Early probiotic supplementation and the risk of celiac disease in children at genetic risk

Probiotics are linked to positive regulatory effects on the immune system. The aim of the study was to examine the association between the exposure of probiotics via dietary supplements or via infant formula by the age of 1 year and the development of celiac disease autoimmunity (CDA) and celiac disease among a cohort of 6520 genetically susceptible children. Use of probiotics during the first year of life was reported by 1460 children. Time-to-event analysis was used to examine the associations. Overall exposure of probiotics during the first year of life was not associated with either CDA (n = 1212) (HR 1.15; 95%CI 0.99, 1.35; p = 0.07) or celiac disease (n = 455) (HR 1.11; 95%CI 0.86, 1.43; p = 0.43) when adjusting for known risk factors. Intake of probiotic dietary supplements, however, was associated with a slightly increased risk of CDA (HR 1.18; 95%CI 1.00, 1.40; p = 0.043) compared to children who did not get probiotics. It was concluded that the overall exposure of probiotics during the first year of life was not associated with CDA or celiac disease in children at genetic risk.  </div

Isolation and charaterization of stem cell populations in the periodontium.

Stem cells represent promising candidates for tissue engineering due to their capacity for self-renewal and their potential for differentiating into multiple cell lineages. The periodontal tissues are composed of various cell types, such as periodontal ligament fibroblasts, osteoblasts, cementoblasts, endothelial cells, the Epithelial Cell Rests of Malassez (ERM). Studies have previously identified periodontal ligament stem cells (PDLSC) within these tissues, which have the capacity to form periodontal ligament, cementum and bone. Another potential source of progenitor cells described in periodontal tissues are ERM, which are the only odontogenic epithelial cells in the adult periodontium. The present study identified that ERM contained a unique multipotential stem cell population with similar properties as described for PDLSC. Furthermore, the present proposal investigated the cell surface protein expression of PDLSC to identify unique markers for the isolation and purification of PDLSC. The present study demonstrated that ovine Epithelial Cell Rests of Malassez contain a subpopulation of stem cells that could undergo epithelial-mesenchymal transition into mesenchymal stem-like cells with multi lineage potential. Ex vivo-expanded ERM expressed both epithelial (cytokeratin-8, E-cadherin and Epithelial Membrane Protein-1) and bone marrow stromal/stem cell markers (CD44, CD29, Heat Shock Protein-90β). Integrin α₆/CD49f could be used for the enrichment of clonogenic cell clusters (colony-forming units-epithelial cells [CFU-Epi]) which was weakly expressed by PDLSC. Importantly, ERM demonstrated a capacity to differentiation into bone, fat, cartilage and neural cells in vitro, and form bone, cementum-like and Sharpey’s fibre-like structures when transplanted into immunocompromised mice. Additionally, gene expression studies showed that osteogenic induction of ERM triggered an epithelial-mesenchymal transition. The present study also examined the cell surface protein expression of human PDLSC using CyDye cell surface labelling and two-dimensional electrophoresis coupled with liquid chromatography--electrospray-ionization tandem mass spectrometry. In addition to the expression of well known mesenchymal stem cell associated cell surface antigens such as CD73 (ecto-5'-nucleotidase) and CD90 (Thy-1), PDLSC were also found to express two novel cell surface proteins, Annexin A2 and sphingosine kinase 1. Interestingly, previous studies have implicated CD73, CD90, Annexin A2 and sphingosine kinase 1 expression in the maintenance of various stem cell populations. Comparative analyses investigated the expression of CD73, CD90, Annexin A2 and sphingosine kinase-1 in human gingival fibroblasts, human keratinocytes, ovine PDLSC and ovine ERM cells. Importantly, this study found that human skin epithelial cells lacked any cell surface expression for CD73, CD90 and Annexin A2. In summary, ERM and PDLSC are both important stem cell sources that could play a pivotal role in periodontal homeostasis and regeneration following insult or disease. As periodontal regeneration is essentially a re-enactment of the periodontal tissue development process, it is plausible to suggest that the combination of ERM and PDLSC would hold greater potential for periodontal regeneration compared to established bone marrow-derived mesenchymal stem cells.Thesis (Ph.D.) -- University of Adelaide, School of Dentistry, 201

Change of Consumption Behaviours in the Pandemic of COVID-19: Examining Residents’ Consumption Expenditure and Driving Determinants

This study investigated changes of individuals’ consumption behaviours during the COVID-19 pandemic and explored the driving determinants in consumption expenditure in Zhejiang China. Based on the 454 samples of survey data, which were collected in 2020 and 2021, it showed a reduction trend in consumption expenditure during the pandemic. Compared to the consumptions before the pandemic, money spent on housing, food, and beverage did not change too much. However, expenditures on wearing, recreation, and education reduced. Age, family size, and household income were significant to the expenditure changes. Online shopping became an important alternative way for residents during the pandemic and the trend is expected to continue even after the pandemic. Based on the findings, suggestions are summarized as two points. First, the young and single residents are the main group for recovering the consumption for wearing, recreation, education, and public transport. Meanwhile, to improve the satisfactions in online shopping, regulations should be issued by the government in improving the quality of goods and service

Quantifying the nonlinear response of vegetation greening to driving factors in Longnan of China based on machine learning algorithm

The main influencing factors and their nonlinear effects on the changes of vegetation in China’s mountainous areas under the interaction of different factors are not yet clear, and comprehending the evolutionary trends and driving mechanisms of vegetation is crucial to reveal the changes in ecosystem structure and function. In this study, trend analysis (M−K, T-S and EEMD) combined with machine learning algorithm, namely Boosted Regression Tree model (BRT), were used to quantify the trends of nonlinear responses and thresholds for bioclimatic variables, topography, soil properties and anthropogenic factors for vegetation changes in Longnan. The results showed that the trend analysis clearly confirm the increasing trend of vegetation at multiple spatio-temporal scales. The BRT indicated that total precipitation (bio12, 15.22%), land use (LUCC, 12.68%), elevation (DEM, 11.20%), and population density (Pd, 9.20%) were the more important factors of dominant vegetation greening. Bioclimatic variables were found to revealed the effects of climate with vegetation more clearly. In addition, the BRT revealed that the selected factors have the different nonlinear response relationships to vegetation greening trend and specific thresholds. Among them, increasing of cropland, grassland and forestland can promote vegetation greening. However, GDP, Pd, DEM, bio12, mean diurnal range and temperature seasonality (bio2, bio4) exceed the threshold can significantly inhibit vegetation growth. The BRT combined with trend analysis revealed the nonlinear response relationships and thresholds of the drivers behind the vegetation change patterns, which have obvious effects in exploring the driving mechanisms of vegetation changes in mountainous areas. This study provided an important reference for better revealing the interaction mechanisms between vegetation changes and drivers in the semi-humid zone in East Asia even globally

Investigation of the Cell Surface Proteome of Human Periodontal Ligament Stem Cells

The present study examined the cell surface proteome of human periodontal ligament stem cells (PDLSC) compared to human fibroblasts. Cell surface proteins were prelabelled with CyDye before processing to extract the membrane lysates, which were separated using 2D electrophoresis. Selected differentially expressed protein “spots” were identified using Mass spectrometry. Four proteins were selected for validation: CD73, CD90, Annexin A2, and sphingosine kinase 1 previously associated with mesenchymal stem cells. Flow cytometric analysis found that CD73 and CD90 were highly expressed by human PDLSC and gingival fibroblasts but not by keratinocytes, indicating that these antigens could be used as potential markers for distinguishing between mesenchymal cells and epithelial cell populations. Annexin A2 was also found to be expressed at low copy number on the cell surface of human PDLSC and gingival fibroblasts, while human keratinocytes lacked any cell surface expression of Annexin A2. In contrast, sphingosine kinase 1 expression was detected in all the cell types examined using immunocytochemical analysis. These proteomic studies form the foundation to further define the cell surface protein expression profile of PDLSC in order to better characterise this cell population and help develop novel strategies for the purification of this stem cell population

PII: S0092-8674(02)01115-7

enzyme is an oligomer, it was suggested that one subtemplate. The 3.0 Å resolution crystal structures of the unit might be specific for adding two C&apos;s with scrunching CCA-adding enzyme from Bacillus stearothermophiat the 3Ј terminus, after which the 3Ј terminus would lus and its complexes with ATP or CTP reveal a seashuttle to a second subunit that was specific for adding horse-shaped subunit consisting of four domains: A the NT superfamily; these are the CCA-adding enzyme, CCA-adding activity is highly conserved throughout evolu-PAP, and TdT. Of these three, the CCA-adding enzyme tion, and the activity has been identified in all three exhibits the greatest specificity by recognizing and inkingdom

Atrial Fibrillation Detection by the Combination of Recurrence Complex Network and Convolution Neural Network

In this paper, R wave peak interval independent atrial fibrillation detection algorithm is proposed based on the analysis of the synchronization feature of the electrocardiogram signal by a deep neural network. Firstly, the synchronization feature of each heartbeat of the electrocardiogram signal is constructed by a Recurrence Complex Network. Then, a convolution neural network is used to detect atrial fibrillation by analyzing the eigenvalues of the Recurrence Complex Network. Finally, a voting algorithm is developed to improve the performance of the beat-wise atrial fibrillation detection. The MIT-BIH atrial fibrillation database is used to evaluate the performance of the proposed method. Experimental results show that the sensitivity, specificity, and accuracy of the algorithm can achieve 94.28%, 94.91%, and 94.59%, respectively. Remarkably, the proposed method was more effective than the traditional algorithms to the problem of individual variation in the atrial fibrillation detection