Christopher Stem
Wake Forest Institute for Regenerative Medicine
6 Papers
13 Citations
Christopher Stem is an academic researcher from Wake Forest Institute for Regenerative Medicine. The author has contributed to research in topics: Medicine & Internal medicine. The author has an hindex of 1, co-authored 2 publications.
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Papers
Gene therapy: the promise of a permanent cure.
TL;DR: Gene therapy offers the possibility of a permanent cure for any of the more than 10,000 human diseases caused by a defect in a single gene, and studies in both animals and humans have provided evidence that a permanent Cure for hemophilia is within reach.
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Investigating Optimal Autologous Cellular Platforms for Prenatal or Perinatal Factor VIII Delivery to Treat Hemophilia A.
Christopher Stem,Christopher Rodman,Ritu M Ramamurthy,Sunil George,Diane Meares,Andrew M. Farland,Anthony Atala,Christopher B. Doering,H. Trent Spencer,Christopher D. Porada,Graça Almeida-Porada +10 more
TL;DR: The authors evaluated a panel of readily available cell types for their suitability as cellular vehicles to deliver long-lasting FVIII replacement following transduction with a retroviral vector encoding a B domain-deleted human F8 transgene.
Effect of ketamine on transcranial Doppler Gosling pulsatility index in children undergoing procedural sedation: A pilot study
TL;DR: The objective of this study was to quantify Gosling pulsatility index changes as a surrogate marker for ICP changes in previously healthy children receiving intravenous ketamine for procedural sedation.
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Peer Review
Pediatric Procedure Curriculum Initiative
TL;DR: Amandatory longitudinal procedure curriculum improved procedural comfort level among pediatric residents with the introduction of a mandatory longitudinal pediatric procedural curriculum, including simulation in combination with online modules.
345 Creating a Deep Learning Classifier for the Detection of Soft Tissue Infections Using Point-of-Care Ultrasound Images
N Li,Nicola DiPlacido,Ryan M Barnes,Aalap J. Shah,Haden Smith,Evan Verplancken,Christopher Stem,Matthew M. Moake,C. Oliva,E. Cummings +9 more
TL;DR: In this article , the authors performed a pilot study with the goal to train and assess the performance of a deep neural network model for automatically classifying ultrasound images into three groups: normal soft tissue, cellulitis and abscess.