Publications by authors named "Pouria Mashouri"

8 Publications

  • Page 1 of 1

Prospective observational study and serosurvey of SARS-CoV-2 infection in asymptomatic healthcare workers at a Canadian tertiary care center.

PLoS One 2021 16;16(2):e0247258. Epub 2021 Feb 16.

University Health Network, Toronto, Ontario, Canada.

Health care workers (HCWs) are at higher risk for SARS-CoV-2 infection and may play a role in transmitting the infection to vulnerable patients and members of the community. This is particularly worrisome in the context of asymptomatic infection. We performed a cross-sectional study looking at asymptomatic SARS-CoV-2 infection in HCWs. We screened asymptomatic HCWs for SARS-CoV-2 via PCR. Complementary viral genome sequencing was performed on positive swab specimens. A seroprevalence analysis was also performed using multiple assays. Asymptomatic health care worker cohorts had a combined swab positivity rate of 29/5776 (0.50%, 95%CI 0.32-0.75) relative to a comparative cohort of symptomatic HCWs, where 54/1597 (3.4%) tested positive for SARS-CoV-2 (ratio of symptomatic to asymptomatic 6.8:1). SARS-CoV-2 seroprevalence among 996 asymptomatic HCWs with no prior known exposure to SARS-CoV-2 was 1.4-3.4%, depending on assay. A novel in-house Coronavirus protein microarray showed differing SARS-CoV-2 protein reactivities and helped define likely true positives vs. suspected false positives. Our study demonstrates the utility of routine screening of asymptomatic HCWs, which may help to identify a significant proportion of infections.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0247258PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7886177PMC
February 2021

EpigenCentral: Portal for DNA methylation data analysis and classification in rare diseases.

Hum Mutat 2020 Oct 15;41(10):1722-1733. Epub 2020 Jul 15.

Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada.

Epigenetic processes play a key role in regulating gene expression. Genetic variants that disrupt chromatin-modifying proteins are associated with a broad range of diseases, some of which have specific epigenetic patterns, such as aberrant DNA methylation (DNAm), which may be used as disease biomarkers. While much of the epigenetic research has focused on cancer, there is a paucity of resources devoted to neurodevelopmental disorders (NDDs), which include autism spectrum disorder and many rare, clinically overlapping syndromes. To address this challenge, we created EpigenCentral, a free web resource for biomedical researchers, molecular diagnostic laboratories, and clinical practitioners to perform the interactive classification and analysis of DNAm data related to NDDs. It allows users to search for known disease-associated patterns in their DNAm data, classify genetic variants as pathogenic or benign to assist in molecular diagnostics, or analyze patterns of differential methylation in their data through a simple web form. EpigenCentral is freely available at http://epigen.ccm.sickkids.ca/.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/humu.24076DOI Listing
October 2020

Correction: Phenotate: crowdsourcing phenotype annotations as exercises in undergraduate classes.

Genet Med 2020 Aug;22(8):1427

Centre for Computational Medicine, The Hospital For Sick Children, Toronto, ON, Canada.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41436-020-0866-6DOI Listing
August 2020

Phenotate: crowdsourcing phenotype annotations as exercises in undergraduate classes.

Genet Med 2020 08 5;22(8):1391-1400. Epub 2020 May 5.

Centre for Computational Medicine, The Hospital For Sick Children, Toronto, ON, Canada.

Purpose: Computational documentation of genetic disorders is highly reliant on structured data for differential diagnosis, pathogenic variant identification, and patient matchmaking. However, most information on rare diseases (RDs) exists in freeform text, such as academic literature. To increase availability of structured RD data, we developed a crowdsourcing approach for collecting phenotype information using student assignments.

Methods: We developed Phenotate, a web application for crowdsourcing disease phenotype annotations through assignments for undergraduate genetics students. Using student-collected data, we generated composite annotations for each disease through a machine learning approach. These annotations were compared with those from clinical practitioners and gold standard curated data.

Results: Deploying Phenotate in five undergraduate genetics courses, we collected annotations for 22 diseases. Student-sourced annotations showed strong similarity to gold standards, with F-measures ranging from 0.584 to 0.868. Furthermore, clinicians used Phenotate annotations to identify diseases with comparable accuracy to other annotation sources and gold standards. For six disorders, no gold standards were available, allowing us to create some of the first structured annotations for them, while students demonstrated ability to research RDs.

Conclusion: Phenotate enables crowdsourcing RD phenotypic annotations through educational assignments. Presented as an intuitive web-based tool, it offers pedagogical benefits and augments the computable RD knowledgebase.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41436-020-0812-7DOI Listing
August 2020

Quality Indicators as Predictors of Future Inspection Performance in Ontario Nursing Homes.

J Am Med Dir Assoc 2020 06 30;21(6):793-798.e1. Epub 2019 Oct 30.

KITE, Toronto Rehabilitation Institute, University Health Network, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada; Center for Mental Health, University Health Network, Toronto, Ontario, Canada. Electronic address:

Objectives: There are several mechanisms for monitoring the quality of care in long-term care (LTC), including the use of quality indicators derived from resident assessments and formal inspections. The LTC inspection process is time and resource-intensive, and there may be opportunities to better target inspections. In this study, we aimed to examine whether quality indicators could predict future inspection performance in LTC homes across Ontario, Canada.

Setting And Participants: In total, 594 LTC homes across Ontario.

Methods: Using a database compiling detailed inspection reports for the period from 2017 to 2018, we classified each home into 1 of 3 categories (in good standing, needing improvement, needing significant improvement). Machine learning techniques were used to examine whether publicly available Resident Assessment Instrument‒Minimum Data Set quality indicators for the period 2016‒2017 could predict facility classification based on inspection results.

Results: After running a wide range of models, only a weak relationship was found between quality indicators and future inspection performance. The best-performing model was able to achieve a classification accuracy of 40.1%. Feature analysis was performed on the final model to identify which quality indicators were most indicative of predicted poor performance. Experiencing worsened pain, restraint use, and worsened pressure ulcers were correlated with homes predicted as needing significant improvement. Counterintuitively, improved physical functioning had an inverse relationship with homes predicted as being in good standing.

Conclusions And Implications: Most quality indicators are poor predictors of inspection performance. Further work is required to explore the limited relationship between these 2 measures of LTC quality, and to identify other quality measures that may be useful as predictors of facilities facing difficulty in meeting quality standards.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jamda.2019.09.007DOI Listing
June 2020

Expanding the Boundaries of RNA Sequencing as a Diagnostic Tool for Rare Mendelian Disease.

Am J Hum Genet 2019 03 28;104(3):466-483. Epub 2019 Feb 28.

Division of Neurology, the Hospital for Sick Children, Toronto, ON M5G 1X8, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5G 1X8, Canada; Program in Genetics and Genome Biology, Research Institute, the Hospital for Sick Children, Toronto, ON M5G 0A4, Canada. Electronic address:

Gene-panel and whole-exome analyses are now standard methodologies for mutation detection in Mendelian disease. However, the diagnostic yield achieved is at best 50%, leaving the genetic basis for disease unsolved in many individuals. New approaches are thus needed to narrow the diagnostic gap. Whole-genome sequencing is one potential strategy, but it currently has variant-interpretation challenges, particularly for non-coding changes. In this study we focus on transcriptome analysis, specifically total RNA sequencing (RNA-seq), by using monogenetic neuromuscular disorders as proof of principle. We examined a cohort of 25 exome and/or panel "negative" cases and provided genetic resolution in 36% (9/25). Causative mutations were identified in coding and non-coding exons, as well as in intronic regions, and the mutational pathomechanisms included transcriptional repression, exon skipping, and intron inclusion. We address a key barrier of transcriptome-based diagnostics: the need for source material with disease-representative expression patterns. We establish that blood-based RNA-seq is not adequate for neuromuscular diagnostics, whereas myotubes generated by transdifferentiation from an individual's fibroblasts accurately reflect the muscle transcriptome and faithfully reveal disease-causing mutations. Our work confirms that RNA-seq can greatly improve diagnostic yield in genetically unresolved cases of Mendelian disease, defines strengths and challenges of the technology, and demonstrates the suitability of cell models for RNA-based diagnostics. Our data set the stage for development of RNA-seq as a powerful clinical diagnostic tool that can be applied to the large population of individuals with undiagnosed, rare diseases and provide a framework for establishing minimally invasive strategies for doing so.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.ajhg.2019.01.012DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6407525PMC
March 2019
-->