Ultrasonographic Estimation of Total Brain Volume: 3D Reliability and 2D Estimation. Enabling Routine Estimation During NICU Admission in the Preterm Infant

Objectives: The aim of this study is to explore if manually segmented total brain volume (TBV) from 3D ultrasonography (US) is comparable to TBV estimated by magnetic resonance imaging (MRI). We then wanted to test 2D based TBV estimation obtained through three linear axes which would enable monitoring brain growth in the preterm infant during admission. Methods: We included very low birth weight preterm infants admitted to our neonatal intensive care unit (NICU) with normal neuroimaging findings. We measured biparietal diameter, anteroposterior axis, vertical axis from US and MRI and TBV from both MRI and 3D US. We calculated intra- and interobserver agreement within and between techniques using the intraclass correlation coefficient and Bland-Altman methodology. We then developed a multilevel prediction model of TBV based on linear measurements from both US and MRI, compared them and explored how they changed with increasing age. The multilevel prediction model for TBV from linear measures was tested for internal and external validity and we developed a reference table for ease of prediction of TBV. Results: We used measurements obtained from 426 US and 93 MRI scans from 118 patients. We found good intra- and interobserver agreement for all the measurements. US measurements were reliable when compared to MRI, including TBV which achieved excellent agreement with that of MRI [ICC of 0.98 (95% CI 0.96-0.99)]. TBV estimated through 2D measurements of biparietal diameter, anteroposterior axis, and vertical axis was comparable among both techniques. We estimated the population 95% confidence interval for the mean values of biparietal diameter, anteroposterior axis, vertical axis, and total brain volume by post-menstrual age. A TBV prediction table based on the three axes is proposed to enable easy implementation of TBV estimation in routine 2D US during admission in the NICU. Conclusions: US measurements of biparietal diameter, vertical axis, and anteroposterior axis are reliable. TBV segmented through 3D US is comparable to MRI estimated TBV. 2D US accurate estimation of TBV is possible through biparietal diameter, vertical, and anteroposterior axes.This work was supported by the 2017 (PI0052/2017) and 2019 (ITI-0019-2019) ITI-Cadiz integrated territorial initiative for biomedical research European Regional Development Fund (ERDF) 2014-2020. Andalusian Ministry of Health and Families, Spain

Control of tHe structure of marine phytoplAnkton cOmmunities by turbulence and nutrient supply dynamicS (CHAOS)

extended abstract del posterIn order to investigate the role of turbulence mixing on structuring marine phytoplankton communities, the CHAOS project included a multidisciplinary approach involving specifically designed field observations supported by remote sensing, database analyses, and modeling and laboratory chemostat experiments. Field observations carried out in the outer part of Ría de Vigo in summer 2013 showed that, as a result of increased mixing levels, nitrate diffusive input into the euphotic layer was approximately 4-fold higher during spring tides. This nitrate supply could contribute to explain the continuous dominance of large-sized phytoplankton during the upwelling favorable season. Simultaneous estimates of nitrate diffusive fluxes, derived from microturbulence observations, and picoplankton abundance collected in more than 100 stations, spanning widely different hydrographic regimes, showed that the contribution of eukaryotes to picoautotrophic biomass increases with nutrient supply, whereas that of picocyanobacteria shows the opposite trend. These findings were supported by laboratory and modeling chemostat experiments that reproduced the competitive dynamics between picoeukaryote and picocyanobacteria as a function of changing nutrient supply. The results derived from this project confirm that turbulence and mixing control the availability of light and nutrients, which in turn determine the structure of marine phytoplankton communities.RADIALES-20 (IEO), CHAOS (CTM 2012-30680), Malaspina-2010(CSD2008-00077