Experimental colitis is mediated by inflammatory or dysregulated immune responses to microbial factors of the gastrointestinal tract. In this study we observed that administration of Toll-like receptor 9 (TLR9) agonists suppressed the severity of experimental colitis in RAG1 -/-but not in SCID mice. This differential responsiveness between phenotypically similar but genetically distinct animals was related to a partial blockade in TLR9 signaling and defective production of type I IFN (i.e., IFN-α/β) in SCID mice upon TLR9 stimulation. The addition of neutralization antibodies against type I IFN abolished the antiinflammatory effects induced by TLR9 agonists, whereas the administration of recombinant IFN-β mimicked the antiinflammatory effects induced by TLR9 agonists in this model. Furthermore, mice deficient in the IFN-α/β receptor exhibited more severe colitis than wild-type mice did upon induction of experimental colitis. These results indicate that TLR9-triggered type I IFN has antiinflammatory functions in colitis. They also underscore the important protective role of type I IFN in intestinal homeostasis and suggest that strategies to modulate innate immunity may be of therapeutic value for the treatment of intestinal inflammatory conditions.

Background: Stress in nursing students may be related to attrition from nursing

Epidemic is a rapid and wide spread of infectious disease threatening many lives and economy damages. It is important to fore-tell the epidemic lifetime so to decide on timely and remedic actions. These measures include closing borders, schools, suspending community services and commuters. Resuming such curfews depends on the momentum of the outbreak and its rate of decay. Being able to accurately forecast the fate of an epidemic is an extremely important but difficult task. Due to limited knowledge of the novel disease, the high uncertainty involved and the complex societal-political factors that influence the widespread of the new virus, any forecast is anything but reliable. Another factor is the insufficient amount of available data. Data samples are often scarce when an epidemic just started. With only few training samples on hand, finding a forecasting model which offers forecast at the best efforts is a big challenge in machine learning. In the past, three popular methods have been proposed, they include 1) augmenting the existing little data, 2) using a panel selection to pick the best forecasting model from several models, and 3) fine-tuning the parameters of an individual forecasting model for the highest possible accuracy. In this paper, a methodology that embraces these three virtues of data mining from a small dataset is proposed. An experiment that is based on the recent coronavirus outbreak originated from Wuhan is conducted by applying this methodology. It is shown that an optimized forecasting model that is constructed from a new algorithm, namely polynomial neural network with corrective feedback (PNN+cf) is able to make a forecast that has relatively the lowest prediction error. The results showcase that the newly proposed methodology and PNN+cf are useful in generating acceptable forecast upon the critical time of disease outbreak when the samples are far from abundant.

In the advent of the novel coronavirus epidemic since December 2019, governments and authorities have been struggling to make critical decisions under high uncertainty at their best efforts. Composite Monte-Carlo (CMC) simulation is a forecasting method which extrapolates available data which are broken down from multiple correlated/casual micro-data sources into many possible future outcomes by drawing random samples from some probability distributions. For instance, the overall trend and propagation of the infested cases in China are influenced by the temporal-spatial data of the nearby cities around the Wuhan city (where the virus is originated from), in terms of the population density, travel mobility, medical resources such as hospital beds and the timeliness of quarantine control in each city etc. Hence a CMC is reliable only up to the closeness of the underlying statistical distribution of a CMC, that is supposed to represent the behaviour of the future events, and the correctness of the composite data relationships. In this paper, a case study of using CMC that is enhanced by deep learning network and fuzzy rule induction for gaining better stochastic insights about the epidemic development is experimented. Instead of applying simplistic and uniform assumptions for a MC which is a common practice, a deep learning-based CMC is used in conjunction of fuzzy rule induction techniques. As a result, decision makers are benefited from a better fitted MC outputs complemented by min-max rules that foretell about the extreme ranges of future possibilities with respect to the epidemic. * Corresponding