Ensure Follow-up for Functional Issues Post COVID-19

What\u27s the Problem? Patients post COVID-19 infection have functional limitations at home related to the multi-system effects of the infection, in addition to elevated risk for post-intensive care syndrome. Our goals were to ensure patients had the resources they needed to safely mobilize at home, ensure referral to appropriate agencies for home care and therapies, and provide guidance on return to life activities in the setting of the stay-at-home order, need to self-quarantine, and limited outpatient therapy options

Screening Tool to Ensure Optimal Patient Care and Function Post COVID Diagnosis

We developed a screening tool of questions to determine which patients would benefit from outpatient PM&R visits after discharge from the hospital/ED that included: four questions looking at function, shortness of breath and cognition. Screening questions were created so a YES response triggers a referral to PM&R to assess functional and cognitive needs for the patient

Friendship as a Political Concept: A Groundwork for Analysis

What kind of a concept is friendship, and what is its connection to politics? Critics sometimes claim that friendship does not have a role to play in the study of politics. Such objections misconstrue the nature of the concept of friendship and its relation to politics. In response, this article proposes three approaches to understanding the concept of friendship: (1) as a ‘family resemblance’ concept, (2) as an instance of an ‘essentially contested’ concept, and (3) as a concept indicating a problématique. The article thus responds to the dismissal of friendship by undertaking the groundwork for understanding what kind of a concept friendship might be, and how it might serve different purposes. In doing so, it opens the way for understanding friendship’s relation to politics

Intuitive Visualization and Analysis of Multi-Omics Data and Application to Escherichia coli Carbon Metabolism

Combinations of ‘omics’ investigations (i.e, transcriptomic, proteomic, metabolomic and/or fluxomic) are increasingly applied to get comprehensive understanding of biological systems. Because the latter are organized as complex networks of molecular and functional interactions, the intuitive interpretation of multi-omics datasets is difficult. Here we describe a simple strategy to visualize and analyze multi-omics data. Graphical representations of complex biological networks can be generated using Cytoscape where all molecular and functional components could be explicitly represented using a set of dedicated symbols. This representation can be used i) to compile all biologically-relevant information regarding the network through web link association, and ii) to map the network components with multi-omics data. A Cytoscape plugin was developed to increase the possibilities of both multi-omic data representation and interpretation. This plugin allowed different adjustable colour scales to be applied to the various omics data and performed the automatic extraction and visualization of the most significant changes in the datasets. For illustration purpose, the approach was applied to the central carbon metabolism of Escherichia coli. The obtained network contained 774 components and 1232 interactions, highlighting the complexity of bacterial multi-level regulations. The structured representation of this network represents a valuable resource for systemic studies of E. coli, as illustrated from the application to multi-omics data. Some current issues in network representation are discussed on the basis of this work

Bioclipse: an open source workbench for chemo- and bioinformatics

BACKGROUND: There is a need for software applications that provide users with a complete and extensible toolkit for chemo- and bioinformatics accessible from a single workbench. Commercial packages are expensive and closed source, hence they do not allow end users to modify algorithms and add custom functionality. Existing open source projects are more focused on providing a framework for integrating existing, separately installed bioinformatics packages, rather than providing user-friendly interfaces. No open source chemoinformatics workbench has previously been published, and no sucessful attempts have been made to integrate chemo- and bioinformatics into a single framework. RESULTS: Bioclipse is an advanced workbench for resources in chemo- and bioinformatics, such as molecules, proteins, sequences, spectra, and scripts. It provides 2D-editing, 3D-visualization, file format conversion, calculation of chemical properties, and much more; all fully integrated into a user-friendly desktop application. Editing supports standard functions such as cut and paste, drag and drop, and undo/redo. Bioclipse is written in Java and based on the Eclipse Rich Client Platform with a state-of-the-art plugin architecture. This gives Bioclipse an advantage over other systems as it can easily be extended with functionality in any desired direction. CONCLUSION: Bioclipse is a powerful workbench for bio- and chemoinformatics as well as an advanced integration platform. The rich functionality, intuitive user interface, and powerful plugin architecture make Bioclipse the most advanced and user-friendly open source workbench for chemo- and bioinformatics. Bioclipse is released under Eclipse Public License (EPL), an open source license which sets no constraints on external plugin licensing; it is totally open for both open source plugins as well as commercial ones. Bioclipse is freely available at

SIDEKICK: Genomic data driven analysis and decision-making framework

<p>Abstract</p> <p>Background</p> <p>Scientists striving to unlock mysteries within complex biological systems face myriad barriers in effectively integrating available information to enhance their understanding. While experimental techniques and available data sources are rapidly evolving, useful information is dispersed across a variety of sources, and sources of the same information often do not use the same format or nomenclature. To harness these expanding resources, scientists need tools that bridge nomenclature differences and allow them to integrate, organize, and evaluate the quality of information without extensive computation.</p> <p>Results</p> <p>Sidekick, a genomic data driven analysis and decision making framework, is a web-based tool that provides a user-friendly intuitive solution to the problem of information inaccessibility. Sidekick enables scientists without training in computation and data management to pursue answers to research questions like "What are the mechanisms for disease X" or "Does the set of genes associated with disease X also influence other diseases." Sidekick enables the process of combining heterogeneous data, finding and maintaining the most up-to-date data, evaluating data sources, quantifying confidence in results based on evidence, and managing the multi-step research tasks needed to answer these questions. We demonstrate Sidekick's effectiveness by showing how to accomplish a complex published analysis in a fraction of the original time with no computational effort using Sidekick.</p> <p>Conclusions</p> <p>Sidekick is an easy-to-use web-based tool that organizes and facilitates complex genomic research, allowing scientists to explore genomic relationships and formulate hypotheses without computational effort. Possible analysis steps include gene list discovery, gene-pair list discovery, various enrichments for both types of lists, and convenient list manipulation. Further, Sidekick's ability to characterize pairs of genes offers new ways to approach genomic analysis that traditional single gene lists do not, particularly in areas such as interaction discovery.</p

Seeded Bayesian Networks: Constructing genetic networks from microarray data

<p>Abstract</p> <p>Background</p> <p>DNA microarrays and other genomics-inspired technologies provide large datasets that often include hidden patterns of correlation between genes reflecting the complex processes that underlie cellular metabolism and physiology. The challenge in analyzing large-scale expression data has been to extract biologically meaningful inferences regarding these processes – often represented as networks – in an environment where the datasets are often imperfect and biological noise can obscure the actual signal. Although many techniques have been developed in an attempt to address these issues, to date their ability to extract meaningful and predictive network relationships has been limited. Here we describe a method that draws on prior information about gene-gene interactions to infer biologically relevant pathways from microarray data. Our approach consists of using preliminary networks derived from the literature and/or protein-protein interaction data as seeds for a Bayesian network analysis of microarray results.</p> <p>Results</p> <p>Through a bootstrap analysis of gene expression data derived from a number of leukemia studies, we demonstrate that seeded Bayesian Networks have the ability to identify high-confidence gene-gene interactions which can then be validated by comparison to other sources of pathway data.</p> <p>Conclusion</p> <p>The use of network seeds greatly improves the ability of Bayesian Network analysis to learn gene interaction networks from gene expression data. We demonstrate that the use of seeds derived from the biomedical literature or high-throughput protein-protein interaction data, or the combination, provides improvement over a standard Bayesian Network analysis, allowing networks involving dynamic processes to be deduced from the static snapshots of biological systems that represent the most common source of microarray data. Software implementing these methods has been included in the widely used TM4 microarray analysis package.</p

Spatial Extent of Charge Repulsion Regulates Assembly Pathways for Lysozyme Amyloid Fibrils

Formation of large protein fibrils with a characteristic cross β-sheet architecture is the key indicator for a wide variety of systemic and neurodegenerative amyloid diseases. Recent experiments have strongly implicated oligomeric intermediates, transiently formed during fibril assembly, as critical contributors to cellular toxicity in amyloid diseases. At the same time, amyloid fibril assembly can proceed along different assembly pathways that might or might not involve such oligomeric intermediates. Elucidating the mechanisms that determine whether fibril formation proceeds along non-oligomeric or oligomeric pathways, therefore, is important not just for understanding amyloid fibril assembly at the molecular level but also for developing new targets for intervening with fibril formation. We have investigated fibril formation by hen egg white lysozyme, an enzyme for which human variants underlie non-neuropathic amyloidosis. Using a combination of static and dynamic light scattering, atomic force microscopy and circular dichroism, we find that amyloidogenic lysozyme monomers switch between three different assembly pathways: from monomeric to oligomeric fibril assembly and, eventually, disordered precipitation as the ionic strength of the solution increases. Fibril assembly only occurred under conditions of net repulsion among the amyloidogenic monomers while net attraction caused precipitation. The transition from monomeric to oligomeric fibril assembly, in turn, occurred as salt-mediated charge screening reduced repulsion among individual charged residues on the same monomer. We suggest a model of amyloid fibril formation in which repulsive charge interactions are a prerequisite for ordered fibril assembly. Furthermore, the spatial extent of non-specific charge screening selects between monomeric and oligomeric assembly pathways by affecting which subset of denatured states can form suitable intermolecular bonds and by altering the energetic and entropic requirements for the initial intermediates emerging along the monomeric vs. oligomeric assembly path

Quantitative utilization of prior biological knowledge in the Bayesian network modeling of gene expression data

<p>Abstract</p> <p>Background</p> <p>Bayesian Network (BN) is a powerful approach to reconstructing genetic regulatory networks from gene expression data. However, expression data by itself suffers from high noise and lack of power. Incorporating prior biological knowledge can improve the performance. As each type of prior knowledge on its own may be incomplete or limited by quality issues, integrating multiple sources of prior knowledge to utilize their consensus is desirable.</p> <p>Results</p> <p>We introduce a new method to incorporate the quantitative information from multiple sources of prior knowledge. It first uses the Naïve Bayesian classifier to assess the likelihood of functional linkage between gene pairs based on prior knowledge. In this study we included cocitation in PubMed and schematic similarity in Gene Ontology annotation. A candidate network edge reservoir is then created in which the copy number of each edge is proportional to the estimated likelihood of linkage between the two corresponding genes. In network simulation the Markov Chain Monte Carlo sampling algorithm is adopted, and samples from this reservoir at each iteration to generate new candidate networks. We evaluated the new algorithm using both simulated and real gene expression data including that from a yeast cell cycle and a mouse pancreas development/growth study. Incorporating prior knowledge led to a ~2 fold increase in the number of known transcription regulations recovered, without significant change in false positive rate. In contrast, without the prior knowledge BN modeling is not always better than a random selection, demonstrating the necessity in network modeling to supplement the gene expression data with additional information.</p> <p>Conclusion</p> <p>our new development provides a statistical means to utilize the quantitative information in prior biological knowledge in the BN modeling of gene expression data, which significantly improves the performance.</p