Impact of the COVID-19 pandemic on congenital diaphragmatic hernia patients: a single-center retrospective study.

PURPOSE: To investigate the impact of COVID-19 on the treatment of children with congenital diaphragmatic hernia (CDH). METHODS: We retrospectively collected and compared the data of patients with CDH admitted between January 1, 2020 and December 31, 2021(study group) with the CDH patients admitted before the pandemic between January 1, 2018 and December 31, 2019 (control group). RESULTS: During the pandemic, 41 patients with CDH diagnosed prenatally were transferred to our hospital, and 40 underwent surgical repair. The number of patients treated in our hospital increased by 24.2% compared with the 33 patients before the pandemic. During the pandemic, the overall survival rate, postoperative survival rate and recurrence rate were 85.4%, 87.5% and 7.3%, respectively, and there were no significant differences compared with the control group (75.8%, 83.3% and 9.1%, respectively). The average length of hospital stay in patients admitted during the pandemic was longer than that in the control group (31 days vs. 16 days, P < 0.001), and the incidence of nosocomial infection was higher than that in the control group (19.5% vs. 3%, P = 0.037). CONCLUSIONS: CDH patients confirmed to be SARS-CoV-2 infection-free can receive routine treatment. Our data indicate that the implementation of protective measures during the COVID-19 pandemic, along with appropriate screening and case evaluation, do not have a negative impact on the prognosis of children.

The Trends of Psychological Status of People Entering from High-Risk Areas of COVID-19 Coronavirus During the Quarantine in Dedicated Hotels: A Longitudinal Survey Study from Guangzhou, China.

BACKGROUND: The quarantine in dedicated hotels has become an inevitable safety measure due to the frequent cross-border travel since the outbreak of COVID-19. The aim of the present study was to explore the trends in the psychological status of individuals entering from high-risk areas of COVID-19 coronavirus while quarantining in dedicated hotels. METHODS: A total of 591 individuals who isolated in dedicated hotels were recruited between March and June 2020. Participants self-reported mental symptoms [Self-rating Anxiety Scale (SAS) and Self-rating Depression Scale (SDS)] every three days during the quarantine. A mixed-effects linear regression model was used to assess the trends. RESULTS: Participants reporting anxiety and depression symptoms at least one time during quarantine accounted for 4.5% and 18.4%, respectively. Their psychological status was alleviated during some first 9 days, and then it slightly deteriorated, which was suggested by SAS and SDS scores that were negatively correlated with the days of quarantine (T) (adjusted coefficient [ß] -0.81, 95% CI -1.00 to -0.62; and ß -0.75, 95% CI -0.97 to -0.53, respectively), and were positively correlated with the square of T (ß 0.04, 95% CI 0.02 to 0.06; and ß 0.04, 95% CI 0.02 to 0.06, respectively). The unemployed and 18~30-year-old participants were prone to greater levels of psychological distress. No significant difference in the trend of mental health was found among different subgroups. CONCLUSION: The mental health of the people entering Guangzhou from high-risk areas of COVID-19 coronavirus resulted positive during the early period of quarantine in dedicated hotels, after which it deteriorated. The psychological status of individuals should be closely monitored at the beginning and after more than 9 days of quarantine, especially for individuals who are unemployed and 18~30-year-old ones.

Liquid Chromatography-Multiple Reaction Monitoring-Mass Spectrometry Assay for Quantitative Measurement of Therapeutic Antibody Cocktail REGEN-COV Concentrations in COVID-19 Patient Serum.

REGEN-COV is a cocktail of two human IgG1 monoclonal antibodies (REGN10933 + REGN10987) that targets severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein and has shown great promise to reduce the SARS-CoV-2 viral load in COVID-19 patients enrolled in clinical studies. A liquid chromatography-multiple reaction monitoring-mass spectrometry (LC-MRM-MS)-based method, combined with trypsin and rAspN dual enzymatic digestion, was developed for the determination of total REGN10933 and total REGN10987 concentrations in several hundreds of pharmacokinetic (PK) serum samples from COVID-19 patients participating in phase I, II, and III clinical studies. The performance characteristics of this bioanalytical assay were evaluated with respect to linearity, accuracy, precision, selectivity, specificity, and analyte stability before and after enzymatic digestion. The developed LC-MRM-MS assay has a dynamic range from 10 to 2000 µg/mL antibody drug in the human serum matrix, which was able to cover the serum drug concentration from day 0 to day 28 after drug administration in two-dose groups for the clinical PK study of REGEN-COV. The concentrations of REGEN-COV in the two-dose groups measured by the LC-MRM-MS assay were comparable to the concentrations measured by a fully validated electrochemiluminescence (ECL) immunoassay.

Real-Time Task Allocation of Heterogeneous Unmanned Aerial Vehicles for Search and Prosecute Mission

In recent years, the Internet of Things (IoT) has developed rapidly after the era of computers and smart phones, which is expected to be applied to cities to improve the quality of life and realize the intelligence of smart cities. In particular, with the outbreak of coronavirus disease 2019 (COVID-19) last year, in order to reduce contact, some IoT devices, such as robots, unmanned aerial vehicles (UAVs), and unmanned vehicles, have played a great role in temperature monitoring, goods delivery, and so on. In this paper, we study the real-time task allocation problem of heterogeneous UAVs searching and delivering goods in the city. Considering the resource requirement of task and resource constraints of the UAV, when the resource of a single UAV cannot meet the requirement of the task, we propose a method of forming a UAV coalition based on contract net protocol. We analyze the coalition formation problem from two aspects: mission completion time and UAV’s energy consumption. Firstly, the mathematical model is established according to the optimization objective and condition constraints. Then, according to the established mathematical model, different coalition formation algorithms are proposed. To minimize the mission completion time, we propose a two-stage coalition formation algorithm. Aiming at minimizing the UAV’s energy consumption, it is transformed into a zero-one integer programming problem, which can be solved by the existing solver. Then, considering both mission completion time and energy consumption, we propose a coalition formation algorithm based on a resource tree. Finally, we design some simulation experiments and compare with the task allocation algorithm based on resource welfare. The simulation results show that our proposed algorithms are feasible and effective.

Recommendations for perinatal and neonatal surgical management during the COVID-19 pandemic.

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 has spread rapidly around the world and is a significant threat to global health. Patients in the Neonatal Surgery Department have rapidly progressing diseases and immature immunity, which makes them vulnerable to pulmonary infection and a relatively higher mortality. This means that these patients require multidisciplinary treatment including early diagnosis, timely transport, emergency surgery and intensive critical care. The COVID-19 pandemic poses a threat to carrying out these treatments. To provide support for the health protection requirements of the medical services in the Neonatal Surgery Department, we developed recommendations focusing on patient transport, surgery selection and protection requirements with the aim of improving treatment strategies for patients and preventing infection in medical staff during the current COVID-19 pandemic.