ABSTRACT
Objective: Alterations in behavioral habits of children were analyzed in Shanghai in the winters prior to and following the COVID-19 pandemic, with the aim of assisting in the prevention of children's respiratory diseases in the post-pandemic era. Method: This cross-sectional survey was conducted via an offline questionnaire from January 1 to February 28, 2021. The behavioral habits of Shanghainese children in different age groups during the winter of 2019 and 2020 were statistically analyzed using the SPSS 25.0 software. The parameters surveyed and analyzed included the frequency of outdoor activities, self-protection measures (frequency of wearing masks and washing hands after going out), travel history, time to visit since symptom onset, and influenza vaccination status. Lastly, the number of cases of respiratory diseases in the pediatric outpatient department of our hospital was analyzed during the same period. Result: A total of 1816 questionnaires were investigated and analyzed, and the results revealed that self-protection measures were significantly enhanced in children of all age groups after the COVID-19 pandemic breakout compared with those before the pandemic, whereas the frequency of outdoor activities and traveling were reduced. In the age group over 3 years old, the time to visit since symptom onset was shorter, and the influenza vaccination rate increased following the pandemic. During the same period, the number of cases of children with respiratory diseases in our outpatient department was significantly decreased compared with that prior to the COVID-19 pandemic. Conclusion: The COVID-19 pandemic has had a significant impact on self-protection measures, outdoor activities, and other children's behaviors in all age groups in Shanghai. These changes in habits were correlated with a lower incidence and transmission of respiratory diseases in children. Overall, this study lays a theoretical basis for the prevention of childhood illnesses in the post-pandemic era.
ABSTRACT
In order to investigate the clinical features of pregnant women and their neonates with coronavirus disease 2019 (COVID-19) and the evidence of vertical transmission of COVID-19, we retrieved studies included in PubMed, Medline and Chinese databases from January 1, 2000 to October 25, 2020 using relevant terms, such as 'COVID-19', 'vertical transmission' et al. in 'Title/Abstract'. Case reports and case series were included according to the inclusion and exclusion criteria. We conducted literature screening and data extraction, and performed literature bias risk assessment. Total of 13 case series and 16 case reports were collected, including a total of 564 pregnant women with COVID-19 and their 555 neonates, of which 549 neonates received nucleic acid test for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and 18 neonates was diagnosed with COVID-19. The positive rate is 3.28%. Amniotic fluid of one woman was tested positive for SARS-CoV-2. The majority of infected neonates were born under strict infection control and received isolation and artificial feeding. Up till now, there is no sufficient evidence to exclude the possibility of vertical transmission for COVID-19 based on the current available data.
ABSTRACT
The prospect of assisting disabled patients by translating neural activity from the brain into control signals for prosthetic devices, has flourished in recent years. Current systems rely on neural activity present during natural arm movements. We propose here that neural activity present before or even without natural arm movements can provide an important, and potentially advantageous, source of control signals. To demonstrate how control signals can be derived from such plan activity we performed a computational study with neural activity previously recorded from the posterior parietal cortex of rhesus monkeys planning arm movements. We employed maximum likelihood decoders to estimate movement direction and to drive finite state machines governing when to move. Performance exceeded 90% with as few as 40 neurons.
