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

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

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

Frequent overexpression of HMGA1 and 2 in gastroenteropancreatic neuroendocrine tumours and its relationship to let-7 downregulation

The molecular pathogenesis of gastroenteropancreatic (GEP) neuroendocrine tumours (NETs) remains to be elucidated. High-mobility group A (HMGA) proteins play important roles in the regulation of transcription, differentiation, and neoplastic transformation. In this study, the expression of HMGA1 and HMGA2 was studied in 55 GEP NETs. Overexpression of HMGA1 and 2 was frequently detected in GEP NETs compared with normal tissues. Nuclear immunostaining of HMGA1 and 2 was observed in GEP NETs (38 of 55, 69%; 40 of 55, 73%, respectively). High-mobility group A2 expression increased from well-differentiated NET (WNET) to well-differentiated neuroendocrine carcinoma (WNEC) and poorly differentiated NEC (PNEC) (P<0.005) and showed the highest level in stage IV tumours (P<0.01). In WNECs, the expression of HMGA1 and 2 was significantly higher in metastatic tumours than those without metastasis (P<0.05). Gastroenteropancreatic NETs in foregut showed the highest level of HMGA1 and 2 expressions. MIB-1 labelling index (MIB-1 LI) correlated with HMGA1 and 2 overexpression (R=0.28, P<0.05; R=0.434, P<0.001; respectively) and progressively increased from WNETs to WNECs and PNECs (P<0.001). Let-7 expression was addressed in 6 normal organs, 30 tumour samples, and 24 tumour margin non-tumour tissues. Compared with normal tissues, let-7 downregulation was frequent in NETs (19 of 30, 63%). Higher expression of HMGA1 and 2 was frequently observed in tumours with let-7 significant reduction (53, 42%, respectively). The reverse correlation could be detected between HMGA1 and let-7 (P<0.05). Our findings suggested that HMGA1 and 2 overexpression and let-7 downregulation might relate to pathogenesis of GEP NETs

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

Influence of Dopaminergically Mediated Reward on Somatosensory Decision-Making

This pharmacological fMRI study shows that during reward-based sensory decision-making, dopamine is crucially involved in reward-related modulation of human primary sensory cortex

Generative Embedding for Model-Based Classification of fMRI Data

Decoding models, such as those underlying multivariate classification algorithms, have been increasingly used to infer cognitive or clinical brain states from measures of brain activity obtained by functional magnetic resonance imaging (fMRI). The practicality of current classifiers, however, is restricted by two major challenges. First, due to the high data dimensionality and low sample size, algorithms struggle to separate informative from uninformative features, resulting in poor generalization performance. Second, popular discriminative methods such as support vector machines (SVMs) rarely afford mechanistic interpretability. In this paper, we address these issues by proposing a novel generative-embedding approach that incorporates neurobiologically interpretable generative models into discriminative classifiers. Our approach extends previous work on trial-by-trial classification for electrophysiological recordings to subject-by-subject classification for fMRI and offers two key advantages over conventional methods: it may provide more accurate predictions by exploiting discriminative information encoded in ‘hidden’ physiological quantities such as synaptic connection strengths; and it affords mechanistic interpretability of clinical classifications. Here, we introduce generative embedding for fMRI using a combination of dynamic causal models (DCMs) and SVMs. We propose a general procedure of DCM-based generative embedding for subject-wise classification, provide a concrete implementation, and suggest good-practice guidelines for unbiased application of generative embedding in the context of fMRI. We illustrate the utility of our approach by a clinical example in which we classify moderately aphasic patients and healthy controls using a DCM of thalamo-temporal regions during speech processing. Generative embedding achieves a near-perfect balanced classification accuracy of 98% and significantly outperforms conventional activation-based and correlation-based methods. This example demonstrates how disease states can be detected with very high accuracy and, at the same time, be interpreted mechanistically in terms of abnormalities in connectivity. We envisage that future applications of generative embedding may provide crucial advances in dissecting spectrum disorders into physiologically more well-defined subgroups