VEGF-C in rectal cancer tissues promotes tumor invasion and metastasis

PURPOSE

To investigate the relationship between the expression of vascular endothelial growth factor-C (VEGF-C) in rectal cancer tissues and clinicopathological factors.

METHODS

The molecular expression of VEGF-C in rectal cancer tissue derived from 45 patients and normal colon tissue from 15 subjects was detected using the immunohistochemical streptavidin/biotin/peroxidase complex (SABC) three-step method. The expression of VEGF-C in rectal cancer and its relationship with clinicopathological factors were statistically analyzed via x2 test or Spearman's rank correlation analysis or Wilcoxon rank sum test.

RESULTS

The positive expression rate of VEGF-C was 75.55% in rectal cancer tissues and 6.66% in normal tissues (p<0.01). The positive expression of VEGF-C was not related to patient gender, age and tumor diameter, but related to the grade of differentiation, depth of invasion, lymph node metastasis and Dukes stage (p<0.05). Positive intensity had no statistically significant difference with grade of differentiation and depth of invasion (p>0.05), but had statistically significant difference in lymph node metastasis and Dukes stage (p<0.05).

CONCLUSIONS

1) VEGF-C is highly expressed in rectal cancer tissues; 2) The positive expression of VEGF-C is positively correlated with tumor invasion depth, lymph node metastasis and Dukes stage; 3) Detection of VEGF-C expression can be used as a prognostic marker in rectal cancer.

SEGMENTATION OF ORGANS AT RISK IN THORACIC CT IMAGES USING A SHARPMASK ARCHITECTURE AND CONDITIONAL RANDOM FIELDS

Cancer is one of the leading causes of death worldwide. Radiotherapy is a standard treatment for this condition and the first step of the radiotherapy process is to identify the target volumes to be targeted and the healthy organs at risk (OAR) to be protected. Unlike previous methods for automatic segmentation of OAR that typically use local information and individually segment each OAR, in this paper, we propose a deep learning framework for the joint segmentation of OAR in CT images of the thorax, specifically the heart, esophagus, trachea and the aorta. Making use of Fully Convolutional Networks (FCN), we present several extensions that improve the performance, including a new architecture that allows to use low level features with high level information, effectively combining local and global information for improving the localization accuracy. Finally, by using Conditional Random Fields (specifically the CRF as Recurrent Neural Network model), we are able to account for relationships between the organs to further improve the segmentation results. Experiments demonstrate competitive performance on a dataset of 30 CT scans.

Fuling Granule, a Traditional Chinese Medicine Compound, Suppresses Cell Proliferation and TGFβ-Induced EMT in Ovarian Cancer

The compound fuling granule (CFG) is a traditional Chinese drug which has been used to treat ovarian cancer in China for over twenty years. Nevertheless, the underlying molecular mechanism of its anti-cancer effect remains unclear. In this study, microarray data analysis was performed to search differentially expressed genes in CFG-treated ovarian cancer cells. Several cell cycle and epithelial-mesenchymal transition (EMT) related genes were identified. The microarray analyses also revealed that CFG potentially regulates EMT in ovarian cancer. We also found that, functionally, CFG significantly suppresses ovarian cancer cell proliferation by cell cycle arrest, apoptosis and senescence and the AKT/GSK-3β pathway is possibly involved. Additionally, the invasion and migration ability of ovarian cancer induced by TGFβ is significantly suppressed by CFG. In conclusion, our results demonstrated that CFG suppresses ovarian cancer cell proliferation as well as TGFβ1-induced EMT in vitro. Finally, we discovered that CFG suppresses tumor growth and distant metastasis in vivo. Overall, these findings provide helpful clues to design novel clinical treatments against cancer.

A model for the coupling of the Greater Bairam and local environmental factors in promoting Rift-Valley Fever epizootics in Egypt

OBJECTIVES

Rift-Valley Fever (RVF) is a zoonotic mosquito-borne disease in Africa and the Arabian Peninsula. Drivers for this disease vary by region and are not well understood for North African countries such as Egypt. A deeper understanding of RVF risk factors would inform disease management policies.

STUDY DESIGN

The present study employs mathematical and computational modeling techniques to ascertain the extent to which the severity of RVF epizootics in Egypt differs depending on the interaction between imported ruminant and environmentally-constrained mosquito populations.

METHODS

An ordinary differential system of equations, a numerical model, and an individual-based model (IBM) were constructed to represent RVF disease dynamics between localized mosquitoes and ruminants being imported into Egypt for the Greater Bairam. Four cases, corresponding to the Greater Bairam's occurrence during distinct quarters of the solar year, were set up in both models to assess whether the different season-associated mosquito populations present during the Greater Bairam resulted in RVF epizootics of variable magnitudes.

RESULTS

The numerical model and the IBM produced nearly identical results: ruminant and mosquito population plots for both models were similar in shape and magnitude for all four cases. In both models, all four cases differed in the severity of their corresponding simulated RVF epizootics. The four cases, ranked by the severity of the simulated RVF epizootics in descending order, correspond with the occurrence of the Greater Bairam on the following months: July, October, April, and January. The numerical model was assessed for sensitivity with respect to parameter values and exhibited a high degree of robustness.

CONCLUSIONS

Limiting the importation of infected ruminants beginning one month prior to the Greater Bairam festival (on years in which the festival falls between the months of July and October: 2014-2022) might be a feasible way of mitigating future RVF epizootics in Egypt.

Segmentation of heterogeneous or small FDG PET positive tissue based on a 3D-locally adaptive random walk algorithm

A segmentation algorithm based on the random walk (RW) method, called 3D-LARW, has been developed to delineate small tumors or tumors with a heterogeneous distribution of FDG on PET images. Based on the original algorithm of RW [1], we propose an improved approach using new parameters depending on the Euclidean distance between two adjacent voxels instead of a fixed one and integrating probability densities of labels into the system of linear equations used in the RW. These improvements were evaluated and compared with the original RW method, a thresholding with a fixed value (40% of the maximum in the lesion), an adaptive thresholding algorithm on uniform spheres filled with FDG and FLAB method, on simulated heterogeneous spheres and on clinical data (14 patients). On these three different data, 3D-LARW has shown better segmentation results than the original RW algorithm and the three other methods. As expected, these improvements are more pronounced for the segmentation of small or tumors having heterogeneous FDG uptake.

Identification of Maillard reaction products on peanut allergens that influence binding to the receptor for advanced glycation end products

BACKGROUND

Recent immunological data demonstrated that dendritic cells preferentially recognize advanced glycation end product (AGE)-modified proteins, upregulate expression of the receptor for AGE (RAGE), and consequently bias the immune response toward allergy.

METHODS

Peanut extract was characterized by mass spectrometry (MS) to elucidate the specific residues and specific AGE modifications found in raw and roasted peanuts and on rAra h 1 that was artificially glycated by incubation with glucose or xylose. The binding of the RAGE-V1C1 domain to peanut allergens was assessed by PAGE and Western analysis with anti-Ara h 1, 2, and 3 antibodies. IgE binding to rAra h 1 was also assessed using the same methods.

RESULTS

AGE modifications were found on Ara h 1 and Ara h 3 in both raw and roasted peanut extract. No AGE modifications were found on Ara h 2. Mass spectrometry and Western blot analysis demonstrated that RAGE binds selectively to Ara h 1 and Ara h 3 derived from peanut extract, whereas the analysis failed to demonstrate Ara h 2 binding to RAGE. rAra h 1 with no AGE modifications did not bind RAGE; however, after AGE modification with xylose, rAra h 1 bound to RAGE.

CONCLUSIONS

AGE modifications to Ara h 1 and Ara h 3 can be found in both raw and roasted peanuts. Receptor for AGE was demonstrated to selectively interact with AGE-modified rAra h 1. If sensitization to peanut allergens occurs in dendritic cells via RAGE interactions, these cells are likely interacting with modified Ara h 1 and Ara h 3, but not Ara h 2.

Computationally predicted IgE epitopes of walnut allergens contribute to cross-reactivity with peanuts

BACKGROUND

Cross-reactivity between peanuts and tree nuts implies that similar immunoglobulin E (IgE) epitopes are present in their proteins.

OBJECTIVE

To determine whether walnut sequences similar to known peanut IgE-binding sequences, according to the property distance (PD) scale implemented in the Structural Database of Allergenic Proteins, react with IgE from sera of patients with allergy to walnut and/or peanut.

METHODS

  Patient sera were characterized by western blotting for IgE binding to nut protein extracts and to peptides from walnut and peanut allergens, similar to known peanut epitopes as defined by low PD values, synthesized on membranes. Competitive enzyme-linked immunosorbent assay (ELISA) was used to show that peanut and predicted walnut epitope sequences compete with purified Ara h 2 for binding to IgE in serum from a cross-reactive patient.

RESULTS

Sequences from the vicilin walnut allergen Jug r 2, which had low PD values to epitopes of the peanut allergen Ara h 2, a 2S albumin, bound to IgE in sera from five patients who reacted to either walnut or peanut or both. A walnut epitope recognized by sera from six patients mapped to a surface-exposed region on a model of the N-terminal pro-region of Jug r 2. This predicted walnut epitope competed for IgE binding to Ara h 2 in serum as well as the known IgE epitope from Ara h 2.

CONCLUSIONS

Sequences with low PD value (< 8.5) to known IgE epitopes could contribute to cross-reactivity between allergens. This further validates the PD scoring method for predicting cross-reactive epitopes in allergens.