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Tobias Strunk MD, PhD, FRACP Head, Neonatal Health tobias.strunk@thekids.org.au Head, Neonatal Health Clinical Professor Tobias Strunk is a
Citation: Davis JW, Stewart M. Data collection in neonatal retrieval medicine: a platform for research and improvement. Arch Dis Child Fetal Neonatal
We compared mortality and morbidity of inborn versus outborn very preterm infants <32 weeks' gestation in Western Australia (WA) between 2005 and 2018
The purpose of this study was to characterise neonatal Staphylococcus aureus (SA) sepsis in Western Australia (WA) between 2001 and 2020 at the sole tertiary neonatal intensive care unit (NICU), examine risk factors for sepsis in the cohort, and compare short- and long-term outcomes to control infants without any sepsis.
Developmental thymic waves of innate-like and adaptive-like γδ T cells have been described, but the current understanding of γδ T cell development is mainly limited to mouse models.
Preterm birth and subsequent neonatal ventilatory treatment disrupts development of the hypoxic ventilatory response (HVR). An attenuated HVR has been identified in preterm neonates, however it is unknown whether the attenuation persists into the second year of life.
While a systematic review exists detailing neonatal sepsis outcomes from clinical trials, there remains an absence of a qualitative systematic review capturing the perspectives of key stakeholders.
To investigate the physicochemical compatibility of caffeine citrate and caffeine base injections with 43 secondary intravenous drugs used in Neonatal Intensive Care Unit settings.
With advances in perinatal care, we have achieved major reductions in mortality in premature and critically ill infants, but they still remain at increased risk of neurodevelopmental disability. In this context, recent advances in neuroimaging are perceived as an addition of significant value to current clinical developmental screening programs.
Machine learning (ML) algorithms are powerful tools that are increasingly being used for sepsis biomarker discovery in RNA-Seq data. RNA-Seq datasets contain multiple sources and types of noise (operator, technical and non-systematic) that may bias ML classification. Normalisation and independent gene filtering approaches described in RNA-Seq workflows account for some of this variability and are typically only targeted at differential expression analysis rather than ML applications.