Avaliação de assimetria hemisférica funcional em pacientes com doença de Alzheimer

Introdução: as assimetrias cerebrais constituem aspectos fundamentais para a organização funcional e o processamento cognitivo. Estudos prévios têm reportado que alterações no padrão de assimetria hemisférica existentes em algumas patologias reduzem a eficiência no processamento de informação. Objetivos: avaliar a assimetria hemisférica funcional a partir de padrões de homofilia e heterofilia; analisar a assimetria da conectividade funcional de redes dinâmicas em portadores de Alzheimer. Metodologia: foram construídas redes funcionais dinâmicas do córtex cerebral de pacientes com diferentes níveis de Alzheimer (muito leve, leve e moderado a grave) e do grupo de controle a partir de EEG (19 eletrodos, Sistema 10-20). Tais redes foram geradas a partir do método de associação sincronização por motifs e representadas através de grafos variantes no tempo e de redes estáticas agregadas. Essas redes foram avaliadas por meio de índices de assimetria e conectividade. Resultados: os resultados mostram uma tendência à redução da homofilia com a progressão da enfermidade, quando se compara ao controle, além de um leve aumento do índice para o último estágio, se comparado aos estágios iniciais da doença. A assimetria para a conectividade funcional, por sua vez, mostrou uma propensão a maior conectividade para o hemisfério direito, salvo para o estágio moderado a grave. Conclusão: embora os resultados não tenham apresentado diferenças significativas, eles indicam tendências de alteração no padrão de homofilia e na assimetria da conectividade funcional em redes funcionais dinâmicas de pacientes com Alzheimer.

Intestinal fungi contribute to development of alcoholic liver disease

This study was supported in part by NIH grants R01 AA020703, U01 AA021856 and by Award Number I01BX002213 from the Biomedical Laboratory Research & Development Service of the VA Office of Research and Development (to B.S.). K.H. was supported by a DFG (Deutsche Forschungsgemeinschaft) fellowship (HO/ 5690/1-1). S.B. was supported by a grant from the Swiss National Science Foundation (P2SKP3_158649). G.G. received funding from the Yale Liver Center NIH P30 DK34989 and R.B. from NIAAA grant U01 AA021908. A.K. received support from NIH grants RC2 AA019405, R01 AA020216 and R01 AA023417. G.D.B. is supported by funds from the Wellcome Trust. We acknowledge the Human Tissue and Cell Research (HTCR) Foundation for making human tissue available for research and Hepacult GmbH (Munich, Germany) for providing primary human hepatocytes for in vitro analyses. We thank Dr. Chien-Yu Lin Department of Medicine, Fu-Jen Catholic University, Taiwan for statistical analysis.Peer reviewedPublisher PD

In vivo tomographic imaging of red-shifted fluorescent proteins

We have developed a spectral inversion method for three-dimensional tomography of far-red and near-infrared fluorescent proteins in animals. The method was developed in particular to address the steep light absorption transition of hemoglobin from the visible to the far-red occurring around 600 nm. Using an orthotopic mouse model of brain tumors expressing the red-shifted fluorescent protein mCherry, we demonstrate significant improvements in imaging accuracy over single-wavelength whole body reconstructions. Furthermore, we show an improvement in sensitivity of at least an order of magnitude over green fluorescent protein (GFP) for whole body imaging. We discuss how additional sensitivity gains are expected with the use of further red-shifted fluorescent proteins and we explain the differences and potential advantages of this approach over two-dimensional planar imaging methods

A standardized analytics pipeline for reliable and rapid development and validation of prediction models using observational health data

Background and objective: As a response to the ongoing COVID-19 pandemic, several prediction models in the existing literature were rapidly developed, with the aim of providing evidence-based guidance. However, none of these COVID-19 prediction models have been found to be reliable. Models are commonly assessed to have a risk of bias, often due to insufficient reporting, use of non-representative data, and lack of large-scale external validation. In this paper, we present the Observational Health Data Sciences and Informatics (OHDSI) analytics pipeline for patient-level prediction modeling as a standardized approach for rapid yet reliable development and validation of prediction models. We demonstrate how our analytics pipeline and open-source software tools can be used to answer important prediction questions while limiting potential causes of bias (e.g., by validating phenotypes, specifying the target population, performing large-scale external validation, and publicly providing all analytical source code). Methods: We show step-by-step how to implement the analytics pipeline for the question: ‘In patients hospitalized with COVID-19, what is the risk of death 0 to 30 days after hospitalization?’. We develop models using six different machine learning methods in a USA claims database containing over 20,000 COVID-19 hospitalizations and externally validate the models using data containing over 45,000 COVID-19 hospitalizations from South Korea, Spain, and the USA. Results: Our open-source software tools enabled us to efficiently go end-to-end from problem design to reliable Model Development and evaluation. When predicting death in patients hospitalized with COVID-19, AdaBoost, random forest, gradient boosting machine, and decision tree yielded similar or lower internal and external validation discrimination performance compared to L1-regularized logistic regression, whereas the MLP neural network consistently resulted in lower discrimination. L1-regularized logistic regression models were well calibrated. Conclusion: Our results show that following the OHDSI analytics pipeline for patient-level prediction modelling can enable the rapid development towards reliable prediction models. The OHDSI software tools and pipeline are open source and available to researchers from all around the world.</p

Polarised light sheet tomography

The research leading to these results has received funding from the People Programme (Marie Curie Actions) of the European’s Seventh Framework Programme (FP7/2007-2013) under REA grant agreement no. 608133 and Scottish Funding Council (SFC) Horizon fund.The various benefits of light sheet microscopy have made it a widely used modality for capturing three- dimensional images. It is mostly used for fluorescence imaging, but recently another technique called Light Sheet Tomography solely relying on scattering was presented. The method was successfully applied to imaging of plant roots in transparent soil, but is limited when it comes to more turbid samples. This study presents a Polarised Light Sheet Tomography system and its advantages when imaging in highly scattering turbid media. The experimental configuration is guided by Monte Carlo Radiation Transfer methods, which model the propagation of a polarised light sheet in the sample. Images of both reflecting and absorbing phantoms in a complex collagenous matrix were acquired, and the results for different polarisation configurations are compared. Focus scanning methods were then used to reduce noise and produce three-dimensional reconstructions of absorbing targets.PostprintPeer reviewe

Challenges and opportunities for quantifying roots and rhizosphere interactions through imaging and image analysis

The morphology of roots and root systems influences the efficiency by which plants acquire nutrients and water, anchor themselves and provide stability to the surrounding soil. Plant genotype and the biotic and abiotic environment significantly influence root morphology, growth and ultimately crop yield. The challenge for researchers interested in phenotyping root systems is, therefore, not just to measure roots and link their phenotype to the plant genotype, but also to understand how the growth of roots is influenced by their environment. This review discusses progress in quantifying root system parameters (e.g. in terms of size, shape and dynamics) using imaging and image analysis technologies and also discusses their potential for providing a better understanding of root:soil interactions. Significant progress has been made in image acquisition techniques, however trade-offs exist between sample throughput, sample size, image resolution and information gained. All of these factors impact on downstream image analysis processes. While there have been significant advances in computation power, limitations still exist in statistical processes involved in image analysis. Utilizing and combining different imaging systems, integrating measurements and image analysis where possible, and amalgamating data will allow researchers to gain a better understanding of root:soil interactions