OBJECTIVES:
The American Academy of Pediatrics National Registry for the Surveillance and Epidemiology of Perinatal coronavirus disease 2019 (COVID-19) (NPC-19) was developed to provide information on the effects of perinatal severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection.
METHODS:
National Registry for the Surveillance and Epidemiology of Perinatal COVID-19 participating centers entered maternal and newborn data for pregnant persons who tested positive for SARS-CoV-2 infection between 14 days before and 10 days after delivery. Incidence of and morbidities associated with maternal and newborn SARS-CoV-2 infection were assessed.
RESULTS:
From April 6, 2020 to March 19, 2021, 242 centers in the United States centers reported data for 7524 pregnant persons; at the time of delivery, 78.1% of these persons were asymptomatic, 18.2% were symptomatic but not hospitalized specifically for COVID-19, 3.4% were hospitalized for COVID-19 treatment, and 18 (0.2%) died in the hospital of COVID-related complications. Among 7648 newborns, 6486 (84.8%) were tested for SARS-CoV-2, and 144 (2.2%) were positive; the highest rate of newborn infection was observed when mothers first tested positive in the immediate postpartum period (17 of 125, 13.6%). No newborn deaths were attributable to SARS-CoV-2 infection. Overall, 15.6% of newborns were preterm: among tested newborns, 30.1% of polymerase chain reaction-positive and 16.2% of polymerase chain reaction-negative were born preterm (P < .001). Need for mechanical ventilation did not differ by newborn SARS-CoV-2 test result, but those with positive tests were more likely to be admitted to a NICU.
CONCLUSIONS:
Early in the pandemic, SARS-CoV-2 infection was acquired by newborns at variable rates and without apparent short-term effects. During a period that preceded widespread availability of vaccines, we observed higher than expected numbers of preterm births and maternal in-hospital deaths.
These findings suggest that the utilization of different assays to detect ASA may detect sera that are positive for ASA with more reliability than single assay testing.
We analyze a performance profile of several accelerated and hybrid accelerated methods. All comparative methods are at least linearly convergent and have satisfied numerical characteristics regarding tested metrics: number of iterations, CPU time and number of function evaluations. Among the chosen set of methods we numerically show which one is the most efficient and the most effective. Therewith, we derived a conclusion about what type of method is more preferable to use considering analyzed metrics.
Biogas obtained by anaerobic digestion process from various organic fractions of waste is increasingly used as a renewable energy sources for the generation of electricity and heat. The quantity of biogas produced by anaerobic digestion depends on many factors: types and characteristics of organic waste, elemental composition of waste, C/N ratio, pH value, inhibitors, retention time, content of nutrients, etc. In addition to the selection of parameters that influence the process of anaerobic digestion, biogas yield can also be influenced by choosing the optimal combination and ratio of organic fractions of waste. In this paper, an analysis of the influential parameters in the process of anaerobic digestion was performed on biogas yields and an overview of the essential characteristics of waste (elementary composition, C/N ratio, lignin content, etc.) for different fractions of organic waste (organic municipal waste, various types of waste of animal origin, as well as agricultural waste). In order to choose the optimal mixing ratio of different fractions of organic waste for maximum biogas yield, a mathematical model has been developed using the multi-criteria optimization method. The boundary conditions set for the multi-criteria optimization was the C/N ratio in the range of 20 to 30 and the minimum content of the lignin in the substrate. The application of the developed model was carried out on the case study of the city of Nis, and the optimal mix of different types of organic waste was determined, as well as the optimal amount of each waste fraction and biogas yield.
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