Expression pattern of ALDH1, E-cadherin, Vimentin and Twist in early and late onset sporadic colorectal cancer.

Aim: To evaluate the expression pattern of ALDH1 (aldehyde dehydrogenase 1), E-cadherin, Vimentin and Twist in early and late onset sporadic colorectal cancer (CRC) and to study association of their expression with the occurrence of CRC at a young age. Materials & methods: Immunohistochemistry of ALDH1, E-cadherin, Vimentin and Twist was performed on 103 pretreated CRC biopsy samples. Results: ALDH1 expression was found to have strong correlation with early onset CRC (p < 0.0001). Conclusion: High ALDH1 expression correlates with the early onset of CRC. ALDH1 over-expression correlates with poor overall survival in colon cancer.

Averaged model for probabilistic coalescence avalanches in two-dimensional emulsions: Insights into uncertainty propagation.

A two-dimensional concentrated emulsion exhibits spontaneous rapid destabilization through an avalanche of coalescence events which propagate through the assembly stochastically. We propose a deterministic model to explain the average dynamics of the avalanching process. The dynamics of the avalanche phenomenon is studied as a function of a composite parameter, the decay time ratio, which characterizes the ratio of the propensity of coalescence to cease propagation to that of propagation. When this ratio is small, the avalanche grows autocatalytically to destabilize the emulsion. Using a scaling analysis, we unravel the relation between a local characteristic of the system and a global system wide effect. The anisotropic nature of local coalescence results in a system size dependent transition from nonautocatalytic to autocatalytic behavior. By incorporating uncertainty into the parameters in the model, several possible realizations of the coalescence avalanche are generated. The results are compared with the Monte Carlo simulations to derive insights into how the uncertainty propagates in the system.

An optimal strategy to model microbial growth in a multiple substrate environment.

A comprehensive model is developed based on an optimal strategy describing varied microbial growth phenomenon involving sequential and simultaneous utilization of substrate. The model mimics the complex regulatory process of a cell which results in diverse growth process with the help of simple multi-variable constrained optimization, which aims at maximizing the specific cell growth. The metabolic processes of a cell are represented by simple flux balance equations. The different growth phenomenon exhibited by a microorganism are attributed to different levels of control present inside the cell. Provision is made in the model for these controls, in the form of constraints in the optimization formulation. The model prediction matches well with the experimental data of simultaneous growth of E. coli K12 on a mixture of glucose and organic acids like lactate, pyruvate, and acetate. Moreover, the model predictions are well in agreement with earlier published experimental data for the growth of E. coli K12 on other organic acids like fumarate, alpha-ketoglutarate, and succinate. (c) 1997 John Wiley & Sons, Inc. Biotechnol Bioeng 56: 635-644, 1997.