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An evaluation of GPT models for phenotype concept recognitionClinical deep phenotyping and phenotype annotation play a critical role in both the diagnosis of patients with rare disorders as well as in building computationally-tractable knowledge in the rare disorders field.
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Rare disease education in Europe and beyond: time to actPeople living with rare diseases (PLWRD) still face huge unmet needs, in part due to the fact that care systems are not sufficiently aligned with their needs and healthcare workforce (HWF) along their care pathways lacks competencies to efficiently tackle rare disease-specific challenges. Level of rare disease knowledge and awareness among the current and future HWF is insufficient.
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Personalised analytics for rare disease diagnosticsHere we focus on the problem of prioritising variants with respect to the observed disease phenotype
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CAGE-defined promoter regions of the genes implicated in Rett SyndromeA comprehensive picture of the regulatory regions of the three genes involved in Rett Syndrome
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Promoter-level expression clustering identifies time development of transcriptional regulatory cascades initiated by ERBB receptors in breast cancer cellsThe analysis of CAGE (Cap Analysis of Gene Expression) time-courses has been applied to examine the dynamics of enhancer and promoter by sequentially...
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A phenotype centric benchmark of variant prioritisation toolsWe hypothesised that the performance of variant prioriisation tools may vary by disease phenotype.
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Timo LassmannFeilman Fellow; Head, Precision Health Research and Head, Computational Biology
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Bilateral murine tumor models for characterizing the response to immune checkpoint blockadeThis protocol describes bilateral murine tumor models that display a symmetrical yet dichotomous response to immune checkpoint blockade
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Expression Levels of Therapeutic Targets as Indicators of Sensitivity to Targeted TherapeuticsWe investigated the relationship between the sensitivity of hundreds of cell lines to hundreds of drugs, and the relative expression levels of the targets
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Electrostatic Filters to Reduce COVID-19 Spread in Bubble CPAP: An in vitro Study of Safety and EfficacyBubble CPAP may be used in infants with suspected or confirmed COVID-19. Electrostatic filters may reduce cross infection. This study aims to determine if including a filter in the bubble CPAP circuit impacts stability of pressure delivery. A new electrostatic filter was placed before (pre) or after (post) the bubble CPAP generator, or with no filter (control) in an in vitro study. Pressure was recorded at the nasal interface for 18 h (6 L/min; 7 cm H2O) on 3 occasions for each configuration. Filter failure was defined as pressure >9 cm H2O for 60 continuous minutes. The filter was weighed before and after each experiment.