Background: Rapid and accurate detection of SARS-CoV-2 infected individuals is crucial for taking timely measures and minimizing the risk of further SARS-CoV-2 spread. We aimed to assess the accuracy of exhaled breath analysis by electronic nose (eNose) for the discrimination between individuals with and without a SARS-CoV-2 infection. Methods: This was a prospective real-world study of individuals presenting to public test facility for SARS-CoV-2 detection by molecular amplification tests (TMA or RT-PCR). After sampling of a combined throat/nasopharyngeal swab, breath profiles were obtained using a cloud-connected eNose. Data-analysis involved advanced signal processing and statistics based on independent t-tests followed by linear discriminant and ROC analysis. Data from the training set were tested in a validation, a replication and an asymptomatic set. Findings: For the analysis 4510 individuals were available. In the training set (35 individuals with; 869 without SARS-CoV-2), the eNose sensors were combined into a composite biomarker with a ROC-AUC of 0.947 (CI:0.928-0.967). These results were confirmed in the validation set (0.957; CI:0.942-0.971, n=904) and externally validated in the replication set (0.937; CI:0.926-0.947, n=1948) and the asymptomatic set (0.909; CI:0.879-0.938, n=754). Selecting a cut-off value of 0.30 in the training set resulted in a sensitivity/specificity of 100/78, >99/84, 98/82% in the validation, replication and asymptomatic set, respectively. Interpretation: eNose represents a quick and non-invasive method to reliably rule out SARS-CoV-2 infection in public health test facilities and can be used as a screening test to define who needs an additional confirmation test. Funding: Ministry of Health, Welfare and Sport

Our study demonstrated good repeatability and reproducibility for ultrasonic measurements of left cardiac structures in children, showing that values obtained for measurement of these structures in both clinical and epidemiological research projects can be confidently accepted.

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BackgroundGlucocorticoids have an important role in early growth and development. Glucocorticoid receptor gene polymorphisms have been identified that contribute to the variability in glucocorticoid sensitivity. We examined whether these glucocorticoid receptor gene polymorphisms are associated with growth in fetal and early postnatal life.MethodsThis study was embedded in a population-based prospective cohort study from fetal life onwards. The studied glucocorticoid receptor gene polymorphisms included BclI (rs41423247), TthIIII (rs10052957), GR-9β (rs6198), N363S (rs6195) and R23K (rs6789 and6190). Fetal growth was assessed by ultrasounds in second and third trimester of pregnancy. Anthropometric measurements in early childhood were performed at birth and at the ages of 6, 14 and 24 months postnatally. Analyses focused on weight, length and head circumference. Analyses were based on 2,414 healthy, Caucasian children.ResultsGlucocorticoid receptor gene polymorphisms were not associated with fetal weight, birth weight and early postnatal weight. Also, no associations were found with length and head circumference. Neither were these polymorphisms associated with the risks of low birth weight or growth acceleration from birth to 24 months of age.ConclusionsWe found in a large population-based cohort no evidence for an effect of known glucocorticoid receptor gene polymorphisms on fetal and early postnatal growth characteristics. Further systematic searches for common genetic variants by means of genome-wide association studies will enable us to obtain a more complete understanding of what genes and polymorphisms are involved in growth in fetal life and infancy.

Systemic sclerosis (SSc) is an autoimmune connective tissue disease, characterized by immune dysregulation and progressive fibrosis. Interstitial lung disease (ILD) is the most common cause of death among SSc patients and there are currently very limited approved disease-modifying treatment options for systemic sclerosis-related interstitial lung disease (SSc-ILD). The mechanisms underlying pulmonary fibrosis in SSc-ILD are not completely unraveled, and knowledge on fibrotic processes has been acquired mostly from studies in idiopathic pulmonary fibrosis (IPF). The incomplete knowledge of SSc-ILD pathogenesis partly explains the limited options for diseasemodifying therapy for SSc-ILD. Fibrosis in IPF appears to be related to aberrant repair following injury, but whether this also holds for SSc-ILD is less evident. Furthermore, immune dysregulation appears to contribute to pro-fibrotic responses in SSc-ILD, perhaps more than in IPF. In addition, SSc-ILD patient heterogeneity complicates the understanding of the underlying mechanisms of disease development, and more importantly, limits correct clinical diagnosis and treatment effectivity. Therefore, there is an unmet need for patient-relevant (in vitro) models to examine patient-specific disease pathogenesis, predict disease progression, screen appropriate treatment regimens and identify new targets for treatment. Technological advances in in vitro patient-relevant disease modeling, including (human induced pluripotent stem cell (hiPSC)-derived) lung epithelial cells, organoids and organ-on-chip technology offer a platform that has the potential to contribute to unravel the underlying mechanisms of SSc-ILD development. Combining these models with state-of-the-art analysis platforms, including (single cell) RNA sequencing and (imaging) mass cytometry, may help to delineate pathogenic mechanisms and define new treatment targets of SSc-ILD.