Rational Use of Fecal Calprotectin in Irritable Bowel Syndrome and Inflammatory Bowel Disease

The gastrointestinal pathologies have increased over the last years. The clinical pictures of inflammatory and irritable bowel disease might overlap, leading to expensive and invasive tests. Our study aims to investigate fecal calprotectin as an effective tool for differential diagnosis of gastrointestinal disorders. Two hundred fifty-six patients with the diagnosis of the gastrointestinal disorder and subjected to colonoscopy were collected for the statistical analysis of fecal calprotectin. The differential diagnosis of intestinal inflammation or non-inflammation was performed according to the Receiver Operating Characteristic (ROC) curve that outlines the Area Under Curve (AUC), Sensitivity (Se), Specificity (Sp). Fecal calprotectin was significantly elevated in patients with inflammatory bowel disease compared with patients with irritable bowel syndrome. Especially, the mean values of fecal calprotectin were 522 g/g (IQR=215-975) and 21 g/g (IQR=14-34.5) in patients with and without inflammation, respectively (P<0.0001). AUC value of fecal calprotectin was 0.958 (Se=88.9%, Sp=91.1%, with a cut-off value of 50 g/g) for differentiating between inflammatory bowel disease and irritable bowel syndrome. Fecal calprotectin seems to be a non-invasive and inexpensive biomarker useful for the purpose of a differential diagnosis between inflammatory bowel disease and irritable bowel syndrome

Multi-fidelity machine learning models for structure-property mapping of organic electronics

Machine learning approaches have been used with significant success in constructing, curating, and exploring relationships between microstructure and property. However, one major limitation of these approaches is the need for a significant amount of training data consisting of microstructure-property pairs. Getting property values associated with a specific microstructure typically requires deploying a detailed physics simulator which becomes resource-intensive. While using a low(er) fidelity property quantifier can offset the cost of creating the training dataset, there is a trade-off in terms of accuracy/fidelity of the estimated property. Here, we leverage the availability of low- and high- fidelity property simulators to construct a multi-fidelity mapping from microstructure to property using deep convolutional neural networks. Starting with a large dataset of morphologies representing the active layer of organic photovoltaic devices, we assimilate data from a rapid graph-based low-fidelity characterization of the morphology with limited data from a high fidelity excitonic drift-diffusion detailed physics simulator. We show that our method provides significant computational savings while maintaining competitive performance. This work can be easily extended to other applications, and we envision it as a basis for accelerated material quantification and discovery.This is a version of record published as Yang, Chih-Hsuan, Balaji Sesha Sarath Pokuri, Xian Yeow Lee, Sangeeth Balakrishnan, Chinmay Hegde, Soumik Sarkar, and Baskar Ganapathysubramanian. "Multi-fidelity machine learning models for structure–property mapping of organic electronics." Computational Materials Science 213 (2022): 111599. doi: https://doi.org/10.1016/j.commatsci.2022.111599

Modelling the allocation of pallets in a flexible manufacturing cell.

Pallets are an essential part of manufacturing operations. This research concentrates on pallets for machining and, in particular, pallets for use in a flexible manufacturing cells (FMC). Flexible manufacturing has received widespread attention in the modern manufacturing environment. A close look at the research done on different areas of flexible manufacturing shows that the aspect of pallets in the design stage has been neglected. The objective of this research, therefore, is to develop mathematical programming models which deals with the palletizing problem in flexible manufacturing. The models assume that there is a set of existing machines in a manufacturing shop with known compatibilities and capacities to perform operations on the parts to be processed. The demand for each part is assumed uniform over the planning period under consideration. Model 1 considers the problem of grouping parts, machines, and the pallets in a flexible manufacturing environment. Considerations of physical limitations such as upper bounds on machine capacity, pallet time capacity are considered. Model 2 considers the reloading time and the machine-pallet compatibility. The models are formulated as a 0-1 integer programs, and illustrative examples are solved and the results were analyzed.Dept. of Industrial and Manufacturing Systems Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis1997 .B375. Source: Masters Abstracts International, Volume: 37-01, page: 0333. Adviser: R. S. Lashkari. Thesis (M.A.Sc.)--University of Windsor (Canada), 1997

Modelling the allocation of pallets in a flexible manufacturing cell.

Pallets are an essential part of manufacturing operations. This research concentrates on pallets for machining and, in particular, pallets for use in a flexible manufacturing cells (FMC). Flexible manufacturing has received widespread attention in the modern manufacturing environment. A close look at the research done on different areas of flexible manufacturing shows that the aspect of pallets in the design stage has been neglected. The objective of this research, therefore, is to develop mathematical programming models which deals with the palletizing problem in flexible manufacturing. The models assume that there is a set of existing machines in a manufacturing shop with known compatibilities and capacities to perform operations on the parts to be processed. The demand for each part is assumed uniform over the planning period under consideration. Model 1 considers the problem of grouping parts, machines, and the pallets in a flexible manufacturing environment. Considerations of physical limitations such as upper bounds on machine capacity, pallet time capacity are considered. Model 2 considers the reloading time and the machine-pallet compatibility. The models are formulated as a 0-1 integer programs, and illustrative examples are solved and the results were analyzed.Dept. of Industrial and Manufacturing Systems Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis1997 .B375. Source: Masters Abstracts International, Volume: 37-01, page: 0333. Adviser: R. S. Lashkari. Thesis (M.A.Sc.)--University of Windsor (Canada), 1997

The role of microbiome in rheumatoid arthritis treatment

Rheumatoid arthritis (RA) is an autoimmune disorder with multifactorial etiology; both genetic and environmental factors are known to be involved in pathogenesis. Treatment with disease-modifying antirheumatic drugs (DMARDs) plays an essential role in controlling disease progression and symptoms. DMARDs have immunomodulatory properties and suppress immune response by interfering in various pro-inflammatory pathways. Recent evidence has shown that the gut microbiota directly and indirectly modulates the host immune system. RA has been associated with dysbiosis of the gut microbiota. Patients with RA treated with DMARDs show partial restoration of eubiotic gut microbiome. Hence, it is essential to understand the impact of DMARDs on the microbial composition and its consequent influences on the host immune system to identify novel therapies for RA. In this review, we discuss the importance of antirheumatic-drug-induced host microbiota modulations and possible probiotics that can generate eubiosis. </jats:p

Autoimmunity-Associated Gut Commensals Modulate Gut Permeability and Immunity in Humanized Mice

Abstract Objective Although the etiology of rheumatoid arthritis (RA) is unknown, recent studies have led to the concept that gut dysbiosis may be involved in onset. In this study, we aimed to determine if human gut commensals modulate the immune response and gut epithelial integrity in DQ8 mice. Methods DQ8 mice were orally gavaged with RA-associated (Eggerthella lenta or Collinsella aerofaciens) and non-associated (Prevotella histicola or Bifidobacterium sp.) on alternate days for 1 week in naïve mice. Some mice were immunized with type II collagen and oral gavage continued for 6 weeks and followed for arthritis. Epithelial integrity was done by FITC-Dextran assay. In addition, cytokines were measured in sera by ELISA and various immune cells were quantified using flow cytometry. Results Gut permeability was increased by the RA-associated bacteria and was sex and age-dependent. In vivo and in vitro observations showed that the RA-non-associated bacteria outgrow the RA-associated bacteria when gavaged or cultured together. Mice gavaged with the RA-non-associated bacteria produced lower levels of pro-inflammatory MCP-1 and MCP-3 and had lower numbers of Inflammatory monocytes CD11c+Ly6c+, when compared to controls. E. lenta treated naïve mice produce Th17 cytokines. Conclusions Our studies suggest that gut commensals influence immune response in and away from the gut by changing the gut permeability and immunity. Dysbiosis helps the growth of RA-associated bacteria and reduces the beneficial bacteria. </jats:sec

<i>Prevotella histicola</i> treatment reduces arthritic pain and partially normalizes gut microbiota and metabolites

Abstract We have previously shown that treatment with a novel human commensal, Prevotella histicola, isolated from a duodenal biopsy, can protect HLA-DQ8 mice from collagen induced arthritis (CIA), a model for RA, by modulating the systemic immune response. The present study analyzed the functional and mechanistic impact of P. histicola by comparing pain, motor function, gut microbiota and metabolites in treated and non-treated arthritic DQ8 mice. P. histicola treatment reduced the arthritis-associated pain by improving the motor function when compared to control mice. Analysis of synovium showed that P. histicola treatment increased T regulatory cells and reduced the levels of IL6 in the synovium of arthritic mice which were associated with reduced pain. Treated mice showed P. histicola colonization in the duodenum, a niche from where it was isolated. Further, P. histicola treatment showed varied effects based on circadian rhythm. A longitudinal follow-up of the gut microbial profile in different gut sections suggested that dysbiosis caused due to arthritis in DQ8 is partly normalized to naïve profile after treatment with P. histicola. Treated mice showed an expansion of genus Allobaculum, a butyrate-producing Firmicutes in the guts of DQ8 arthritic mice as compared to pre-treatment. The post-treatment restoration was also reflected in short-chain fatty acids and glucose assimilation rates in T cells. The present study proposes that P. histicola treatment of arthritis in DQ8 mice leads to gut homeostasis. Normalized microbial diversity and metabolites lead to immune regulation resulting in reduced inflammation with improved physical function. The observations suggest P. histicola as a strong candidate for monoclonal bio-therapeutics.</jats:p

In-silico screening of 2,3-diphenylquinozaline derivatives as C-met kinase inhibitors

Quinoxaline, an important class of heterocylic compounds drawn greater attention due to their wide spectrum of biological activities. They are considered as an important chemical scaffold for anticancer drug design  due to their potential inhibitory activity against  C-met tyrosine kinase. C-met kinase inhibitors  are a class of small molecules that having therapeutic potential in  the treatment of various types of cancers. The present study aims to focus on the chemistry of quinoxaline derivatives, their potential activities against C-met tyrosine kinase, and in-silico screening of designed compounds. A series of twelve compounds were designed and docked against C-met tyrosine kinase for their binding energy. All compounds were found to be interacting  well with the protein. Compound NQ1 was found to have good binding energy showing an estimated Ki value of  1.1μm.  SAR  study indicated the presence of an electron withdrawing substitution on  benzilidine  phenyl ring of quinoxaline greatly improves its binding interaction with the protein.</jats:p