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Background: Several screening strategies for identifying congenital CMV (cCMV) have been proposed; however, the optimal solution has yet to be determined. We aimed to determine the prevalence of cCMV by universal screening with saliva pool testing and to identify the clinical variables associated with a higher risk of cCMV to optimize an expanded screening strategy. Methods: We carried out a prospective universal cCMV screening (September/2022 to August/2023) of 2186 newborns, analyzing saliva samples in pools of five (Alethia-LAMP-CMV®) and then performed confirmatory urine CMV RT-PCR. Infants with risk factors (small for gestational age, failed hearing screening, HIV-exposed, born to immunosuppressed mothers, or <1000 g birth weight) underwent expanded screening. Multivariate analyses were used to assess the association with maternal/neonatal variables. Results: We identified 10 infants with cCMV (prevalence: 0.46%, 95% CI 0.22–0.84), with significantly higher rates (2.1%, 95% CI 0.58–5.3) in the high-risk group (p = 0.04). False positives occurred in 0.09% of cases. No significant differences in maternal/neonatal characteristics were observed, except for a higher prevalence among infants born to non-Chilean mothers (p = 0.034), notably those born to Haitian mothers (1.5%, 95% CI 0.31–4.34), who had higher odds of cCMV (OR 6.82, 95% CI 1.23–37.9, p = 0.04). Incorporating maternal nationality improved predictive accuracy (AUC: 0.65 to 0.83). Conclusions: For low-prevalence diseases such as cCMV, universal screening with pool testing in saliva represents an optimal and cost-effective approach to enhance diagnosis in asymptomatic patients. An expanded screening strategy considering maternal nationality could be beneficial in resource-limited settings. [ABSTRACT FROM AUTHOR]

For polyolefins, a transition from ductile- to brittle fracture behavior can be observed at decreasing temperatures, which is typically characterized by the Ductile–Brittle Transition Temperature (DBTT). For each material, the DBTT needs to be determined by performing multiple impact tests across wide temperature ranges. In this study, we utilize Convolutional Neural Networks (CNN) to predict the DBTT from single Instrumented Notched Charpy (INC) experiments instead. Our dataset consists of over 6732 INCs encompassing 132 Polyethylene (PE) and Polypropylene (PP) compounds. For each of these compounds, we examine 51 impact tests at temperatures ranging from -40 °C to 60 °C. We train models on combinations of instrumentation signals and fracture surface images. We test the trained networks on 1683 INCs, where we achieve a R2-score of 0.96 and an average error below 5 °C of predictions compared to experimentally determined DBTTs. The presented methodology provides a flexible way of predicting DBTTs in case of non-instrumented testing or if thermal specimen conditioning is not possible. This allows for fast screening of new materials. [Display omitted] • 6732 instrumented notched Charpy experiments are utilized for machine learning. • 132 polyethylene- and polypropylene compounds are investigated. • Structure–property relations are extracted by networks from fracture surface images. • The Ductile–Brittle transition of polyolefins is predicted; average error below 10 °C. • The presented methodology allows for fast screening of materials. [ABSTRACT FROM AUTHOR]