Cell colonization is an important in a wide variety of biological processes and applications including vascularization, wound healing, tissue engineering, stem cell differentiation and biosensors. During colonization porous 3D structures are used to support and guide the ingrowth of cells into the matrix. In this review, we summarize our understanding of various factors affecting cell colonization in three-dimensional environment. The structural, biological and degradation properties of the matrix all play key roles during colonization. Further, specific scaffold properties such as porosity, pore size, fiber thickness, topography and scaffold stiffness as well as important cell material interactions such as cell adhesion and mechanotransduction also influence colonization.

The gastrointestinal tract and pancreas exhibit ~17 different neuroendocrine cell types, but neither the cell of origin nor the biological basis of GEP-NETs is understood. This review examines GEP-NETs from the cellular and molecular perspective and addresses the distinct patterns of functional tumor biology pertinent to clinicians. Although grouped as a neoplastic entity (NETs), each lesion is derived from distinct cell precursors, produces specific bioactive products, exhibits distinct chromosomal abnormalities and somatic mutation events and has uniquely dissimilar clinical presentations. GEP-NETs demonstrate very different survival rates reflecting the intrinsic differences in malignant potential and variations in proliferative regulation. Apart from the identification of the inhibitory role of the somatostatin receptors, there is limited biological knowledge of the key regulators of proliferation and hence a paucity of successful targeted therapeutic agents. IGF-I, TGFβ and a variety of tyrosine kinases have been postulated as key regulatory elements; rigorous data is still required to define predictably effective and rational therapeutic strategy in an individual tumor. A critical issue in the clinical management of GEP-NETs is the need to appreciate both the neuroendocrine commonalities of the disease as well as the unique characteristics of each tumor. The further acquisition of a detailed biological and molecular appreciation of GEP-NETs is vital to the development of effective management strategy.

Neuroendocrine tumors (NETs) are a heterogeneous group of cancers of which the commonest site is the small intestine (SI). Most information available to determine tumor behavior reflects univariate assessment of factors or is anecdotal or experience based. There currently exists no objective multivariate analysis of indices that defines SI NET prognosis. A key unmet need is the lack of a rigorous mathematical-based tool – a nomogram – for the assessment of parameters that define progress, determine prognosis and can guide therapy. Since prediction of NET behavior is a critical criterion in determining clinical strategy, we constructed a NET nomogram (Modlin Score) for prognosis prediction, patient group comparisons and a guide for stratification of treatment and surveillance. We used hazard ratio (HR), Cox analysis and Kaplan-Meier analysis of published data and the current Surveillance, Epidemiology and End Results (SEER) database (approx. 20,000 patients) to develop a nomogram from 15 variables demonstrated to provide significant multivariate HRs. These included age, gender, ethnicity, symptoms, urinary 5-hydroxyindoleacetic acid, plasma chromogranin A, liver function tests, tumor size, invasion, metastasis, histology, Ki-67 index, carcinoid heart disease and therapy (surgery or long-acting somatostatin analogs). Internal validation was assessed using 33 SI NET patients. A NET nomoscore (Modlin Score) was developed by HR weighting and stratification into low (<75), medium (75–95) and high risk (>95). This identified significant differences (p <0.03, Kaplan-Meier) in survival (15.5 ± 4.3, 9.7 ± 2.5 and 6.4 ± 1.1 years, respectively). The Modlin Score was significantly elevated (p <0.01) in deceased compared to alive patients. This nomogram represents an optimized construct based upon currently analyzable data, and application will facilitate accurate stratification for comparison in clinical trials. External validation and amplification by identification of additional indices, e.g. molecular biomarkers, are necessary. The development of a mathematically validated nomogram provides a platform for objective assessment of SI NET disease, a finite basis for precise prognostication and a tool to guide management strategy.

Bioreactors are widely used in tissue engineering as a way to distribute nutrients within porous materials and provide physical stimulus required by many tissues. However, the fluid dynamics within the large porous structure are not well understood. In this study, we explored the effect of reactor geometry by using rectangular and circular reactors with three different inlet and outlet patterns. Geometries were simulated with and without the porous structure using the computational fluid dynamics software Comsol Multiphysics 3.4 and/or ANSYS CFX 11 respectively. Residence time distribution analysis using a step change of a tracer within the reactor revealed non-ideal fluid distribution characteristics within the reactors. The Brinkman equation was used to model the permeability characteristics with in the chitosan porous structure. Pore size was varied from 10 to 200 microm and the number of pores per unit area was varied from 15 to 1,500 pores/mm(2). Effect of cellular growth and tissue remodeling on flow distribution was also assessed by changing the pore size (85-10 microm) while keeping the number of pores per unit area constant. These results showed significant increase in pressure with reduction in pore size, which could limit the fluid flow and nutrient transport. However, measured pressure drop was marginally higher than the simulation results. Maximum shear stress was similar in both reactors and ranged approximately 0.2-0.3 dynes/cm(2). The simulations were validated experimentally using both a rectangular and circular bioreactor, constructed in-house. Porous structures for the experiments were formed using 0.5% chitosan solution freeze-dried at -80 degrees C, and the pressure drop across the reactor was monitored.