Introduction: Hyperglycemia in very preterm infants is associated with increased morbidity and mortality. We aimed to investigate potential associations between early hyperglycemia, neonatal cerebral magnetic resonance imaging (MRI), and neurodevelopment at 2.5 years. Methods: The study population included 69 infants with gestational age (GA) 22.3–31.9 weeks (n = 29 with GA <28 weeks), born 2011–2014. Plasma glucose concentrations during the first week were checked according to clinical routines. Hyperglycemia was defined as glucose concentrations above 8.3 mmol/L (150 mg/dL) and above 10 mmol/L (180 mg/dL), respectively, categorized as the highest glucose days 0–2, number of days above 8.3 and 10 mmol/L, and prolonged (yes/no) 2 days or more above 8.3 and 10 mmol/L. The MRI analysis included morphological assessment, regional brain volumes, and assessment of apparent diffusion coefficient (ADC). Neurodevelopmental impairment (NDI) developed in 13 of 67 infants with available outcomes, of which 57 were assessed with the Bayley-III. Univariate and multiple linear and logistic regressions were performed with adjustments for GA, birth weight z-scores, and illness severity expressed as days on mechanical ventilation. Results: Hyperglycemia above 8.3 mmol/L and 10 mmol/L was present in 47.8% and 31.9% of the infants. Hyperglycemia correlated independently with lower white matter volume, but not with other regional brain volumes, and was also associated with lower ADC values in white matter. Hyperglycemia also correlated with lower Bayley-III cognitive and motor scores in infants with GA <28 weeks, but there was no significant effect on NDI. Conclusion: Early hyperglycemia is associated with white matter injury and poorer neurodevelopment in very preterm infants.

Magnetic resonance imaging (MRI) plays an important role in assessing early brain development and injury in neonates. When using an automated volumetric analysis, brain tissue segmentation is necessary, preceded by brain extraction (BE) to remove non-brain tissue. BE remains challenging in neonatal brain MRI, and despite the existence of several methods, manual segmentation is still considered the gold standard. Therefore, the purpose of this study was to assess different BE methods in the MRI of preterm neonates and their effects on the estimation of intracranial volumes (ICVs). This study included twenty-two premature neonates (mean gestational age ± standard deviation: 28.4 ± 2.1 weeks) with MRI brain scans acquired at term, without detectable lesions or congenital conditions. Manual segmentation was performed for T2-weighted scans to establish reference brain masks. Four automated BE methods were used: Brain Extraction Tool (BET2); Simple Watershed Scalping (SWS); HD Brain Extraction Tool (HD-BET); and SynthStrip. Regarding segmentation metrics, HD-BET outperformed the other methods with median improvements of +0.031 (BET2), +0.002 (SWS), and +0.011 (SynthStrip) points for the dice coefficient; and −0.786 (BET2), −0.055 (SWS), and −0.124 (SynthStrip) mm for the mean surface distance. Regarding ICVs, SWS and HD-BET provided acceptable levels of agreement with manual segmentation, with mean differences of −1.42% and 2.59%, respectively.

The education and training of a nuclear medicine technologist (NMT) is not homogeneous among European countries, which leads to different scope of practices and, therefore, different technical skills are assigned. The goal of this research was to characterize the education and training of NMT in Europe. This study was based on a literature research to characterize the education and training of NMT and support the historical evolution of this profession. It was divided into two different phases: the first phase included analysis of scientific articles and the second phase included research of curricula that allow health professionals to work as NMT in Europe. The majority of the countries [N=31 (89%)] offer the NMT curriculum integrated into the high education system and only in four (11%) countries the education is provided by professional schools. The duration in each education system is not equal, varying in professional schools (2-3 years) and high education level system (2-4 years), which means that different European Credit Transfer and Accumulation System, such as 240, 230, 222, 210 or 180 European Credit Transfer and Accumulation System, are attributed to the graduates. The professional title and scope of the practice of NMT are different in different countries in Europe. In most countries of Europe, nuclear medicine training is not specific and curriculum does not demonstrate the Nuclear Medicine competencies performed in clinical practice. The heterogeneity in education and training for NMT is an issue prevalent among European countries. For NMT professional development, there is a huge need to formalize and unify educational and training programmes in Europe.

Manual counting of platelets, in microscopy images, is greatly time-consuming. Our goal was to automatically segment and count platelets images using a deep learning approach, applying U-Net and Fully Convolutional Network (FCN) modelling. Data preprocessing was done by creating binary masks and utilizing supervised learning with ground-truth labels. Data augmentation was implemented, for improved model robustness and detection. The number of detected regions was then retrieved as a count. The study investigated the U-Net models performance with different datasets, indicating notable improvements in segmentation metrics as the dataset size increased, while FCN performance was only evaluated with the smaller dataset and abandoned due to poor results. U-Net surpassed FCN in both detection and counting measures in the smaller dataset Dice 0.90, accuracy of 0.96 (U-Net) vs Dice 0.60 and 0.81 (FCN). When tested in a bigger dataset U-Net produced even better values (Dice 0.99, accuracy of 0.98). The U-Net model proves to be particularly effective as the dataset size increases, showcasing its versatility and accuracy in handling varying cell sizes and appearances. These data show potential areas for further improvement and the promising application of deep learning in automating cell segmentation for diverse life science research applications.

Proper selection and application of preprocessing steps are crucial for obtaining accurate segmentation in brain Magnetic Resonance Imaging (MRI). The aim of this study is to evaluate the impact brain extraction (BE) and bias field correction (BFC) methods have on regional brain volume (RBV) measurements of preterm neonates’ T2w MRI at term-equivalent age (TEA). Five BE methods (Manual, BET2, SWS, HD-BET, SynthStrip) were applied together with two BFC methods (SPM-BFC and N4ITK), before segmenting the neonatal brain into eight tissue classes (cortical grey matter, white matter, cerebral spinal fluid, deep nuclear grey matter, hippocampus, amygdala, cerebellum, and brainstem) using an automated segmentation software (MANTiS). Quantitative assessments were conducted, including the coefficient of variation (CV), coefficient of joint variation (CJV), Dice coefficient (DC), and RBV. HD-BET, together with N4ITK, showed the highest performance (mean ± standard deviation) regarding CV of 0.047 ± 0.005 (white matter) and 0.070 ± 0.005 (grey matter), CJV of 0.662 ± 0.095, DC of 0.942 ± 0.063, and RBV without significant differences (except in the brainstem) from the manual segmentation. Therefore, such combination of methods is recommended for improved skull-stripping accuracy, intensity homogeneity, and reproducibility of RBV of T2w MRI at TEA.