12 research outputs found

    Developing novel 3D measurement techniques and prediction method for food density determination

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    AbstractDensity is a physical characteristic which depends on the experimental technique used and structural properties of food. True, apparent, and bulk are different types of densities based on the way volume is measured. For porous foods such as grain food products, accurate measurement of density is challenging. Current measurement techniques for food density are inconsistent and nutrient databases do not have sufficient density data. Computed tomography (CT) and magnetic resonance imaging (MRI), laser scanners are non-destructive diagnostic tools for characterizing food microstructure. The objectives of this study were to 1) optimize the parameters of CT, MRI, and laser scanner to determine food density and compare the corresponding values with other traditional techniques, and 2) to develop neural networks as a prediction method for apparent and bulk densities. MicroCT 40 (Scano Medical Inc.), Lightspeed QX/i clinical CT (GE Healthcare), and 3 Tesla Signa HDx MRI (GE Healthcare) were used to acquire 3D images of foods for true density. A 3D laser scanner (NextEngine, Inc) was used to scan the foods items for apparent density. Neural networks were used in conjunction with the data collected from laser scanner and using food composition and processing conditions to generate a black-box prediction scheme. The results of CT, MRI, and laser scanner showed great potential to estimate density in comparison to traditional techniques. Porosity was estimated from the CT and MRI scanned image data. Laser scanner was successful in acquiring 3D images and calculating apparent density. Neural networks provided reliable density prediction power and were comparable to the other empirical equations in terms of accuracy. The ability to predict food density based on composition and processing conditions is necessary to fill gaps in nutrient databases and account for new foods

    FGFR inhibition in endometrial cancer induces caspase-independent cell death that can be augmented with ABT-737

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    Endometrial cancer (EC) is the most commonly diagnosed malignancy of the female reproductive tract. Unfortunately, 15-20% of women demonstrate persistent or recurrent tumors that are refractory to current chemotherapies with an associated poor prognosis. Our laboratory identified activating mutations in Fibroblast Growth Factor Receptor 2 (FGFR2) in 12% (stage I/II) to 17% (stage III/IV) endometrioid endometrial tumors and have since shown in a large (n=970) multi-institutional cohort they are associated with shorter progression free and cancer specific survival. Although FGFR inhibitors are in clinical trials in several cancer types, no detailed study of the mechanism of cell death has been published. We now show that treatment with BGJ398, AZD4547 and PD173074 leads to the induction of mitochondrial depolarization and changes in metabolic flux in two endometrial cancer cell lines (JHUEM2 and AN3CA) carrying activating mutations (C383R and N550K respectively). Despite this mitochondrial dysfunction, we have convincingly shown that the cell death following FGFR inhibition was caspase-independent, as evidenced by the lack of caspase-3, -7, and -9 activation, absence of PARP cleavage, and the inability of the broad-spectrum caspase inhibitor, Z-VAD-FMK, to prevent cell death. Knockdown of EndoG and AIF, common mediators of caspase-independent death, had no effect. Detailed quantification of LC3 positive puncta shows an increase in autophagy in JHUEM2 and AN3CA cells treated with all FGFR inhibitors. Knockdown of ATG3, ATG7 and ATG12 resulted in a slight increase in Annexin positive cell death indicating that the autophagy was cytoprotective in this context. We have now confirmed this novel caspase-independent cell death is mitochondrial dependent as it can be blocked by overexpression of Bcl-2 and/or Bcl-XL. Importantly we have shown that the combination of FGFR inhibitors with the BH3 mimetic ABT737 can markedly augment this caspase-independent cell death which may have implications for the design of more effective clinical trials
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