Platelet signaling at the nexus of innate immunity and rheumatoid arthritis.

Rheumatoid arthritis (RA) is a debilitating autoimmune disorder characterized by chronic inflammation of the synovial tissues and progressive destruction of bone and cartilage. The inflammatory response and subsequent tissue degradation are orchestrated by complex signaling networks between immune cells and their products in the blood, vascular endothelia and the connective tissue cells residing in the joints. Platelets are recognized as immune-competent cells with an important role in chronic inflammatory diseases such as RA. Here we review the specific aspects of platelet function relevant to arthritic disease, including current knowledge of the molecular crosstalk between platelets and other innate immune cells that modulate RA pathogenesis.

Evaluating Use Cases for Human Challenge Trials in Accelerating SARS-CoV-2 Vaccine Development.

Human challenge trials (HCTs) have been proposed as a means to accelerate SARS-CoV-2 vaccine development. We identify and discuss 3 potential use cases of HCTs in the current pandemic: evaluating efficacy, converging on correlates of protection, and improving understanding of pathogenesis and the human immune response. We outline the limitations of HCTs and find that HCTs are likely to be most useful for vaccine candidates currently in preclinical stages of development. We conclude that, while currently limited in their application, there are scenarios in which HCTs would be extremely beneficial. Therefore, the option of conducting HCTs to accelerate SARS-CoV-2 vaccine development should be preserved. As HCTs require many months of preparation, we recommend an immediate effort to (1) establish guidelines for HCTs for COVID-19; (2) take the first steps toward HCTs, including preparing challenge virus and making preliminary logistical arrangements; and (3) commit to periodically re-evaluating the utility of HCTs.

Multi-Ontology Refined Embeddings (MORE): A hybrid multi-ontology and corpus-based semantic representation model for biomedical concepts.

OBJECTIVE: Currently, a major limitation for natural language processing (NLP) analyses in clinical applications is that concepts are not effectively referenced in various forms across different texts. This paper introduces Multi-Ontology Refined Embeddings (MORE), a novel hybrid framework that incorporates domain knowledge from multiple ontologies into a distributional semantic model, learned from a corpus of clinical text. MATERIALS AND METHODS: We use the RadCore and MIMIC-III free-text datasets for the corpus-based component of MORE. For the ontology-based part, we use the Medical Subject Headings (MeSH) ontology and three state-of-the-art ontology-based similarity measures. In our approach, we propose a new learning objective, modified from the sigmoid cross-entropy objective function. RESULTS AND DISCUSSION: We used two established datasets of semantic similarities among biomedical concept pairs to evaluate the quality of the generated word embeddings. On the first dataset with 29 concept pairs, with similarity scores established by physicians and medical coders, MORE's similarity scores have the highest combined correlation (0.633), which is 5.0% higher than that of the baseline model, and 12.4% higher than that of the best ontology-based similarity measure. On the second dataset with 449 concept pairs, MORE's similarity scores have a correlation of 0.481, based on the average of four medical residents' similarity ratings, and that outperforms the skip-gram model by 8.1%, and the best ontology measure by 6.9%. Furthermore, MORE outperforms three pre-trained transformer-based word embedding models (i.e., BERT, ClinicalBERT, and BioBERT) on both datasets. CONCLUSION: MORE incorporates knowledge from several biomedical ontologies into an existing corpus-based distributional semantics model, improving both the accuracy of the learned word embeddings and the extensibility of the model to a broader range of biomedical concepts. MORE allows for more accurate clustering of concepts across a wide range of applications, such as analyzing patient health records to identify subjects with similar pathologies, or integrating heterogeneous clinical data to improve interoperability between hospitals.

Evaluating effectiveness of information visualizations using cognitive fit theory: A neuroergonomics approach.

Information visualizations may be evaluated from the perspective of how they match tasks that must be performed with them, a cognitive fit perspective. However, there is a gap between the high-level references made to cognitive fit and the low-level ability to identify and measure it during human interaction with visualizations. We bridge this gap by using an electroencephalography metric derived from frontal midline theta power and parietal alpha power, known as the task load index, to determine if cognitive effort measured at the level of cortical activity is less when cognitive fit is present compared to when cognitive fit is not. We found that when there is cognitive fit between the type of problem to be solved and the information displayed by a system, the task load index is lower compared to when cognitive fit is not present. We support this finding with subjective (NASA task load index) and performance (response time and accuracy) measures. Our approach, using electroencephalography, provides supplemental information to self-report and performance measures. Findings from this study are important because they (1) provide more validity to the cognitive fit theory using a neurophysiological measure, and (2) use the electroencephalography task load index metric as a means to assess cognitive workload and effort in general.

Automatic post-stroke lesion segmentation on MR images using 3D residual convolutional neural network.

In this paper, we demonstrate the feasibility and performance of deep residual neural networks for volumetric segmentation of irreversibly damaged brain tissue lesions on T1-weighted MRI scans for chronic stroke patients. A total of 239 T1-weighted MRI scans of chronic ischemic stroke patients from a public dataset were retrospectively analyzed by 3D deep convolutional segmentation models with residual learning, using a novel zoom-in&out strategy. Dice similarity coefficient (DSC), average symmetric surface distance (ASSD), and Hausdorff distance (HD) of the identified lesions were measured by using manual tracing of lesions as the reference standard. Bootstrapping was employed for all metrics to estimate 95% confidence intervals. The models were assessed on a test set of 31 scans. The average DSC was 0.64 (0.51-0.76) with a median of 0.78. ASSD and HD were 3.6 mm (1.7-6.2 mm) and 20.4 mm (10.0-33.3 mm), respectively. The latest deep learning architecture and techniques were applied with 3D segmentation on MRI scans and demonstrated effectiveness for volumetric segmentation of chronic ischemic stroke lesions.

Middle-aged death and taxes in the USA: Association of state tax burden and expenditures in 2005 with survival from 2006 to 2015.

BACKGROUND: Longevity in the United States ranks below most other Western nations despite spending more on healthcare per capita than any other country. Across the world, mortality has been declining, but in the USA the trend toward improvement has stalled in some middle-aged demographic groups. Cross-national studies suggest that social welfare is positively associated with longevity. The United States has less government sponsored welfare, education and healthcare than almost all other Western nations, but the level of this social welfare commitment varies across the states. In this study we examined the association of state tax burden and state government expenditures with subsequent middle-aged mortality. METHODS: The primary exposure was state tax burden in 2005, defined as proportion of all state income paid to the state. We also examined the impact of state expenditures per capita in 2005 for education, healthcare, welfare, police and highways. The dependent variable was mortality during the subsequent 10 years. Death counts and population sizes by sex, age group and race strata for 2006-2015 were abstracted from CDC WONDER. Binomial logistic regression was employed based on the number of deaths and underlying population within each county-sex-age-race bin. RESULTS: State tax burden in 2005 varied from 5.8% to 12.2%. An increase of 1.0 percentage point in state tax burden was associated with a 5.8% (SE = 0.1%) reduction in mortality adjusted for sex, age and race, but was associated with a 1.1% (SE = 0.1%) reduction when further adjusting for state income and education levels. Controlling for sex, age and race each type of state expenditures was associated with decreases in middle aged mortality, notably K-12 education (reduction of 4.7%, SE = 0.1%, per 10% expenditure increase) except healthcare but all types were associated with mortality decreases further controlling for state income and education. CONCLUSION: The residents of states with higher state taxation and higher expenditures per capita have lower middle aged mortality rates.

Usability testing of existing type 2 diabetes mellitus websites.

BACKGROUND: Given the significant increase in the use of the internet as an educational tool for diabetes, very little research has been published on the usability of healthcare websites, even though it is a determining factor for user satisfaction. OBJECTIVE: The aim of this study is to evaluate and critique the interfaces of existing diabetes websites for usability concerns and provide design solutions for improvement. Emphasis is placed on Type 2 Diabetes Mellitus since it is the most common and life threatening form of diabetes. METHOD: A usability test was performed on the interfaces of three existing diabetes websites, American Diabetes Association (www.diabetes.org), WebMD (www.webmd.com) and the National Diabetes Education Program (ndep.nih.gov). The goal was to collect qualitative and quantitative data to determine: (1) if participants are able to complete specified tasks successfully; (2) the length of time it takes participants to complete the specified tasks and; (3) participants' satisfaction with the three websites. Twenty adults, 18 years of age and older participated in the study. RESULTS: The results from the MANOVA test revealed a significant difference between the three websites for number of clicks, number of errors and completion time when analyzed simultaneously. The ANOVA tests revealed a significant difference for all three variables. The Student-Newman-Keuls (SNK) test shows a significant difference for completion time between American Diabetes Association and WebMD. A significant difference was found for the number of clicks for the National Diabetes Education Program compared to the American Diabetes Association and WebMD. However, no significant difference was found for the number of clicks between American Diabetes Association and WebMD. Lastly, a significant difference was found between each interface for number of errors. DISCUSSION: Although, the American Diabetes Association web-interface was most favorable, there were many positive design elements for each interface. On the other hand, the significant amount of information overload experienced for each website left participants feeling perplexed. Thus, innovative solutions are needed to reduce information overload and ensure users are engaged and empowered to make informed decisions about their healthcare.

A regulatory toolbox of MiniPromoters to drive selective expression in the brain.

The Pleiades Promoter Project integrates genomewide bioinformatics with large-scale knockin mouse production and histological examination of expression patterns to develop MiniPromoters and related tools designed to study and treat the brain by directed gene expression. Genes with brain expression patterns of interest are subjected to bioinformatic analysis to delineate candidate regulatory regions, which are then incorporated into a panel of compact human MiniPromoters to drive expression to brain regions and cell types of interest. Using single-copy, homologous-recombination "knockins" in embryonic stem cells, each MiniPromoter reporter is integrated immediately 5' of the Hprt locus in the mouse genome. MiniPromoter expression profiles are characterized in differentiation assays of the transgenic cells or in mouse brains following transgenic mouse production. Histological examination of adult brains, eyes, and spinal cords for reporter gene activity is coupled to costaining with cell-type-specific markers to define expression. The publicly available Pleiades MiniPromoter Project is a key resource to facilitate research on brain development and therapies.

The PAZAR database of gene regulatory information coupled to the ORCA toolkit for the study of regulatory sequences.

The PAZAR database unites independently created and maintained data collections of transcription factor and regulatory sequence annotation. The flexible PAZAR schema permits the representation of diverse information derived from experiments ranging from biochemical protein-DNA binding to cellular reporter gene assays. Data collections can be made available to the public, or restricted to specific system users. The data 'boutiques' within the shopping-mall-inspired system facilitate the analysis of genomics data and the creation of predictive models of gene regulation. Since its initial release, PAZAR has grown in terms of data, features and through the addition of an associated package of software tools called the ORCA toolkit (ORCAtk). ORCAtk allows users to rapidly develop analyses based on the information stored in the PAZAR system. PAZAR is available at http://www.pazar.info. ORCAtk can be accessed through convenient buttons located in the PAZAR pages or via our website at http://www.cisreg.ca/ORCAtk.