DSS Model Research Articles

S1669 Gl1001 Attenuation of Acute Colitis in a Mouse DSS Model Is Associated with Inhibition of Its Target, ACE2

S1669 Gl1001 Attenuation of Acute Colitis in a Mouse DSS Model Is Associated with Inhibition of Its Target, ACE2

Design, Synthesis, and Pharmacological Effects of a Cyclization-Activated Steroid Prodrug for Colon Targeting in Inflammatory Bowel Disease

Glucocorticoids are used in the treatment of inflammatory bowel disease. A limitation to their use is that they undergo absorption from the GIT before reaching the colon causing severe systemic side effects. We report here on a novel prodrug approach to targeting corticosteroids to the colon. The design involves attaching a 21-ester group that suppresses absorption during transit to the colon. The prodrug is designed to be primed by colonic microflora liberating an amino ester that cyclizes releasing the steroid. One of the prodrugs 5b was as efficacious as prednisolone in the murine DSS model but did not cause thymic atrophy, a marker for systemic steroid effects.

Resolvin E1‐induced Intestinal Alkaline Phosphatase contributes to inflammation resolution via LPS detoxification

Inflammatory bowel diseases are disorders of unknown etiology characterized by inappropriate inflammation. There is recent intense interest in understanding mechanisms of inflammatory resolution. Resolvin‐E1 (RvE1) has been demonstrated to promote resolution in numerous disease models. Given the importance of epithelial cells in coordinating mucosal inflammation, we hypothesized that RvE1 elicits an epithelial inflammatory resolution signature.Microarray profiling of epithelial cells exposed to RvE1, revealed transcriptional induction of numerous inflammatory response genes. Notably, RvE1 induced intestinal alkaline phosphatase (ALPI) expression and enhanced epithelial ALPI enzymatic activity. ALPI was recently demonstrated to detoxify bacterial LPS. In our studies, both purified ALPI and "conditioned" LPS‐containing media derived from RvE1‐exposed epithelia were demonstrated to detoxify LPS. To define these features in vivo, we employed a murine DSS model of colitis. Compared to vehicle controls, administration of RvE1 resulted in significant improvement of disease activity indices. RvE1 treatment resulted in significantly increased ALPI expression in murine intestinal epithelial cells. Moreover, disease severity inversely correlated with ALPI expression. Taken together, these data implicate a previously unappreciated role for ALPI in RvE1 mediated inflammatory resolution.

ADRENOMEDULLIN (ADM) ACTS AS AN ANTI‐INFLAMMATORY MEDIATOR THROUGH CONCOMITANT DE‐NEDDYLATION OF CUL‐1 AND CUL‐2.

Local hypoxia (HX) has been identified as an anti‐inflammatory mediator through induction of HIF‐ dependent genes. Microarray analysis of HMECs exposed to HX identified ADM as a prominent HX‐inducible gene. ADM has been shown to diminish the severity of inflammation in a number of models. We sought to determine the molecular pathways which converge to confer an anti‐inflammatory phenotype. rhADM treatment resulted in a de‐neddylation of Cullin family members, reported to control NF¿B & HIF activity, respectively. NF¿B & HRE luciferase reporter assays confirmed suppression of NF¿B & activation of HRE by ADM. To address ADM signaling on HIF‐1 activation, we identified an increase in canonical HIF‐1 dependent genes following stimulation with ADM. As an in vivo model, we investigated the role of ADM in treating a DSS model of colitis. Mice treated with ADM had reduced inflammatory indices compared with sham‐ treated DSS mice, which lost more weight & had significant shortening of the colon compared with healthy controls. Furthermore, histological analysis identified less inflammation in ADM treated mice compared to sham‐treated. Whole colon & serum cytokine analysis showed a marked inhibition of DSS colitis‐associated TNF‐α, IL‐1β & KC. Together, these results demonstrate that ADM may act as an administrable therapy to reduce the severity of inflammation through the central action on Cullin family members.

Alterations in Mucosal Cytokine levels reflect acute inflammation but not chronic inflammation in the Murine DSS model of Colitis

Alterations in Mucosal Cytokine levels reflect acute inflammation but not chronic inflammation in the Murine DSS model of Colitis

Alterations in Mucosal Cytokine levels reflect acute inflammation but not chronic inflammation in the Murine DSS model of Colitis

Alterations in Mucosal Cytokine levels reflect acute inflammation but not chronic inflammation in the Murine DSS model of Colitis

S1739 Bilirubin Suppresses Intestinal Inflammation in the DSS Model of Acute Colitis in Mice

S1739 Bilirubin Suppresses Intestinal Inflammation in the DSS Model of Acute Colitis in Mice

Predictive models for reduction of Salmonella Hadar on chicken skin during single and double sequential spraying treatments with acetic acid

The response surface methodology was used to evaluate the effect of acetic acid concentration, spraying time and temperature on the reduction of Salmonella Hadar on poultry skin in a laboratory spraying process, and to identify the best conditions required to develop this operation. A comparative analysis was carried out to ascertain the effects of the application of single (SS) and double sequential decontamination (DSS) treatments on skin samples inoculated with Salm. Hadar. While on the SS treatment, the linear and quadratic acid concentration terms and the interaction of the temperature and time term of the model are statistically significant at P < or = 0.001, P < or = 0.01 and P < or = 0.05, respectively, the other terms do not significantly affect (P > 0.05) the reduction of Salm. Hadar. On the DSS model the acid concentration and time linear terms significantly affected (P < or = 0.001 and P < or = 0.01) the Salm. Hadar reduction within the experimental range assayed. Any of the models could be used as an approach to optimize spray washing during chicken processing. Neither the SS or the DSS treatment has the capability of eliminating Salm. Hadar from carcasses. However, reductions of approx. 99% initial load could be attained if DSS treatment were put into practice.

Time-series modeling of fishery landings using ARIMA models and Fuzzy Expected Intervals software

Forecasting, using historic time-series data, has become an important tool for fisheries management. ARIMA modeling, Modeling for Optimal Forecasting techniques and Decision Support Systems based on fuzzy mathematics may be used to predict the general trend of a given fish landings time-series with increased reliability and accuracy. The present paper applies these three modeling methods to forecast anchovy fish catches landed in a given port (Thessaloniki, Greece) during 1979–2000 and hake and bonito total fish catches during 1982–2000. The paper attempts to assess the model's accuracy by comparing model results to the actual monthly fish catches of the year 2000. According to the measures of forecasting accuracy established, the best forecasting performance for anchovy was shown by the DSS model (MAPE = 28.06%, RMSE = 76.56, U-statistic = 0.67 and R 2 = 0.69). The optimal forecasting technique of genetic modeling improved significantly the forecasting values obtained by the selected ARIMA model. Similarly, the DSS model showed a noteworthy forecasting efficiency for the prediction of hake landings, during the year 2000 (MAPE = 2.88%, RMSE = 13.75, U-statistic = 0.19 and R 2 = 0.98), as compared to the other two modeling techniques. Optimal forecasting produced by combined modeling scored better than application of the simple ARIMA model. Overall, DSS results showed that the Fuzzy Expected Intervals methodology could be used as a very reliable tool for short-term predictions of fishery landings.

Increased disease activity in eNOS-deficient mice in experimental colitis

Oral dextran sodium sulfate (DSS, 3%) produces experimental colitis with many features of human inflammatory bowel disease (IBD), (leukocyte extravasation, cachexia, and histopathology). Previous studies suggest that the inducible nitric oxide synthase (iNOS) in blood cells or in the endothelium contribute to this injury. However, until now no study has been performed to directly evaluate the role of endothelial nitric oxide synthase (eNOS) in IBD. We compared disease activity in wild-type (eNOS +/+) and eNOS-deficient (eNOS −/−) mice in the DSS model of colitis. Administration of DSS induced weight loss, stool blood, and overt histopathology in both mouse strains. Disease activity was dramatically increased in eNOS −/− mice compared to wild types. Histologically, eNOS-deficient mice had greater leukocyte infiltration, gut injury, and expressed higher levels of the mucosal addressin, MAdCAM-1. These results demonstrate that eNOS plays an important role in limiting injury to the intestine during experimental colitis and altered eNOS content and/or activity may contribute to human IBD.

Linking conceptual and simulation models of the Cooum River: collaborative development of a GIS-based DSS for environmental management

The Cooum River is an extremely polluted stream that flows through Chennai, India. Past attempts to rehabilitate the river have focussed on physical interventions such as dredging. However, the problem continues to worsen. This research has borrowed techniques from adaptive management and soft systems methodology to apply an ecosystem approach to the problem. This involved participatory development of conceptual models of relevant systems that informed construction of a GIS-based DSS and environmental model. This paper addresses the link between conceptual models and the DSS. The process of problem identification, system conceptualization, development of the GIS database and DSS, and its use for exploratory scenario analysis was found to stimulate learning about the situation and promoted novel solutions to the problem.

DSS Model Usage in Public and Private Sectors

DSS Model Usage in Public and Private Sectors

Using non-traditional approaches to statistical classification and regression in DSS model analysis

In a model-based decision support system, the decision maker initially has two primary tasks: finding the key parameters in the model and discovering how those key parameters, both individually and interactively, affect the solution. This paper presents an application of a non-traditional approach to statistical classification and regression to the inductive analysis of model output. Specifically, we describe the application of Ivakhnenko's group method of data handling (GMDH) to the identification of key model parameters and the discovery of a simplified polynomial metamodel, both of which frequently enhance the decision maker's understanding of the modeled environment.

A Decision Support System for Micro-Watershed Management in India

The improvement of economic conditions in developing countries like India is crucially dependent on improvement in agriculture. Crop production depends on the volume of irrigation water and its temporal distribution, water quality, solar radiation, precipitation and a host of soil properties. All these factors vary from crop to crop. In view of the above it is imperative for increasing agricultural production that scarce water resources should have efficient management. It is necessary to evolve an innovative approach for water distribution and management. This paper presents an integrated DSS model for a Micro-Watershed Management System (MWMS) for generating alternative water allocation and agricultural production scenarios for a semi-arid region. The model is tested with data from the Radharamanpur micro-watershed (364 Ha. area) in the Bankura district of West Bengal state in India.

A DSS model for a school of engineering

Although it may be questioned whether OR has failed to make a significant impact on decision processes at the top of the management hierarchy, the author believes it can be appropriate for decision making within subunits of the organization. One such example is the application of OR methodologies to the strategic planning process of a school of engineering. The fact that the university is a service organization with inherently fuzzy data leading to frequently non-quantifiable objectives exacerbated the problem with using typical quantitative OR techniques. An approach was developed that structured the objectives into a hierarchy of mission, goals, objectives, strategies, and actions, all of which could be relatively weighted through input of knowledgeable middle managers within the school. Constraints were incorporated into a 0–1 integer programming model that attempted to maximize the importance for initiation of each action as its importance varied over time.

Capability Criteria for Marketing Decision Support Systems

:Decision support systems (dss) are becoming popular computer information system tools to assist in resolving strategic business problems. This paper reports the results of a laboratory study investigating the usefulness of dss features for marketing problem solving. Marketers perceived dss model interrogation features as most useful during decision making. Simulation capabilities and features enabling probabilistic specification of problem variables were considered least useful. Study results suggest that model building and decision training be included when introducing a dss.

A conceptual architecture for DSS generators

The development of DSS generators is a complicated task. No existing DSS generator has been reported as a generalised, powerful and “user friendly” system, which provides full-range of capabilities for easily building specific DSS in any application area. In order to integrate a variety of decision support and data management capabilities into a well-designed, orderly whole, a conceptual design model must be created as a foundation for developing such software systems. Sprague's DSS model provides a basis for the creation of a foundation for DSS generator development. This paper proposes a comprehensive conceptual design model which is an in-depth augmentation of Sprague's original model. This model not merely provides a basis for developing DSS generators; it also proposes a fundamental architecture of DSS generators which removes some of the responsibilities of DSS design from the user. Further, the conceptual model has been technically validated by implementing an experimental DSS generator REGIMES on a microcomputer. This implementation also demonstrates the feasibility of implementing a powerful DSS generator on cost-effective hardware.