Sishen Wan enhances intestinal barrier function via regulating endoplasmic reticulum stress to improve mice with diarrheal irritable bowel syndrome.

BACKGROUND: Diarrheal irritable bowel syndrome (IBS-D), characterized primarily by the presence of diarrhea and abdominal pain, is a clinical manifestation resulting from a multitude of causative factors. Furthermore, Sishen Wan (SSW) has demonstrated efficacy in treating IBS-D. Nevertheless, its mechanism of action remains unclear. METHODS: A model of IBS-D was induced by a diet containing 45 % lactose and chronic unpredictable mild stress. Additionally, the impact of SSW was assessed by measuring body weight, visceral sensitivity, defecation parameters, intestinal transport velocity, intestinal neurotransmitter levels, immunohistochemistry, and transmission electron microscopy analysis. Immunofluorescent staining was used to detect the expression of Mucin 2 (MUC2) and Occludin in the colon. Western blotting was used to detect changes in proteins related to tight junction (TJ), autophagy, and endoplasmic reticulum (ER) stress in the colon. Finally, 16S rRNA amplicon sequencing was used to monitor the alteration of gut microbiota after SSW treatment. RESULTS: Our study revealed that SSW administration resulted in reduced visceral sensitivity, improved defecation parameters, decreased intestinal transport velocity, and reduced intestinal permeability in IBS-D mice. Furthermore, SSW promotes the secretion of colonic mucus by enhancing autophagy and inhibiting ER stress. SSW treatment caused remodeling of the gut microbiome by increasing the abundance of Blautia, Muribaculum and Ruminococcus torques group. CONCLUSION: SSW can improve intestinal barrier function by promoting autophagy and inhibiting ER stress, thus exerting a therapeutic effect on IBS-D.

Shenxiang Suhe pill improves cardiac function through modulating gut microbiota and serum metabolites in rats after acute myocardial infarction.

CONTEXT: Shenxiang Suhe pill (SXSH), a traditional Chinese medicine, is clinically effective against coronary heart disease, but the mechanism of cardiac-protective function is unclear. OBJECTIVE: We investigated the cardiac-protective mechanism of SXSH via modulating gut microbiota and metabolite profiles. MATERIALS AND METHODS: Sprague-Dawley (SD) male rats were randomly divided into 6 groups (n = 8): Sham, Model, SXSH (Low, 0.063 g/kg; Medium, 0.126 g/kg; High, 0.252 g/kg), and Ato (atorvastatin, 20 mg/kg). Besides the Sham group, rats were modelled with acute myocardial infarction (AMI) by ligating the anterior descending branch of the left coronary artery (LAD). After 3, 7, 14 days' administration, ultrasound, H&E staining, serum enzymic assay, 16S rRNA sequencing were conducted to investigate the SXSH efficacy. Afterwards, five groups of rats: Sham, Model, Model-ABX (AMI with antibiotics-feeding), SXSH (0.126 g/kg), SXSH-ABX were administrated for 14 days to evaluate the gut microbiota-dependent SXSH efficacy, and serum untargeted metabolomics test was performed. RESULTS: 0.126 g/kg of SXSH intervention for 14 days increased ejection fraction (EF, 78.22%), fractional shortening (FS, 109.07%), and aortic valve flow velocities (AV, 21.62%), reduced lesion area, and decreased serum LDH (8.49%) and CK-MB (10.79%). Meanwhile, SXSH upregulated the abundance of Muribaculaceae (199.71%), Allobaculum (1744.09%), and downregulated Lactobacillus (65.51%). The cardiac-protective effect of SXSH was disrupted by antibiotics administration. SXSH altered serum metabolites levels, such as downregulation of 2-n-tetrahydrothiophenecarboxylic acid (THTC, 1.73%), and lysophosphatidylcholine (lysoPC, 4.61%). DISCUSSION AND CONCLUSION: The cardiac-protective effect and suggested mechanism of SXSH could provide a theoretical basis for expanding its application in clinic.

Identification of Drug-Induced Liver Injury Biomarkers from Multiple Microarrays Based on Machine Learning and Bioinformatics Analysis.

Drug-induced liver injury (DILI) is the most common adverse effect of numerous drugs and a leading cause of drug withdrawal from the market. In recent years, the incidence of DILI has increased. However, diagnosing DILI remains challenging because of the lack of specific biomarkers. Hence, we used machine learning (ML) to mine multiple microarrays and identify useful genes that could contribute to diagnosing DILI. In this prospective study, we screened six eligible microarrays from the Gene Expression Omnibus (GEO) database. First, 21 differentially expressed genes (DEGs) were identified in the training set. Subsequently, a functional enrichment analysis of the DEGs was performed. We then used six ML algorithms to identify potentially useful genes. Based on receiver operating characteristic (ROC), four genes, DDIT3, GADD45A, SLC3A2, and RBM24, were identified. The average values of the area under the curve (AUC) for these four genes were higher than 0.8 in both the training and testing sets. In addition, the results of immune cell correlation analysis showed that these four genes were highly significantly correlated with multiple immune cells. Our study revealed that DDIT3, GADD45A, SLC3A2, and RBM24 could be biomarkers contributing to the identification of patients with DILI.

Potential roles of gut microbes in biotransformation of natural products: An overview.

Natural products have been extensively applied in clinical practice, characterized by multi-component and multi-target, many pharmacodynamic substances, complex action mechanisms, and various physiological activities. For the oral administration of natural products, the gut microbiota and clinical efficacy are closely related, but this relationship remains unclear. Gut microbes play an important role in the transformation and utilization of natural products caused by the diversity of enzyme systems. Effective components such as flavonoids, alkaloids, lignans, and phenols cannot be metabolized directly through human digestive enzymes but can be transformed by enzymes produced by gut microorganisms and then utilized. Therefore, the focus is paid to the metabolism of natural products through the gut microbiota. In the present study, we systematically reviewed the studies about gut microbiota and their effect on the biotransformation of various components of natural products and highlighted the involved common bacteria, reaction types, pharmacological actions, and research methods. This study aims to provide theoretical support for the clinical application in the prevention and treatment of diseases and provide new ideas for studying natural products based on gut biotransformation.

A new way for handling mobility in longitudinal data.

In the social sciences, applied researchers often face a statistical dilemma when multilevel data is structured such that lower-level units are not purely clustered within higher-level units. To aid applied researchers in appropriately analyzing such data structures, this study proposes a multiple membership growth curve model (MM-GCM). The MM-GCM offers some advantages to other similar modeling approaches, including greater flexibility in modeling the intercept at the time-point most desired for interpretation. A real longitudinal dataset from the field of education with a multiple membership structure, where some students changed schools over time, was used to demonstrate the application of the MM-GCM. Baseline and conditional MM-GCMs are presented, and parameter estimates were compared with two other common approaches to handling such data structures - the final school-GCM that ignores mobile students by only modeling the final school attended and the delete-GCM that deletes mobile students. Additionally, a simulation study was conducted to further assess the impact of ignoring mobility on parameter estimates. The results indicate that ignoring mobility results in substantial bias in model estimates, especially for cluster-level coefficients and variance components.

Developing a Longitudinal Scale for Language: Linking Across Developmentally Different Versions of the Same Test.

Purpose Many language tests use different versions that are not statistically linked or do not have a developmental scaled score. The current article illustrates the problems of scores that are not linked or equated, followed by a statistical model to derive a developmental scaled score. Method Using an accelerated cohort design of 890 students in Grades 1-5, a confirmatory factor model was fit to 6 subtests of the Test of Language Development-Primary and Intermediate: Fourth Edition ( Hammill & Newcomer, 2008a , 2008b ). The model allowed for linking the subtests to a general factor of language and equating their measurement characteristics across grades and cohorts of children. A sequence of models was fit to evaluate the appropriateness of the linking assumptions. Results The models fit well, with reasonable support for the validity of the tests to measure a general factor of language on a longitudinally consistent scale. Conclusion Although total and standard scores were problematic for longitudinal relations, the results of the model suggest that language grows in a relatively linear manner among these children, regardless of which set of subtests they received. Researchers and clinicians interested in longitudinal inferences are advised to design research or choose tests that can provide a developmental scaled score.

The Impact of Dialect Density on the Growth of Language and Reading in African American Children.

Purpose: The goal of the current study was to examine the impact of dialect density on the growth of oral language and reading skills in a sample of African American English (AAE)-speaking children reared in urban communities. Method: Eight hundred thirty-five African American children in first through fifth grades participated. Using an accelerated cohort design, univariate and bivariate growth models were employed to examine dialect density, oral language and reading, and the relationships between these variables. Results: For the univariate models, results indicated that (a) dialect density decreased over time by approximately 5% per year beyond first grade, (b) language skills improved approximately 0.5 SD per year, and (c) reading comprehension increased significantly from first to second grade and slowed 23% per year in second through fifth grades. Results from the bivariate models revealed that (a) dialect density and language ability are negatively associated, although dialect density did not affect change in language over time, and (b) higher dialect density is related to slower growth in reading. Conclusions: Findings from this investigation provide converging evidence for accounts in the extant literature particularly supporting a negative relationship between dialect density and oral language and between dialect density and reading while also contributing novel longitudinal evidence that suggests that changes in dialect use over time may be driven by oral language skills and that reading and dialect have a reciprocal relationship.