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- Klionsky, Daniel J., et al.
(author)
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Guidelines for the use and interpretation of assays for monitoring autophagy
- 2012
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In: Autophagy. - : Informa UK Limited. - 1554-8635 .- 1554-8627. ; 8:4, s. 445-544
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Research review (peer-reviewed)abstract
- In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. A key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process vs. those that measure flux through the autophagy pathway (i.e., the complete process); thus, a block in macroautophagy that results in autophagosome accumulation needs to be differentiated from stimuli that result in increased autophagic activity, defined as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (in most higher eukaryotes and some protists such as Dictyostelium) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the field understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular autophagy assays, we hope to encourage technical innovation in the field.
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- Thompson, Paul M., et al.
(author)
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The ENIGMA Consortium : large-scale collaborative analyses of neuroimaging and genetic data
- 2014
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In: BRAIN IMAGING BEHAV. - : Springer Science and Business Media LLC. - 1931-7557 .- 1931-7565. ; 8:2, s. 153-182
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Journal article (peer-reviewed)abstract
- The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium is a collaborative network of researchers working together on a range of large-scale studies that integrate data from 70 institutions worldwide. Organized into Working Groups that tackle questions in neuroscience, genetics, and medicine, ENIGMA studies have analyzed neuroimaging data from over 12,826 subjects. In addition, data from 12,171 individuals were provided by the CHARGE consortium for replication of findings, in a total of 24,997 subjects. By meta-analyzing results from many sites, ENIGMA has detected factors that affect the brain that no individual site could detect on its own, and that require larger numbers of subjects than any individual neuroimaging study has currently collected. ENIGMA's first project was a genome-wide association study identifying common variants in the genome associated with hippocampal volume or intracranial volume. Continuing work is exploring genetic associations with subcortical volumes (ENIGMA2) and white matter microstructure (ENIGMA-DTI). Working groups also focus on understanding how schizophrenia, bipolar illness, major depression and attention deficit/hyperactivity disorder (ADHD) affect the brain. We review the current progress of the ENIGMA Consortium, along with challenges and unexpected discoveries made on the way.
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- Harms, Hendrik J, et al.
(author)
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Quantification of [(11)C]-meta-hydroxyephedrine uptake in human myocardium
- 2014
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In: EJNMMI Research. - : Springer Science and Business Media LLC. - 2191-219X. ; 4
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Journal article (peer-reviewed)abstract
- BACKGROUND: The aims of this study were to determine the optimal tracer kinetic model for [(11)C]-meta-hydroxyephedrine ([(11)C]HED) and to evaluate the performance of several simplified methods.METHODS: Thirty patients underwent dynamic 60-min [(11)C]HED scans with online arterial blood sampling. Single-tissue and both reversible and irreversible two-tissue models were fitted to the data using the metabolite-corrected arterial input function. For each model, reliable fits were defined as those yielding outcome parameters with a coefficient of variation (CoV) <25%. The optimal model was determined using Akaike and Schwarz criteria and the F-test, together with the number of reliable fits. Simulations were performed to study accuracy and precision of each model. Finally, quantitative results obtained using a population-averaged metabolite correction were evaluated, and simplified retention index (RI) and standardized uptake value (SUV) results were compared with quantitative volume of distribution (V T) data.RESULTS: The reversible two-tissue model was preferred in 75.8% of all segments, based on the Akaike information criterion. However, V T derived using the single-tissue model correlated highly with that of the two-tissue model (r (2) = 0.94, intraclass correlation coefficient (ICC) = 0.96) and showed higher precision (CoV of 24.6% and 89.2% for single- and two-tissue models, respectively, at 20% noise). In addition, the single-tissue model yielded reliable fits in 94.6% of all segments as compared with 77.1% for the reversible two-tissue model. A population-averaged metabolite correction could not be used in approximately 20% of the patients because of large biases in V T. RI and SUV can provide misleading results because of non-linear relationships with V T.CONCLUSIONS: Although the reversible two-tissue model provided the best fits, the single-tissue model was more robust and results obtained were similar. Therefore, the single-tissue model was preferred. RI showed a non-linear correlation with V T, and therefore, care has to be taken when using RI as a quantitative measure.
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