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Epstein AA, Janos SN, Menozzi L, Pegram K, Jain V, Bisset LC, Davis JT, Morrison S, Shailaja A, Guo Y, Chao AS, Abdi K, Rikard B, Yao J, Gregory SG, Fisher K, Pittman R, Erkanli A, Gustafson KE, Carrico CWT, Malcolm WF, Inder TE, Cotten CM, Burt TD, Shinohara ML, Maxfield CM, Benner EJ. Subventricular zone stem cell niche injury is associated with intestinal perforation in preterm infants and predicts future motor impairment. Cell Stem Cell 2024; 31:467-483.e6. [PMID: 38537631 DOI: 10.1016/j.stem.2024.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Revised: 02/11/2024] [Accepted: 03/01/2024] [Indexed: 04/07/2024]
Abstract
Brain injury is highly associated with preterm birth. Complications of prematurity, including spontaneous or necrotizing enterocolitis (NEC)-associated intestinal perforations, are linked to lifelong neurologic impairment, yet the mechanisms are poorly understood. Early diagnosis of preterm brain injuries remains a significant challenge. Here, we identified subventricular zone echogenicity (SVE) on cranial ultrasound in preterm infants following intestinal perforations. The development of SVE was significantly associated with motor impairment at 2 years. SVE was replicated in a neonatal mouse model of intestinal perforation. Examination of the murine echogenic subventricular zone (SVZ) revealed NLRP3-inflammasome assembly in multiciliated FoxJ1+ ependymal cells and a loss of the ependymal border in this postnatal stem cell niche. These data suggest a mechanism of preterm brain injury localized to the SVZ that has not been adequately considered. Ultrasound detection of SVE may serve as an early biomarker for neurodevelopmental impairment after inflammatory disease in preterm infants.
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Affiliation(s)
- Adrian A Epstein
- Department of Pediatrics, Division of Neonatology, Duke University School of Medicine, Durham, NC, USA
| | - Sara N Janos
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Luca Menozzi
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Kelly Pegram
- Department of Pediatrics, Division of Neonatology, Duke University School of Medicine, Durham, NC, USA
| | - Vaibhav Jain
- Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, NC, USA
| | - Logan C Bisset
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Joseph T Davis
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Samantha Morrison
- Department of Biostatistics & Bioinformatics, Duke University School of Medicine, Durham, NC, USA
| | - Aswathy Shailaja
- Department of Pediatrics, Division of Neonatology, Duke University School of Medicine, Durham, NC, USA
| | - Yingqiu Guo
- Department of Integrative Immunobiology, Duke University School of Medicine, Durham, NC 27710, USA
| | - Agnes S Chao
- Department of Pediatrics, Division of Neonatology, Duke University School of Medicine, Durham, NC, USA
| | - Khadar Abdi
- Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, NC, USA
| | - Blaire Rikard
- Department of Pediatrics, Division of Neonatology, Duke University School of Medicine, Durham, NC, USA
| | - Junjie Yao
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Simon G Gregory
- Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, NC, USA; Department of Neurosurgery, Duke University School of Medicine, Durham, NC, USA
| | - Kimberley Fisher
- Department of Pediatrics, Division of Neonatology, Duke University School of Medicine, Durham, NC, USA
| | - Rick Pittman
- Department of Pediatrics, Division of Neonatology, Duke University School of Medicine, Durham, NC, USA
| | - Al Erkanli
- Department of Biostatistics & Bioinformatics, Duke University School of Medicine, Durham, NC, USA
| | - Kathryn E Gustafson
- Department of Pediatrics, Division of Neonatology, Duke University School of Medicine, Durham, NC, USA
| | | | - William F Malcolm
- Department of Pediatrics, Division of Neonatology, Duke University School of Medicine, Durham, NC, USA
| | - Terrie E Inder
- Department of Pediatric Newborn Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - C Michael Cotten
- Department of Pediatrics, Division of Neonatology, Duke University School of Medicine, Durham, NC, USA
| | - Trevor D Burt
- Department of Pediatrics, Division of Neonatology, Duke University School of Medicine, Durham, NC, USA; Children's Health and Discovery Initiative, Duke University School of Medicine, Durham, NC, USA
| | - Mari L Shinohara
- Department of Integrative Immunobiology, Duke University School of Medicine, Durham, NC 27710, USA; Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC 27710, USA
| | - Charles M Maxfield
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA.
| | - Eric J Benner
- Department of Pediatrics, Division of Neonatology, Duke University School of Medicine, Durham, NC, USA; Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, NC, USA.
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Li X, Samei E, Barnhart HX, Gaca AM, Hollingsworth CL, Maxfield CM, Carrico CWT, Colsher JG, Frush DP. Lung nodule detection in pediatric chest CT: quantitative relationship between image quality and radiologist performance. Med Phys 2011; 38:2609-18. [PMID: 21776798 DOI: 10.1118/1.3582975] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE To determine the quantitative relationship between image quality and radiologist performance in detecting small lung nodules in pediatric CT. METHODS The study included clinical chest CT images of 30 pediatric patients (0-16 years) scanned at tube currents of 55-180 mA. Calibrated noise addition software was used to simulate cases at three nominal mA settings: 70, 35, and 17.5 mA, resulting in quantum noise of 7-32 Hounsfield Unit (HU). Using a validated nodule simulation technique, lung nodules with diameters of 3-5 mm and peak contrasts of 200-500 HU were inserted into the cases, which were then randomized and rated independently by four experienced pediatric radiologists for nodule presence on a continuous scale from 0 (definitely absent) to 100 (definitely present). The receiver operating characteristic (ROC) data were analyzed to quantify the relationship between diagnostic accuracy (area under the ROC curve, AUC) and image quality (the product of nodule peak contrast and displayed diameter to noise ratio, CDNR display). RESULTS AUC increased rapidly from 0.70 to 0.87 when CDNR display increased from 60 to 130 mm, followed by a slow increase to 0.94 when CDNR display further increased to 257 mm. For the average nodule diameter (4 mm) and contrast (350 HU), AUC decreased from 0.93 to 0.71 with noise increased from 7 to 28 HU. CONCLUSIONS We quantified the relationship between image quality and the performance of radiologists in detecting lung nodules in pediatric CT. The relationship can guide CT protocol design to achieve the desired diagnostic performance at the lowest radiation dose.]
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Affiliation(s)
- Xiang Li
- Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, Durham, North Carolina 27705, USA
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