Development and characterization of a microfluidic model of the tumour microenvironment

The physical microenvironment of tumours is characterized by heterotypic cell interactions and physiological gradients of nutrients, waste products and oxygen. This tumour microenvironment has a major impact on the biology of cancer cells and their response to chemotherapeutic agents. Despite this, most in vitro cancer research still relies primarily on cells grown in 2D and in isolation in nutrient- and oxygen-rich conditions. Here, a microfluidic device is presented that is easy to use and enables modelling and study of the tumour microenvironment in real-time. The versatility of this microfluidic platform allows for different aspects of the microenvironment to be monitored and dissected. This is exemplified here by real-time profiling of oxygen and glucose concentrations inside the device as well as effects on cell proliferation and growth, ROS generation and apoptosis. Heterotypic cell interactions were also studied. The device provides a live ‘window’ into the microenvironment and could be used to study cancer cells for which it is difficult to generate tumour spheroids. Another major application of the device is the study of effects of the microenvironment on cellular drug responses. Some data is presented for this indicating the device’s potential to enable more physiological in vitro drug screening

Petrographical and geochemical evidences for paragenetic sequence interpretation of diagenesis in mixed siliciclastic–carbonate sediments: Mozduran Formation (Upper Jurassic), south of Agh-Darband, NE Iran

The Upper Jurassic Mozduran Formation with a thickness of 420 m at the type locality is the most important gas-bearing reservoir in NE Iran. It is mainly composed of limestone, dolostone with shale and gypsum interbeds that grade into coarser siliciclastics in the easternmost part of the basin. Eight stratigraphic sections were studied in detail in south of the Agh-Darband area. These analyses suggest that four carbonate facies associations and three siliciclastic lithofacies were deposited in shallow marine to shoreline environments, respectively. Cementation, compaction, dissolution, micritization, neomorphism, hematitization, dolomitization and fracturing are diagenetic processes that affected these sediments.Stable isotope variations of δ18O and δ13C in carbonate rocks show two different trends. High depletion of δ18O and low variation of δ13C probably reflect increasing temperatures during burial diagenesis, while the higher depletion in carbon isotope values with low variations in oxygen isotopes are related to fresh water flushing during meteoric diagenesis. Negative values of carbon isotopes may have also resulted from organic matter alteration during penetration of meteoric water. Fe and Mn enrichment with depletion of δ18O also supports the contention that alteration associated with higher depletion in carbon isotope values with low variations in oxygen isotopes took place during meteoric diagenesis. The presence of bright luminescence indicates redox conditions during precipitation of calcite cement

A Bioinformatics Filtering Strategy for Identifying Radiation Response Biomarker Candidates

The number of biomarker candidates is often much larger than the number of clinical patient data points available, which motivates the use of a rational candidate variable filtering methodology. The goal of this paper is to apply such a bioinformatics filtering process to isolate a modest number (<10) of key interacting genes and their associated single nucleotide polymorphisms involved in radiation response, and to ultimately serve as a basis for using clinical datasets to identify new biomarkers. In step 1, we surveyed the literature on genetic and protein correlates to radiation response, in vivo or in vitro, across cellular, animal, and human studies. In step 2, we analyzed two publicly available microarray datasets and identified genes in which mRNA expression changed in response to radiation. Combining results from Step 1 and Step 2, we identified 20 genes that were common to all three sources. As a final step, a curated database of protein interactions was used to generate the most statistically reliable protein interaction network among any subset of the 20 genes resulting from Steps 1 and 2, resulting in identification of a small, tightly interacting network with 7 out of 20 input genes. We further ranked the genes in terms of likely importance, based on their location within the network using a graph-based scoring function. The resulting core interacting network provides an attractive set of genes likely to be important to radiation response