One of the greatest challenges in cell therapy is to minimally invasively deliver a large quantity of viable cells to a tissue of interest with high engraftment efficiency. Low and inefficient homing of systemically delivered mesenchymal stem cells (MSCs), for example, is thought to be a major limitation of existing MSC-based therapeutic approaches, caused predominantly by inadequate expression of cell surface adhesion receptors. Using a platform approach that preserves the MSC phenotype and does not require genetic manipulation, we modified the surface of MSCs with a nanometer-scale polymer construct containing sialyl Lewisx (sLex) that is found on the surface of leukocytes and mediates cell rolling within inflamed tissue. The sLex engineered MSCs exhibited a robust rolling response on inflamed endothelium in vivo and homed to inflamed tissue with higher efficiency compared with native MSCs. The modular approach described herein offers a simple method to potentially target any cell type to specific tissues via the circulation.
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Most of the information about type B lactic acidosis associated with cancer is derived from case reports and there are no randomized controlled trials to compare different therapeutic modalities. Previous reviews of cases only refer to hematologic malignancies. We present a patient with non-Hodgkin's lymphoma who developed type B lactic acidosis. We performed a search of the PUBMED database using the MESH terms “neoplasms” AND “acidosis, lactic”, limited to the English language, and written between the years 2000 and 2010. A total of 31 cases were retrieved. These cases were identified and reviewed. The possible pathophysiologic mechanisms and treatment options are discussed. Type B lactic acidosis is most commonly seen in patients with lymphoma or leukemia. Although formal prospective trials are lacking, type B lactic acidosis in patients with cancer seems to be a marker of poor prognosis regardless of the treatment offered and may be invariably fatal. Future research should focus on potential therapy based on the pathogenic mechanisms that lead to type B lactic acidosis in cancer patients.
Cancer continues to be a leading cause of mortality and morbidity the world over. While the incidence of cancer is projected to increase by 70% over the next two decades, some research findings suggest a disproportionate distribution of new cancer cases and attendant fatalities across certain regions of the world, with poor and lower income countries worse affected at a time when advances in cancer research, medical technology, and drug development are giving rise to better cancer survival in developed countries. In this study, the role of selected social determinants of health in gauging cancer outcomes relative to incidence across various countries in different regions of the world was explored. The results indicated that the education index, income index, Gini coefficient, availability of cancer control policies and programs, as well as health system performance have an association with and are good predictors of the mortality to incidence ratio (MIR) of lung, breast, cervical, and colorectal cancers. In other words, populations with better education, higher incomes and lower inequalities, active cancer control policies and programs and high performing health systems have better cancer outcomes as reflected in lower MIRs relative to other populations.
Generalized disjunctive programming (GDP) is an extension of the disjunctive programming paradigm developed by Balas. The GDP formulation involves Boolean and continuous variables that are specified in algebraic constraints, disjunctions and logic propositions, which is an alternative representation to the traditional algebraic mixedinteger programming formulation. After providing a brief review of MINLP optimization, we present an overview of GDP for the case of convex functions emphasizing the quality of continuous relaxations of alternative reformulations that include the big-M and the hull relaxation. We then review disjunctive branch and bound as well as logicbased decomposition methods that circumvent some of the limitations in traditional MINLP optimization. We next consider the case of linear GDP problems to show how a hierarchy of relaxations can be developed by performing sequential intersection of disjunctions. Finally, for the case when the GDP problem involves nonconvex functions, we propose a scheme for tightening the lower bounds for obtaining the global optimum using a combined disjunctive and spatial branch and bound search. We illustrate the application of the theoretical concepts and algorithms on several engineering and OR problems.
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