Valor de la Resonancia Nuclear Magnética en Ortopedia Oncológica

Presentamos nuestra experiencia preliminar sobre la utilidad diagnóstica de la Resonancia Nuclear Magnética (RM) en la evaluación pre- y postoperatoria de pacientes con tumores musculoesqueléticos. Se han revisado 21 pacientes con tumores caracterizados histológicamente. Del total, 10 casos eran sarcomas de partes blandas, 7 correspondían a tumores óseos y 4 eran metástasis óseas o recidivas locales. Las imágenes se obtuvieron mediante un sistema de RM de campo medio provisto de un imán superconductor operando a 0.5 Teslas. En todos los casos se obtuvieron imágenes T1 y T2, en los planos axial y coronal. Como norma se objetivó un excelente contraste entre la señal de la lesión y la de las estructuras normales adyacentes. Sólo en un caso, un osteosarcoma del extremo proximal del peroné, las imágenes de extensión a partes blandas vecinas resultaron ser negativas en la exploración quirúrgica. El análisis de los cambios de intensidad de la señal no permitieron distinguir la especificidad tisular del tumor, ni diferenciar lesiones benignas y malignas. En nuestra experiencia, la RM nos ha permitido un mejor diagnóstico anatómico de la extensión tumoral, facilitándonos la planificación quirúrgica que requieren las modernas técnicas reconstructivas en ortopedia oncológica.The preliminary experience using Magnetic Resonance imaging for pre- and post-operative assessment of orthopaedic oncologic patients is hereby reported. Twenty-one patients with histologically characterized bone and soft tissue tumors have been reviewed. Seventeen patients had primary musculoskeletal neoplasia: 10 had soft tissue sarcomas and 7 bone tumors. The remained 4 patients consisted of bone metastasis or local racidive. Magnetic resonance images were acquired using a superconductive magnet operating at 0.5 Tesla. T1- and T2-weighted transaxial and coronal images were obtained in all cases. An excellent contrast between the signal of the lesion and the normal adjacent structures was usually obtained. Only in one osteosarcoma of the proximal fibula, an extraosseous extension was presumed but not found during surgical resection. Changes in image intensity did not permit to identify tumor tissue specificity neither distinguish between benign and malignat lesions. In our experience, Magnetic Resonance shows a great advantage in order to determine tumor anatomical extension, providing a useful information for the surgical planning required by current reconstructive techniques in orthopaedic oncology

Evolution of associative learning in chemical networks

Organisms that can learn about their environment and modify their behaviour appropriately during their lifetime are more likely to survive and reproduce than organisms that do not. While associative learning – the ability to detect correlated features of the environment – has been studied extensively in nervous systems, where the underlying mechanisms are reasonably well understood, mechanisms within single cells that could allow associative learning have received little attention. Here, using in silico evolution of chemical networks, we show that there exists a diversity of remarkably simple and plausible chemical solutions to the associative learning problem, the simplest of which uses only one core chemical reaction. We then asked to what extent a linear combination of chemical concentrations in the network could approximate the ideal Bayesian posterior of an environment given the stimulus history so far? This Bayesian analysis revealed the ’memory traces’ of the chemical network. The implication of this paper is that there is little reason to believe that a lack of suitable phenotypic variation would prevent associative learning from evolving in cell signalling, metabolic, gene regulatory, or a mixture of these networks in cells

Microscopy-BIDS: An extension to the brain imaging data structure for microscopy data

The Brain Imaging Data Structure (BIDS) is a specification for organizing, sharing, and archiving neuroimaging data and metadata in a reusable way. First developed for magnetic resonance imaging (MRI) datasets, the community-led specification evolved rapidly to include other modalities such as magnetoencephalography, positron emission tomography, and quantitative MRI (qMRI). In this work, we present an extension to BIDS for microscopy imaging data, along with example datasets. Microscopy-BIDS supports common imaging methods, including 2D/3D, ex/in vivo, micro-CT, and optical and electron microscopy. Microscopy-BIDS also includes comprehensible metadata definitions for hardware, image acquisition, and sample properties. This extension will facilitate future harmonization efforts in the context of multi-modal, multi-scale imaging such as the characterization of tissue microstructure with qMRI

Response to Comment on “Estimating the reproducibility of psychological science”

Gilbert et al. conclude that evidence from the Open Science Collaboration's Reproducibility Project: Psychology indicates high reproducibility, given the study methodology. Their very optimistic assessment is limited by statistical misconceptions and by causal inferences from selectively interpreted, correlational data. Using the Reproducibility Project: Psychology data, both optimistic and pessimistic conclusions about reproducibility are possible, and neither are yet warranted.status: publishe

Consensus-based guidance for conducting and reporting multi-analyst studies

Any large dataset can be analyzed in a number of ways, and it is possible that the use of different analysis strategies will lead to different results and conclusions. One way to assess whether the results obtained depend on the analysis strategy chosen is to employ multiple analysts and leave each of them free to follow their own approach. Here, we present consensus-based guidance for conducting and reporting such multi-analyst studies, and we discuss how broader adoption of the multi-analyst approach has the potential to strengthen the robustness of results and conclusions obtained from analyses of datasets in basic and applied research

Syndecan-1 and FGF-2, but Not FGF Receptor-1, Share a Common Transport Route and Co-Localize with Heparanase in the Nuclei of Mesenchymal Tumor Cells

Syndecan-1 forms complexes with growth factors and their cognate receptors in the cell membrane. We have previously reported a tubulin-mediated translocation of syndecan-1 to the nucleus. The transport route and functional significance of nuclear syndecan-1 is still incompletely understood. Here we investigate the sub-cellular distribution of syndecan-1, FGF-2, FGFR-1 and heparanase in malignant mesenchymal tumor cells, and explore the possibility of their coordinated translocation to the nucleus. To elucidate a structural requirement for this nuclear transport, we have transfected cells with a syndecan-1/EGFP construct or with a short truncated version containing only the tubulin binding RMKKK sequence. The sub-cellular distribution of the EGFP fusion proteins was monitored by fluorescence microscopy. Our data indicate that syndecan-1, FGF-2 and heparanase co-localize in the nucleus, whereas FGFR-1 is enriched mainly in the perinuclear area. Overexpression of syndecan-1 results in increased nuclear accumulation of FGF-2, demonstrating the functional importance of syndecan-1 for this nuclear transport. Interestingly, exogenously added FGF-2 does not follow the route taken by endogenous FGF-2. Furthermore, we prove that the RMKKK sequence of syndecan-1 is necessary and sufficient for nuclear translocation, acting as a nuclear localization signal, and the Arginine residue is vital for this localization. We conclude that syndecan-1 and FGF-2, but not FGFR-1 share a common transport route and co-localize with heparanase in the nucleus, and this transport is mediated by the RMKKK motif in syndecan-1. Our study opens a new perspective in the proteoglycan field and provides more evidence of nuclear interactions of syndecan-1

Science Forum: Consensus-based guidance for conducting and reporting multi-analyst studies

Any large dataset can be analyzed in a number of ways, and it is possible that the use of different analysis strategies will lead to different results and conclusions. One way to assess whether the results obtained depend on the analysis strategy chosen is to employ multiple analysts and leave each of them free to follow their own approach. Here, we present consensus-based guidance for conducting and reporting such multi-analyst studies, and we discuss how broader adoption of the multi-analyst approach has the potential to strengthen the robustness of results and conclusions obtained from analyses of datasets in basic and applied research

Consensus-based guidance for conducting and reporting multi-analyst studies

International audienceAny large dataset can be analyzed in a number of ways, and it is possible that the use of different analysis strategies will lead to different results and conclusions. One way to assess whether the results obtained depend on the analysis strategy chosen is to employ multiple analysts and leave each of them free to follow their own approach. Here, we present consensus-based guidance for conducting and reporting such multi-analyst studies, and we discuss how broader adoption of the multi-analyst approach has the potential to strengthen the robustness of results and conclusions obtained from analyses of datasets in basic and applied research

ENIGMA-Sleep:Challenges, opportunities, and the road map

Neuroimaging and genetics studies have advanced our understanding of the neurobiology of sleep and its disorders. However, individual studies usually have limitations to identifying consistent and reproducible effects, including modest sample sizes, heterogeneous clinical characteristics and varied methodologies. These issues call for a large-scale multi-centre effort in sleep research, in order to increase the number of samples, and harmonize the methods of data collection, preprocessing and analysis using pre-registered well-established protocols. The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium provides a powerful collaborative framework for combining datasets across individual sites. Recently, we have launched the ENIGMA-Sleep working group with the collaboration of several institutes from 15 countries to perform large-scale worldwide neuroimaging and genetics studies for better understanding the neurobiology of impaired sleep quality in population-based healthy individuals, the neural consequences of sleep deprivation, pathophysiology of sleep disorders, as well as neural correlates of sleep disturbances across various neuropsychiatric disorders. In this introductory review, we describe the details of our currently available datasets and our ongoing projects in the ENIGMA-Sleep group, and discuss both the potential challenges and opportunities of a collaborative initiative in sleep medicine

Variability in the analysis of a single neuroimaging dataset by many teams

Data analysis workflows in many scientific domains have become increasingly complex and flexible. To assess the impact of this flexibility on functional magnetic resonance imaging (fMRI) results, the same dataset was independently analyzed by 70 teams, testing nine ex-ante hypotheses. The flexibility of analytic approaches is exemplified by the fact that no two teams chose identical workflows to analyze the data. This flexibility resulted in sizeable variation in hypothesis test results, even for teams whose statistical maps were highly correlated at intermediate stages of their analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Importantly, meta-analytic approaches that aggregated information across teams yielded significant consensus in activated regions across teams. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset. Our findings show that analytic flexibility can have substantial effects on scientific conclusions, and demonstrate factors related to variability in fMRI. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for multiple analyses of the same data. Potential approaches to mitigate issues related to analytical variability are discussed