Proteome analysis of Arabidopsis thaliana by two-dimensional gel electrophoresis and matrix-assisted laser desorption/ionisation-time of flight mass spectrometry

In the present study we show results of a large-scale proteome analysis of the recently sequenced plant Arabidopsis thaliana. On the basis of a previously published sequential protein extraction protocol, we prepared protein extracts from eight different A. thaliana tissues (primary leaf, leaf, stem, silique, seedling, seed, root, and inflorescence) and analysed these by two-dimensional gel electrophoresis. A total of 6000 protein spots, from three of these tissues, namely primary leaf, silique and seedling, were excised and the contained proteins were analysed by matrix assisted laser desorption/ionisation time of flight mass spectrometry peptide mass fingerprinting. This resulted in the identification of the proteins contained in 2943 spots, which were found to be products of 663 different genes. In this report we present and discuss the methodological and biological results of our plant proteome analysis

PCSK2 expression in neuroendocrine tumors points to a midgut, pulmonary, or pheochromocytoma-paraganglioma origin

Neuroendocrine tumors (NETs) are often diagnosed from the metastases of an unknown primary tumor. Specific immunohistochemical (IHC) markers indicating the location of a primary tumor are needed. The proprotein convertase subtilisin/kexin type 2 (PCSK2) is found in normal neural and neuroendocrine cells, and known to express in NETs. We investigated the tissue microarray (TMA) of 86 primary tumors from 13 different organs and 9 metastatic NETs, including primary tumor-metastasis pairs, for PCSK2 expression with polymer-based IHC. PCSK2 was strongly positive in all small intestine and appendiceal NETs, the so-called midgut NETs, in most pheochromocytomas and paragangliomas, and in some of the typical and atypical pulmonary carcinoid tumors. NETs showing strong positivity were re-evaluated in larger tumor cohorts confirming the primary observation. In the metastases, the expression of PCSK2 mirrored that of the corresponding primary tumors. We found negative or weak staining in NETs from the thymus, gastric mucosa, pancreas, rectum, thyroid, and parathyroid. PCSK2 expression did not correlate with Ki-67 in well-differentiated NETs. Our data suggest that PCSK2 positivity can indicate the location of the primary tumor. Thus, PCSK2 could function in the IHC panel determined from screening metastatic NET biopsies of unknown primary origins.Peer reviewe

Pancreatic alpha cell mass in European subjects with type 2 diabetes

AIMS/HYPOTHESIS: Type 2 diabetes is a bi-hormonal disease characterised by relative hypoinsulinaemia and hyperglucagonaemia with elevated blood glucose levels. Besides pancreatic beta cell defects, a low number of beta cells (low beta cell mass) may contribute to the insufficient secretion of insulin. In this study our aim was to determine whether the alpha cell mass is also altered. METHODS: Using a point counting method, we measured the ratio of alpha to beta cell areas in pancreas samples obtained at autopsy from 50 type 2 diabetic subjects, whose beta cell mass had previously been found to be 36% lower than that of 52 non-diabetic subjects. RESULTS: The topography of alpha and beta cells was similar in both groups: many alpha cells were localised in the centre of the islets and the ratio of alpha/beta cell areas increased with islet size. The average ratio was significantly higher in type 2 diabetic subjects (0.72) than in non-diabetic subjects (0.42), with, however, a large overlap between the two groups. In contrast, the alpha cell mass was virtually identical in type 2 diabetic subjects (366 mg) and non-diabetic subjects (342 mg), and was not influenced by sex, BMI or type of diabetes treatment. CONCLUSIONS: The higher proportion of alpha to beta cells in the islets of some type 2 diabetic subjects is due to a decrease in beta cell number rather than an increase in alpha cell number. This imbalance may contribute to alterations in the normal inhibitory influence exerted by beta cells on alpha cells, and lead to the relative hyperglucagonaemia observed in type 2 diabete

PITX2 as a sensitive and specific marker of midgut neuroendocrine tumors: results from a cohort of 1157 primary neuroendocrine neoplasms

As Neuroendocrine Tumors (NET) often present as metastatic lesions, immunohistochemical assignment to a site of origin is one of the most important tasks in their pathological assessment. Since a fraction of NETs eludes the typical expression profiles of their primary localization, additional sensitive and specific markers are required to improve diagnostic certainty. We investigated the expression of the transcription factor Pituitary Homeobox 2 (PITX2) in a large-scale cohort of 909 NET and 248 Neuroendocrine Carcinomas (NEC) according to the Immunoreactive Score (IRS) and correlated PITX2 expression groups with general tumor groups and localization of the primary. PITX2 expression (all expression groups) was highly sensitive (98.1%) for midgut-derived NET, but not perfectly specific, as non-midgut NET (especially pulmonary/duodenal) were quite frequently weak or moderately positive. The specificity rose to 99.5% for a midgut origin of NET if only a strong PITX2 expression was considered, which was found in only 0.5% (one pancreatic/one pulmonary) of non-midgut NET. In metastases of midgut-derived NET, PITX2 was expressed in all cases (87.5% strong, 12.5% moderate), while CDX2 was negative or only weakly expressed in 31.3% of the metastases. In NEC, a fraction of cases (14%) showed a weak or moderate PITX2 expression, which was not associated with a specific tumor localization. Our study independently validates PITX2 as a very sensitive and specific immunohistochemical marker of midgut-derived NET in a very large collective of Neuroendocrine Neoplasms. Therefore, our data argue towards implementation into diagnostic panels applied for NET as a first line midgut marker

A survey of the state-of-the-art techniques for cognitive impairment detection in the elderly

With a growing number of elderly people in the UK, more and more of them suffer from various kinds of cognitive impairment. Cognitive impairment can be divided into different stages such as mild cognitive impairment (MCI) and severe cognitive impairment like dementia. Its early detection can be of great importance. However, it is challenging to detect cognitive impairment in the early stage with high accuracy and low cost, when most of the symptoms may not be fully expressed. This survey paper mainly reviews the state of the art techniques for the early detection of cognitive impairment and compares their advantages and weaknesses. In order to build an effective and low-cost automatic system for detecting and monitoring the cognitive impairment for a wide range of elderly people, the applications of computer vision techniques for the early detection of cognitive impairment by monitoring facial expressions, body movements and eye movements are highlighted in this paper. In additional to technique review, the main research challenges for the early detection of cognitive impairment with high accuracy and low cost are analysed in depth. Through carefully comparing and contrasting the currently popular techniques for their advantages and weaknesses, some important research directions are particularly pointed out and highlighted from the viewpoints of the authors alone

Support vector machine model for diagnosis of lymph node metastasis in gastric cancer with multidetector computed tomography: a preliminary study

<p>Abstract</p> <p>Background</p> <p>Lymph node metastasis (LNM) of gastric cancer is an important prognostic factor regarding long-term survival. But several imaging techniques which are commonly used in stomach cannot satisfactorily assess the gastric cancer lymph node status. They can not achieve both high sensitivity and specificity. As a kind of machine-learning methods, Support Vector Machine has the potential to solve this complex issue.</p> <p>Methods</p> <p>The institutional review board approved this retrospective study. 175 consecutive patients with gastric cancer who underwent MDCT before surgery were included. We evaluated the tumor and lymph node indicators on CT images including serosal invasion, tumor classification, tumor maximum diameter, number of lymph nodes, maximum lymph node size and lymph nodes station, which reflected the biological behavior of gastric cancer. Univariate analysis was used to analyze the relationship between the six image indicators with LNM. A SVM model was built with these indicators above as input index. The output index was that lymph node metastasis of the patient was positive or negative. It was confirmed by the surgery and histopathology. A standard machine-learning technique called k-fold cross-validation (5-fold in our study) was used to train and test SVM models. We evaluated the diagnostic capability of the SVM models in lymph node metastasis with the receiver operating characteristic (ROC) curves. And the radiologist classified the lymph node metastasis of patients by using maximum lymph node size on CT images as criterion. We compared the areas under ROC curves (AUC) of the radiologist and SVM models.</p> <p>Results</p> <p>In 175 cases, the cases of lymph node metastasis were 134 and 41 cases were not. The six image indicators all had statistically significant differences between the LNM negative and positive groups. The means of the sensitivity, specificity and AUC of SVM models with 5-fold cross-validation were 88.5%, 78.5% and 0.876, respectively. While the diagnostic power of the radiologist classifying lymph node metastasis by maximum lymph node size were only 63.4%, 75.6% and 0.757. Each SVM model of the 5-fold cross-validation performed significantly better than the radiologist.</p> <p>Conclusions</p> <p>Based on biological behavior information of gastric cancer on MDCT images, SVM model can help diagnose the lymph node metastasis preoperatively.</p

Cellular differentiation determines the expression of the hypoxia-inducible protein NDRG1 in pancreatic cancer

N-myc downstream-regulated gene-1 (NDRG1) is a recently described hypoxia-inducible protein that is upregulated in various human cancers. Pancreatic ductal adenocarcinoma, called pancreatic cancer, is a highly aggressive cancer that is characterised by its avascular structure, which results in a severe hypoxic environment. In this study, we investigated whether NDRG1 is upregulated in these tumours, thus providing a novel marker for malignant cells in the pancreas. By immunohistochemistry, we observed that NDRG1 was highly expressed in well-differentiated cells of pancreatic cancer, whereas the poorly differentiated tumour cells were negative. In addition, hyperplastic islets and ducts of nonquiescent pancreatic tissue were positive. To further explore its selective expression in tumours, two well-established pancreatic cancer cell lines of unequal differentiation status were exposed to 2% oxygen. NDRG1 mRNA and protein were upregulated by hypoxia in the moderately differentiated Capan-1 cells; however, its levels remained unchanged in the poorly differentiated Panc-1 cell line. Taken together, our data suggest that NDRG1 will not serve as a reliable marker of tumour cells in the pancreas, but may serve as a marker of differentiation. Furthermore, we present the novel finding that cellular differentiation may be an important factor that determines the hypoxia-induced regulation of NDRG1

Representation of the penalty term of dynamic concave utilities

In this paper we will provide a representation of the penalty term of general dynamic concave utilities (hence of dynamic convex risk measures) by applying the theory of g-expectations.Comment: An updated version is published in Finance & Stochastics. The final publication is available at http://www.springerlink.co

Heparanase expression is a prognostic indicator for postoperative survival in pancreatic adenocarcinoma

Pancreatic ductal adenocarcinoma has a median survival of less than 6 months from diagnosis. This is due to the difficulty in early diagnosis, the aggressive biological behaviour of the tumour and a lack of effective therapies for advanced disease. Mammalian heparanase is a heparan-sulphate proteoglycan cleaving enzyme. It helps to degrade the extracellular matrix and basement membranes and is involved in angiogenesis. Degradation of extracellular matrix and basement membranes as well as angiogenesis are key conditions for tumour cell spreading. Therefore, we have analysed the expression of heparanase in human pancreatic cancer tissue and cell lines. Heparanase is expressed in cell lines derived from primary tumours as well as from metastatic sites. By immunohistochemical analysis, it is preferentially expressed at the invading edge of a tumour at both metastatic and primary tumour sites. There is a trend towards heparanase expression in metastasising tumours as compared to locally growing tumours. Postoperative survival correlates inversely with heparanase expression of the tumour reflected by a median survival of 34 and 17 month for heparanase negative and positive tumours, respectively. Our results suggest, that heparanase promotes cancer cell invasion in pancreatic carcinoma and could be used as a prognostic indicator for postoperative survival of patients