbine

For pump–turbines, most of the instabilities couple with high-level pressure fluctuations, which are harmful to pump–turbines, even the whole units. In order to understand the causes of pressure fluctuations and reduce their amplitudes, proper numerical methods should be chosen to obtain the accurate results. The method of large eddy simulation with wall-adapting local eddy-viscosity model was chosen to predict the pressure fluctuations in pump mode of a pump–turbine compared with the method of unsteady Reynolds-averaged Navier–Stokes with two-equation turbulence model shear stress transport k–ω . Partial load operating point (0.91 Q BEP ) under 15-mm guide vane opening was selected to make a comparison of performance and frequency characteristics between large eddy simulation and unsteady Reynolds-averaged Navier–Stokes based on the experimental validation. Good agreement indicates that the method of large eddy simulation could be applied in the simulation of pump–turbines. Then, a detailed comparison of variation for peak-to-peak value in the whole passage was presented. Both the methods show that the highest level pressure fluctuations occur in the vaneless space. In addition, the propagation of amplitudes of blade pass frequency, 2 times of blade pass frequency, and 3 times of blade pass frequency in the circumferential and flow directions was investigated. Although the difference exists between large eddy simulation and unsteady Reynolds-averaged Navier–Stokes, the trend of variation in different parts is almost the same. Based on the analysis, using the same mesh (8 million), large eddy simulation underestimates pressure characteristics and shows a better result compared with the experiments, while unsteady Reynolds-averaged Navier–Stokes overestimates them