Immunological characterization of chromogranins A and B and secretogranin II in the bovine pancreatic islet

Antisera against chromogranin A and B and secretogranin II were used for analysing the bovine pancreas by immunoblotting and immunohistochemistry. All three antigens were found in extracts of fetal pancreas by one dimensional immunoblotting. A comparison with the soluble proteins of chromaffin granules revealed that in adrenal medulla and in pancreas antigens which migrated identically in electrophoresis were present. In immunohistochemistry, chromogranin A was found in all pancreatic endocrine cell types with the exception of most pancreatic polypeptide-(PP-) producing cells. For chromogranin B, only a faint immunostaining was obtained. For secretorgranin II, A-and B-cells were faintly positive, whereas the majority of PP-cells exhibited a strong immunostaining for this antigen. These results establish that chromogranins A and B and secretogranin II are present in the endocrine pancreas, but that they exhibit a distinct cellular localization

Paraneoplastic pemphigus regression after thymoma resection

<p>Abstract</p> <p>Background</p> <p>Among human neoplasms thymomas are associated with highest frequency with paraneoplastic autoimmune diseases.</p> <p>Case presentation</p> <p>A case of a 42-year-old woman with paraneoplastic pemphigus as the first manifestation of thymoma is reported. Transsternal complete thymoma resection achieved pemphigus regression. The clinical correlations between pemphigus and thymoma are presented.</p> <p>Conclusion</p> <p>Our case report provides further evidence for the important role of autoantibodies in the pathogenesis of paraneoplastic skin diseases in thymoma patients. It also documents the improvement of the associated pemphigus after radical treatment of the thymoma.</p

Spin- and energy relaxation of hot electrons at GaAs surfaces

The mechanisms for spin relaxation in semiconductors are reviewed, and the mechanism prevalent in p-doped semiconductors, namely spin relaxation due to the electron-hole exchange interaction, is presented in some depth. It is shown that the solution of Boltzmann-type kinetic equations allows one to obtain quantitative results for spin relaxation in semiconductors that go beyond the original Bir-Aronov-Pikus relaxation-rate approximation. Experimental results using surface sensitive two-photon photoemission techniques show that the spin relaxation-time of electrons in p-doped GaAs at a semiconductor/metal surface is several times longer than the corresponding bulk spin relaxation-times. A theoretical explanation of these results in terms of the reduced density of holes in the band-bending region at the surface is presented.Comment: 33 pages, 12 figures; earlier submission replaced by corrected and expanded version; eps figures now included in the tex

Primary skin fibroblasts as a model of Parkinson's disease

Parkinson's disease is the second most frequent neurodegenerative disorder. While most cases occur sporadic mutations in a growing number of genes including Parkin (PARK2) and PINK1 (PARK6) have been associated with the disease. Different animal models and cell models like patient skin fibroblasts and recombinant cell lines can be used as model systems for Parkinson's disease. Skin fibroblasts present a system with defined mutations and the cumulative cellular damage of the patients. PINK1 and Parkin genes show relevant expression levels in human fibroblasts and since both genes participate in stress response pathways, we believe fibroblasts advantageous in order to assess, e.g. the effect of stressors. Furthermore, since a bioenergetic deficit underlies early stage Parkinson's disease, while atrophy underlies later stages, the use of primary cells seems preferable over the use of tumor cell lines. The new option to use fibroblast-derived induced pluripotent stem cells redifferentiated into dopaminergic neurons is an additional benefit. However, the use of fibroblast has also some drawbacks. We have investigated PARK6 fibroblasts and they mirror closely the respiratory alterations, the expression profiles, the mitochondrial dynamics pathology and the vulnerability to proteasomal stress that has been documented in other model systems. Fibroblasts from patients with PARK2, PARK6, idiopathic Parkinson's disease, Alzheimer's disease, and spinocerebellar ataxia type 2 demonstrated a distinct and unique mRNA expression pattern of key genes in neurodegeneration. Thus, primary skin fibroblasts are a useful Parkinson's disease model, able to serve as a complement to animal mutants, transformed cell lines and patient tissues

Approaches to Understanding COVID-19 and its Neurological Associations

There is an accumulating volume of research into neurological manifestations of COVID-19. However, inconsistent study designs, inadequate controls, poorly-validated tests, and differing settings, interventions, and cultural norms weaken study quality, comparability, and thus the understanding of the spectrum, burden and pathophysiology of these complications. Therefore, a global COVID-19 Neuro Research Coalition, together with the WHO, has reviewed reports of COVID-19 neurological complications and harmonised clinical measures for future research. This will facilitate well-designed studies using precise, consistent case definitions of SARS-CoV2 infection and neurological complications, with standardised forms for pooled data analyses that non-specialists can use, including in low-income settings

Tobacco prevention policies in west-African countries and their effects on smoking prevalence

Background: The WHO Framework Convention on Tobacco Control was shown to effectively lower smoking prevalence in in high income countries, however knowledge for low and middle income settings is sparse. The objective of this study was to describe WHO MPOWER policy measures in thirteen West-African countries and to investigate their correlation with smoking prevalence. Methods: Age-standardized smoking prevalence data and policy measures were collected from various WHO reports. For analysis MPOWER measures from 2008 and 2010, were combined with prevalence data from 2009 and 2011. Multiple linear regression models were set up. Results: In West-Africa mean smoking prevalence was approximately 20 % among males and approximately 3 % among females. Policy measures were mostly at a middle or low level. Regression analysis showed that tobacco cessation programs, health warnings on cigarettes, and higher price of cigarettes were negatively correlated with smoking prevalence. Significant effects were observed for only one policy measure (tobacco cessation programs) and only within the male population where smoking prevalence is generally higher. Conclusions: Tobacco control policies are enforced at relatively low levels in West-African countries. However, improving tobacco control policy implementation according to the WHO Framework Convention on Tobacco Control should assist in the reduction of smoking prevalence in African countries, thereby counteracting pro-smoking initiatives set forth by the tobacco industry

Computational Lipidology: Predicting Lipoprotein Density Profiles in Human Blood Plasma

Monitoring cholesterol levels is strongly recommended to identify patients at risk for myocardial infarction. However, clinical markers beyond “bad” and “good” cholesterol are needed to precisely predict individual lipid disorders. Our work contributes to this aim by bringing together experiment and theory. We developed a novel computer-based model of the human plasma lipoprotein metabolism in order to simulate the blood lipid levels in high resolution. Instead of focusing on a few conventionally used predefined lipoprotein density classes (LDL, HDL), we consider the entire protein and lipid composition spectrum of individual lipoprotein complexes. Subsequently, their distribution over density (which equals the lipoprotein profile) is calculated. As our main results, we (i) successfully reproduced clinically measured lipoprotein profiles of healthy subjects; (ii) assigned lipoproteins to narrow density classes, named high-resolution density sub-fractions (hrDS), revealing heterogeneous lipoprotein distributions within the major lipoprotein classes; and (iii) present model-based predictions of changes in the lipoprotein distribution elicited by disorders in underlying molecular processes. In its present state, the model offers a platform for many future applications aimed at understanding the reasons for inter-individual variability, identifying new sub-fractions of potential clinical relevance and a patient-oriented diagnosis of the potential molecular causes for individual dyslipidemia

Predicting olfactory receptor neuron responses from odorant structure

Background Olfactory receptors work at the interface between the chemical world of volatile molecules and the perception of scent in the brain. Their main purpose is to translate chemical space into information that can be processed by neural circuits. Assuming that these receptors have evolved to cope with this task, the analysis of their coding strategy promises to yield valuable insight in how to encode chemical information in an efficient way. Results We mimicked olfactory coding by modeling responses of primary olfactory neurons to small molecules using a large set of physicochemical molecular descriptors and artificial neural networks. We then tested these models by recording in vivo receptor neuron responses to a new set of odorants and successfully predicted the responses of five out of seven receptor neurons. Correlation coefficients ranged from 0.66 to 0.85, demonstrating the applicability of our approach for the analysis of olfactory receptor activation data. The molecular descriptors that are best-suited for response prediction vary for different receptor neurons, implying that each receptor neuron detects a different aspect of chemical space. Finally, we demonstrate that receptor responses themselves can be used as descriptors in a predictive model of neuron activation. Conclusions The chemical meaning of molecular descriptors helps understand structure-response relationships for olfactory receptors and their 'receptive fields'. Moreover, it is possible to predict receptor neuron activation from chemical structure using machine-learning techniques, although this is still complicated by a lack of training data