Integration of gut microbiome and serum metabolome revealed the effect of Qing-Wei-Zhi-Tong Micro-pills on gastric ulcer in rats.

ETHNOPHARMACOLOGICAL RELEVANCE: Qing-Wei-Zhi-Tong Micro-pills (QWZT) is herbal compound used in the treatment of GU, whose functions include clearing the stomach and fire, softening the liver and relieving pain. However, its mechanistic profile on host intestinal microbiota and metabolism has not been determined. AIM OF THE STUDY: The present study aimed to observe the healing effect of QWZT on acetic acid-induced gastric ulcer in a rat model and to preliminarily elucidate its possible therapeutic mechanism from the perspective of host intestinal microbiota and metabolism. MATERIALS AND METHODS: The Wistar male rats (7 weeks old; weight 180-200 g) were randomly divided into normal control group (NC), acetic acid-induced gastric ulcer group (GU), and QWZT treatment group (High dose: 1250 mg/kg/day, Middle dose: 625 mg/kg/day, Low dose: 312.5 mg/kg/day) of 6 rats each. An acetic acid-induced gastric ulcer rat model was constructed based on anatomical surgery. QWZT (High dose, Middle dose, and Low dose) was used to treat gastric ulcer rats for 7 days by gavage. At the end of treatment, the body weight, macroscopic condition of gastric tissue ulcers, pathological changes (HE staining), inflammatory factors, oxidative stress factors, and endocrine factors were assessed in each group of rats. Fresh feces and serum from each group of rats were collected for microbiome and metabolome analysis on the machine, respectively. Drug-disease common targets and functional pathways were captured based on network pharmacology. The complex network of Herbs-Targets-Pathways-Metabolites-Microbiota interactions was constructed. Ultimately, Fecal Microbiota Transplantation (FMT) evaluated the contribution of gut microbiota in disease. RESULTS: QWZT increased the abundance of beneficial bacteria (Bacteroides, Alloprevotella, Rikenellaceae_RC9_gut_group, Lactobacillus, Lachnospiraceae_NK4A136_group, Parabacteroides, etc.), reduced the abundance of harmful bacteria (Micromonospora, Geobacter, Nocardioides, and Arenimonas, etc.), reduced the levels of inflammatory mediators (12,13-EpOME, 9,10-Epoxyoctadecenoic acid, SM(d18:1/16:0) and Leukotriene A4, etc.), restored host metabolic disorders (Linoleic acid metabolism, Glycerophospholipid metabolism, and Arachidonic acid metabolism), and regulated the level of cytokines (IL-6, TNF-a, SOD, MDA, PEG-2 and NO), ultimately exerting an anti-ulcer effect. Apart from that, FMT improved acetic acid-induced gastric ulcers in rats. CONCLUSION: QWZT improved acetic acid-induced gastric ulcers in rats by remodeling intestinal microbiota and regulating host metabolism. This work may promote the process of developing and utilizing clinical applications of QWZT.

Enhanced high-temperature electrochemical properties of in situ carbon-coated truncated octahedral LiMn2O4 cathodes.

Truncated octahedral spinel LiMn2O4 was homogenously coated by amorphous carbon layer via chemical vapor deposition (CVD) using acetylene gas (C2H2) as carbon source to ease Mn dissolution to improve high-temperature performance, delivering a capacity retention of 92.9% after 1000 cycles at 5C at 50 °C.

Chronic exposure to low-level lipopolysaccharide dampens influenza-mediated inflammatory response via A20 and PPAR network.

Influenza A virus (IAV) infection leads to severe inflammation, and while epithelial-driven inflammatory responses occur via activation of NF-κB, the factors that modulate inflammation, particularly the negative regulators are less well-defined. In this study we show that A20 is a crucial molecular switch that dampens IAV-induced inflammatory responses. Chronic exposure to low-dose LPS environment can restrict this excessive inflammation. The mechanisms that this environment provides to suppress inflammation remain elusive. Here, our evidences show that chronic exposure to low-dose LPS suppressed IAV infection or LPS stimulation-induced inflammation in vitro and in vivo. Chronic low-dose LPS environment increases A20 expression, which in turn positively regulates PPAR-α and -γ, thus dampens the NF-κB signaling pathway and NLRP3 inflammasome activation. Knockout of A20 abolished the inhibitory effect on inflammation. Thus, A20 and its induced PPAR-α and -γ play a key role in suppressing excessive inflammatory responses in the chronic low-dose LPS environment.

On-the-fly parameter estimation based on item response theory in item-based adaptive learning systems.

Online learning systems are able to offer customized content catered to individual learner's needs, and have seen growing interest from industry and academia alike in recent years. In contrast to the traditional computerized adaptive testing setting, which has a well-calibrated item bank with new items added periodically, the online learning system has two unique features: (1) the number of items is large, and they have likely not gone through costly field testing for item calibration; and (2) the individual's ability may change as a result of learning. The Elo rating system has been recognized as an effective method for fast updating of item and person parameters in online learning systems to enable personalized learning. However, the updating parameter in Elo has to be tuned post hoc, and Elo is only suitable for the Rasch model. In this paper, we propose the use of a moment-matching Bayesian update algorithm to estimate item and person parameters on the fly. With sequentially updated item and person parameters, a modified maximum posterior weighted information criterion (MPWI) is proposed to adaptively assign items to individuals. The Bayesian updated algorithm along with MPWI is validated in a simulated multiple-session online learning setting, and the results show that the new combo can achieve fast and reasonably accurate parameter estimations that are comparable to random selection, match-difficulty selection, and traditional online calibration. Moreover, the combo can still function reasonably well with as low as 20% of items being pre-calibrated in the item bank.

Recent Advances in Nanomaterials for Asthma Treatment.

Asthma is a chronic airway inflammatory disease with complex mechanisms, and these patients often encounter difficulties in their treatment course due to the heterogeneity of the disease. Currently, clinical treatments for asthma are mainly based on glucocorticoid-based combination drug therapy; however, glucocorticoid resistance and multiple side effects, as well as the occurrence of poor drug delivery, require the development of more promising treatments. Nanotechnology is an emerging technology that has been extensively researched in the medical field. Several studies have shown that drug delivery systems could significantly improve the targeting, reduce toxicity and improve the bioavailability of drugs. The use of multiple nanoparticle delivery strategies could improve the therapeutic efficacy of drugs compared to traditional delivery methods. Herein, the authors presented the mechanisms of asthma development and current therapeutic methods. Furthermore, the design and synthesis of different types of nanomaterials and micromaterials for asthma therapy are reviewed, including polymetric nanomaterials, solid lipid nanomaterials, cell membranes-based nanomaterials, and metal nanomaterials. Finally, the challenges and future perspectives of these nanomaterials are discussed to provide guidance for further research directions and hopefully promote the clinical application of nanotherapeutics in asthma treatment.

Research Progress of Metabolomics in Asthma.

Asthma is a highly heterogeneous disease, but the pathogenesis of asthma is still unclear. It is well known that the airway inflammatory immune response is the pathological basis of asthma. Metabolomics is a systems biology method to analyze the difference of low molecular weight metabolites (<1.5 kDa) and explore the relationship between metabolic small molecules and pathophysiological changes of the organisms. The functional interdependence between immune response and metabolic regulation is one of the cores of the body's steady-state regulation, and its dysfunction will lead to a series of metabolic disorders. The signal transduction effect of specific metabolites may affect the occurrence of the airway inflammatory immune response, which may be closely related to the pathogenesis of asthma. Emerging metabolomic analysis may provide insights into the pathogenesis and diagnosis of asthma. The review aims to analyze the changes of metabolites in blood/serum/plasma, urine, lung tissue, and exhaled breath condensate (EBC) samples, and further reveals the potential pathogenesis of asthma according to the disordered metabolic pathways.

The Mechanism behind Influenza Virus Cytokine Storm.

Influenza viruses are still a serious threat to human health. Cytokines are essential for cell-to-cell communication and viral clearance in the immune system, but excessive cytokines can cause serious immune pathology. Deaths caused by severe influenza are usually related to cytokine storms. The recent literature has described the mechanism behind the cytokine-storm network and how it can exacerbate host pathological damage. Biological factors such as sex, age, and obesity may cause biological differences between different individuals, which affects cytokine storms induced by the influenza virus. In this review, we summarize the mechanism behind influenza virus cytokine storms and the differences in cytokine storms of different ages and sexes, and in obesity.

Airway Microbiome and Serum Metabolomics Analysis Identify Differential Candidate Biomarkers in Allergic Rhinitis.

Allergic rhinitis (AR) is a common heterogeneous chronic disease with a high prevalence and a complex pathogenesis influenced by numerous factors, involving a combination of genetic and environmental factors. To gain insight into the pathogenesis of AR and to identity diagnostic biomarkers, we combined systems biology approach to analyze microbiome and serum composition. We collected inferior turbinate swabs and serum samples to study the microbiome and serum metabolome of 28 patients with allergic rhinitis and 15 healthy individuals. We sequenced the V3 and V4 regions of the 16S rDNA gene from the upper respiratory samples. Metabolomics was used to examine serum samples. Finally, we combined differential microbiota and differential metabolites to find potential biomarkers. We found no significant differences in diversity between the disease and control groups, but changes in the structure of the microbiota. Compared to the HC group, the AR group showed a significantly higher abundance of 1 phylum (Actinobacteria) and 7 genera (Klebsiella, Prevotella and Staphylococcus, etc.) and a significantly lower abundance of 1 genus (Pelomonas). Serum metabolomics revealed 26 different metabolites (Prostaglandin D2, 20-Hydroxy-leukotriene B4 and Linoleic acid, etc.) and 16 disrupted metabolic pathways (Linoleic acid metabolism, Arachidonic acid metabolism and Tryptophan metabolism, etc.). The combined respiratory microbiome and serum metabolomics datasets showed a degree of correlation reflecting the influence of the microbiome on metabolic activity. Our results show that microbiome and metabolomics analyses provide important candidate biomarkers, and in particular, differential genera in the microbiome have also been validated by random forest prediction models. Differential microbes and differential metabolites have the potential to be used as biomarkers for the diagnosis of allergic rhinitis.

Reliability assessment of repairable closed-loop process systems under uncertainties.

System reliability assessment plays a crucial role in making maintenance decisions and reducing hazard frequencies. Although many engineering methods can effectively evaluate the process reliability, most of them are often unreasonable for closed-loop systems because of the combination of closed-loop structures, maintenance characteristics, and dynamic failure mechanisms. Also, uncertainties generally exist in the reliability assessment due to the insufficient reliability data and expert knowledge. Therefore, an integrated approach is proposed in present works to assess the dynamic reliability of repairable closed-loop systems with the consideration of uncertainties. Firstly, Bayesian inference and fuzzy theorem are developed to characterize system uncertainties and estimate lifetime parameters of components. After that, a closed-loop probabilistic reliability assessment (CPRA) method is proposed for the dynamic reliability assessment of closed-loop systems by integrating cyclic Bayesian network modeling and dynamic Bayesian network solving. Besides, a novel non-probabilistic reliability assessment (NPRA) approach based on the probabilistic method and Monte Carlo simulation is presented to make maintenance decisions for repairable systems. Finally, an application of reliability assessment for the offshore crude oil separation system is introduced to verify the proposed methods.

Sample Size Requirements for Estimation of Item Parameters in the Multidimensional Graded Response Model.

Likert types of rating scales in which a respondent chooses a response from an ordered set of response options are used to measure a wide variety of psychological, educational, and medical outcome variables. The most appropriate item response theory model for analyzing and scoring these instruments when they provide scores on multiple scales is the multidimensional graded response model (MGRM) A simulation study was conducted to investigate the variables that might affect item parameter recovery for the MGRM. Data were generated based on different sample sizes, test lengths, and scale intercorrelations. Parameter estimates were obtained through the flexMIRT software. The quality of parameter recovery was assessed by the correlation between true and estimated parameters as well as bias and root-mean-square-error. Results indicated that for the vast majority of cases studied a sample size of N = 500 provided accurate parameter estimates, except for tests with 240 items when 1000 examinees were necessary to obtain accurate parameter estimates. Increasing sample size beyond N = 1000 did not increase the accuracy of MGRM parameter estimates.