Hochbegabte Underachiever: Verkannte Schwerarbeiter

Es gibt Schülerinnen und Schüler, die trotz höchster Intelligenzwerte nur dürftige oder ungenügende schulische Leistungen erbringen. Welche Hinweise zu den Gründen solchen Verhaltens liefern die Neurowissenschaften? Und: Welche Ansätze adäquater Förderung lassen sich daraus ableiten? Der vorliegende Artikel resümiert eine schriftliche Arbeit, in deren Rahmen sich Forschungsresultate der Neurowissenschaften, Ergebnisse aus Hochbegabtenstudien und Modelle der Emotionspsychologie zu einer Antwort verdichten. Eine entscheidende Rolle bei Minderleistung Hochbegabter spielen demnach bestimmte dysfunktionale Prozesse der Emotionsverarbeitung. Als logische Konsequenz postuliert der Autor zur adäquaten Förderung Hochbegabter Underachiever Ansätze, in deren Fokus die Verbesserung der Funktionalität eben dieser emotionsverarbeitenden Prozesse liegt

Non-Neoplastic and Neoplastic Pleural Endpoints Following Fiber Exposure

Exposure to asbestos fibers is associated with non-neoplastic pleural diseases including plaques, fibrosis, and benign effusions, as well as with diffuse malignant pleural mesothelioma. Translocation and retention of fibers are fundamental processes in understanding the interactions between the dose and dimensions of fibers retained at this anatomic site and the subsequent pathological reactions. The initial interaction of fibers with target cells in the pleura has been studied in cellular models in vitro and in experimental studies in vivo. The proposed biological mechanisms responsible for non-neoplastic and neoplastic pleural diseases and the physical and chemical properties of asbestos fibers relevant to these mechanisms are critically reviewed. Understanding mechanisms of asbestos fiber toxicity may help us anticipate the problems from future exposures both to asbestos and to novel fibrous materials such as nanotubes. Gaps in our understanding have been outlined as guides for future research

Alterations in MicroRNA Expression Contribute to Fatty Acid–Induced Pancreatic β-Cell Dysfunction

OBJECTIVE—Visceral obesity and elevated plasma free fatty acids are predisposing factors for type 2 diabetes. Chronic exposure to these lipids is detrimental for pancreatic β-cells, resulting in reduced insulin content, defective insulin secretion, and apoptosis. We investigated the involvement in this phenomenon of microRNAs (miRNAs), a class of noncoding RNAs regulating gene expression by sequence-specific inhibition of mRNA translation

A rare SNP in pre-miR-34a is associated with increased levels of miR-34a in pancreatic beta cells.

Open Access Article.Changes in the levels of specific microRNAs (miRNAs) can reduce glucose-stimulated insulin secretion and increase beta-cell apoptosis, two causes of islet dysfunction and progression to type 2 diabetes. Studies have shown that single nucleotide polymorphisms (SNPs) within miRNA genes can affect their expression. We sought to determine whether miRNAs, with a known role in beta-cell function, possess SNPs within the pre-miRNA structure which can affect their expression. Using published literature and dbSNP, we aimed to identify miRNAs with a role in beta-cell function that also possess SNPs within the region encoding its pre-miRNA. Following transfection of plasmids, encoding the pre-miRNA and each allele of the SNP, miRNA expression was measured. Two rare SNPs located within the pre-miRNA structure of two miRNA genes important to beta-cell function (miR-34a and miR-96) were identified. Transfection of INS-1 and MIN6 cells with plasmids encoding pre-miR-34a and the minor allele of rs72631823 resulted in significantly (p < 0.05) higher miR-34a expression, compared to cells transfected with plasmids encoding the corresponding major allele. Similarly, higher levels were also observed upon transfection of HeLa cells. Transfection of MIN6 cells with plasmids encoding pre-miR-96 and each allele of rs41274239 resulted in no significant differences in miR-96 expression. A rare SNP in pre-miR-34a is associated with increased levels of mature miR-34a. Given that small changes in miR-34a levels have been shown to cause increased levels of beta-cell apoptosis this finding may be of interest to studies looking at determining the effect of rare variants on type 2 diabetes susceptibility

Circulating microRNAs as novel biomarkers for diabetes mellitus.

Diabetes mellitus is characterized by insulin secretion from pancreatic β cells that is insufficient to maintain blood glucose homeostasis. Autoimmune destruction of β cells results in type 1 diabetes mellitus, whereas conditions that reduce insulin sensitivity and negatively affect β-cell activities result in type 2 diabetes mellitus. Without proper management, patients with diabetes mellitus develop serious complications that reduce their quality of life and life expectancy. Biomarkers for early detection of the disease and identification of individuals at risk of developing complications would greatly improve the care of these patients. Small non-coding RNAs called microRNAs (miRNAs) control gene expression and participate in many physiopathological processes. Hundreds of miRNAs are actively or passively released in the circulation and can be used to evaluate health status and disease progression. Both type 1 diabetes mellitus and type 2 diabetes mellitus are associated with distinct modifications in the profile of miRNAs in the blood, which are sometimes detectable several years before the disease manifests. Moreover, circulating levels of certain miRNAs seem to be predictive of long-term complications. Technical and scientific obstacles still exist that need to be overcome, but circulating miRNAs might soon become part of the diagnostic arsenal to identify individuals at risk of developing diabetes mellitus and its devastating complications

The UniProt-GO Annotation database in 2011

The GO annotation dataset provided by the UniProt Consortium (GOA: http://www.ebi.ac.uk/GOA) is a comprehensive set of evidenced-based associations between terms from the Gene Ontology resource and UniProtKB proteins. Currently supplying over 100 million annotations to 11 million proteins in more than 360 000 taxa, this resource has increased 2-fold over the last 2 years and has benefited from a wealth of checks to improve annotation correctness and consistency as well as now supplying a greater information content enabled by GO Consortium annotation format developments. Detailed, manual GO annotations obtained from the curation of peer-reviewed papers are directly contributed by all UniProt curators and supplemented with manual and electronic annotations from 36 model organism and domain-focused scientific resources. The inclusion of high-quality, automatic annotation predictions ensures the UniProt GO annotation dataset supplies functional information to a wide range of proteins, including those from poorly characterized, non-model organism species. UniProt GO annotations are freely available in a range of formats accessible by both file downloads and web-based views. In addition, the introduction of a new, normalized file format in 2010 has made for easier handling of the complete UniProt-GOA data set

The UniProt-GO Annotation database in 2011

The GO annotation dataset provided by the UniProt Consortium (GOA: http://www.ebi.ac.uk/GOA) is a comprehensive set of evidenced-based associations between terms from the Gene Ontology resource and UniProtKB proteins. Currently supplying over 100 million annotations to 11 million proteins in more than 360 000 taxa, this resource has increased 2-fold over the last 2 years and has benefited from a wealth of checks to improve annotation correctness and consistency as well as now supplying a greater information content enabled by GO Consortium annotation format developments. Detailed, manual GO annotations obtained from the curation of peer-reviewed papers are directly contributed by all UniProt curators and supplemented with manual and electronic annotations from 36 model organism and domain-focused scientific resources. The inclusion of high-quality, automatic annotation predictions ensures the UniProt GO annotation dataset supplies functional information to a wide range of proteins, including those from poorly characterized, non-model organism species. UniProt GO annotations are freely available in a range of formats accessible by both file downloads and web-based views. In addition, the introduction of a new, normalized file format in 2010 has made for easier handling of the complete UniProt-GOA data se