Rhinovirus infection and co-infection in children with severe acute respiratory infection during the COVID-19 pandemic period.

Rhinovirus causes respiratory tract infections in children and is found in co-infections. The objective of this research was to study the clinical profile of rhinovirus infection and co-infection in children with severe acute respiratory infection (SARI) during the COVID-19 pandemic period. We included 606 children ranging in age from 0.1 to 144 months of age from March 2020 to December 2021, hospitalized in the Pediatric Intensive Care Unit (PICU). The samples were collected by secretion from the nasopharynx region. A total of 259 children were tested positive for viral infection, 153 (59.07%) of them had a single rhinovirus infection and, 56 (36.6%) were aged between 60.1 and 144 months. Nine types of co-infections were identified and were found coinfection with three or more viruses (22/104, 21.15%). Observing the seasonality, the number of cases was similar between 2020 (49.53%) and 2021 (51.47%). Patients with a single infection (86.88%) and coinfection (67.30%) were more likely to have coughed. Patients with co-infection required the use of O2 for longer than those with a single rhinovirus infection. Hemogram results obtained from individuals with a single infection had higher levels of urea when compared to patients with co-infection with and other respiratory viruses. Multiple correspondence analyses indicated different clinical symptoms and comorbidities in patients with co-infection compared to those with single infection. The results found that the rhinovirus was much prevalent virus during the pandemic period and was found in co-infection with other virus types, what is important to diagnostic for the correct treatment of patients.

High-throughput phenotyping allows the selection of soybean genotypes for earliness and high grain yield.

BACKGROUND: Precision agriculture techniques are widely used to optimize fertilizer and soil applications. Furthermore, these techniques could also be combined with new statistical tools to assist in phenotyping in breeding programs. In this study, the research hypothesis was that soybean cultivars show phenotypic differences concerning wavelength and vegetation index measurements. RESULTS: In this research, we associate variables obtained via high-throughput phenotyping with the grain yield and cycle of soybean genotypes. The experiment was carried out during the 2018/2019 and 2019/2020 crop seasons, under a randomized block design with four replications. The evaluated soybean genotypes included 7067, 7110, 7739, 8372, Bonus, Desafio, Maracai, Foco, Pop, and Soyouro. The phenotypic traits evaluated were: first pod height (FPH), plant height (PH), number of branches (NB), stem diameter (SD), days to maturity (DM), and grain yield (YIE). The spectral variables evaluated were wavelengths and vegetation indices (NDVI, SAVI, GNDVI, NDRE, SCCCI, EVI, and MSAVI). The genotypes Maracai and Foco showed the highest grain yields throughout the crop seasons, in addition to belonging to the groups with the highest means for all VIs. YIE was positively correlated with the NDVI and certain wavelengths (735 and 790 nm), indicating that genotypes with higher values for these spectral variables are more productive. By path analyses, GNDVI and NDRE had the highest direct effects on the dependent variable DM, while NDVI had a higher direct effect on YIE. CONCLUSIONS: Our findings revealed that early and productive genotypes can be selected based on vegetation indices and wavelengths. Soybean genotypes with a high grain yield have higher means for NDVI and certain wavelengths (735 and 790 nm). Early genotypes have higher means for NDRE and GNDVI. These results reinforce the importance of high-throughput phenotyping as an essential tool in soybean breeding programs.