An observational study on the safety of COVID-19 vaccination in patients with myasthenia gravis.

OBJECTIVE: There is concern that the coronavirus disease (COVID-19) vaccine may trigger or worsen autoimmune diseases. The objective of this study was to determine the impacts of COVID-19 vaccination on symptom severity in patients with myasthenia gravis (MG). METHODS: A total of 106 enrolled patients with MG who were vaccinated against COVID-19 were followed up, and a questionnaire was used to document in detail the exacerbation of muscle weakness after vaccination and all other uncomfortable reactions after vaccination. Demographic, clinical characteristics, medication, and vaccination data were collected by follow-up interview. The main observation outcome was whether the MG symptoms of patients were exacerbated. The definition of exacerbation is according to the subjective feeling of the patient or a 2-point increase in daily life myasthenia gravis activity score relative to before vaccination, within 30 days after vaccination. RESULTS: Of 106 enrolled patients [median age (SD) 41.0 years, 38 (35.8%) men, 53 (50.0%) with generalized MG, 74 (69.8%) positive for acetylcholine receptor antibody, and 21 (19.8%) with accompanying thymoma], muscle weakness symptoms were stable in 102 (96.2%) patients before vaccine inoculation. Muscle weakness worsened in 10 (9.4%) people after vaccination, of which 8 patients reported slight symptom worsening that resolved quickly (within a few days). Two (1.9%) of patients showed serious symptom aggravation that required hospitalization. CONCLUSION: Our results suggest that inactivated virus vaccines against COVID-19 may be safe for patients with MG whose condition is stable. Patients with generalized MG may be more likely to develop increased muscle weakness after vaccination.

Data-Driven Modeling and Analysis for COVID-19 Pandemic Hospital Beds Planning

The COVID-19 pandemic presents unprecedented challenges for the US healthcare system, and the critical care settings are heavily impacted by the pressures of caring for COVID-19 patients. However, hospital pandemic preparedness has been hampered by a lack of disease specific planning guidelines. In this paper, we proposed a holistic modeling and analysis approach, with a system dynamics model to predict COVID-19 cases and a discrete-event simulation to evaluate hospital bed utilization, to support the hospital planning decisions. Our model was trained using the public data from the JHU Coronavirus Resource Center and was validated using historical patient census data from the University of Florida Health Jacksonville, Jacksonville, FL and public data from the Florida Department of Health (FDOH). Various experiments were conducted to investigate different control measures and the variants of the virus and their impact on the disease transmission, and subsequently, the hospital planning needs. Our proposed approach can be tailored to a given hospital setting of interest and is also generalizable to other hospitals to tackle the pandemic planning challenge. Note to Practitioners-We proposed a holistic modeling and analysis approach to support hospital preparedness and resource planning during the COVID-19 pandemic. To capture the highly dynamic pandemic environment, we developed a numerical method to estimate R-0, the effective basic reproductive rate, and used the most recent estimated data series of daily R-0 to project the change in R-0 in a short-term forecast window. The prediction of the daily confirmed cases in that forecast window were then obtained based on recursively solving the system dynamics model, and was validated to be very close to the real confirmed cases from the public record. This data-driven approach allows us to gain a systematic understanding of the common trends across different states and regions, and to evaluate the effect of the control measures like the stay-at-home order and the impact of the virus variants on the disease transmission behavior. Furthermore, the dynamic prediction allows us to evaluate the hospital resource needs during different stages of the pandemic. The insights obtained through this effort shed light on the impact of interventions (e.g., vaccines and control measures) on the hospital preparedness to support appropriate hospital resource allocation.

A Critical Survey on Renewable Energy Applications in the Philippines and China: Present Challenges and Perspectives

China's Belt and Road (B&R) initiative provides new ideas and opportunities for international cooperation. Renewable energy plays a crucial role not only in the national sustainable development framework of China and the Philippines but also in bilateral cooperation between them. However, some obstacles still need to be addressed because renewable energy cooperation between China and the Philippines has not been thoroughly and comprehensively studied to date. Based on an in-depth analysis of current renewable energy cooperation between China and the Philippines, this paper employs PESTEL analysis to fully investigate the cooperative advantages and disadvantages by considering politics (P), economy (E), society (S), technology (T), environment (E), and legislation (L) and proposes several constructive suggestions. The ultimate purpose was to design feasible schemes to ensure the sufficient utilization of renewable energy and the construction of integrated power grid systems to meet shortages of electricity supply especially in the isolated small islands in the Philippines through cooperation with China. In particular, it offers valuable advice concerning the U.S.-China trade war and COVID- 19 pandemic, outlining how cooperation in the exploitation of potential renewable energy is vital.

Psychological Status of Clinical Laboratory Staff During the COVID-19 Outbreak

Background: In early December 2019, during the outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which was first detected in Wuhan, COVID-19 was suspected, detected, and confirmed in an increasing number of patients every day. The clinical laboratory staff have always played an important role in the laboratory diagnosis of patients. Currently, there are many research studies on the mental health of the first-line doctors or nurses managing the COVID-19 outbreak, both domestically and overseas, but data of the mental health and associated factors among the clinical laboratory staff who handle the blood or biological samples of confirmed cases and are consequently exposed to COVID-19 are limited. Methods: This cross-sectional survey-based study was performed via an online survey in a single designated hospital from April 20 to April 23, 2020 in Yiwu,China. The online survey included questions on sociodemographic and clinical variables. Totally, 45 clinical laboratory staff and 20 nonmedical health workers participated. Mental health variables were assessed via 4 Chinese versions of validated measurement tools : Zungs Self-rating Depression Scale (SDS), Zung's Self-rating Anxiety Scale (SAS), the Pittsburgh Sleep Quality Index (PSQI), and the Eysenck Personality Questionnaire (EPQ). Results: Significant differences were observed in the SDS and SAS scores, between the clinical laboratory staff and the nonmedical health workers (P < .001, P < .003, respectively). The scores for exposure risk and neuroticism of participants were the main factors influencing both the SDS scores of the clinical laboratory staff (P = .002, P = .005, respectively), and also their SAS scores (P = .003 P = .006, respectively). Conclusions: The results showed that a significant proportion of clinical laboratory staff experienced anxiety and depression symptoms. Their scores for mental health problems, exposure risk, and neuroticism were associated with severe symptoms of depression and anxiety. Therefore, the high-risk group of the clinical laboratory staff and those individuals with higher neuroticism scores may need special attention.

Environmental monitoring and infection control of fever clinics in general hospitals during COVID-19 pandemic

The early stage of the COVID-19 epidemic happened to be the flu season. Since some symptoms of influenza and COVID-19 are similar, symptomatic patients flocked to fever clinics and emergency departments. Meanwhile, asymptomatic COVID-19 patients attending other departments in general hospitals made things worse. Lack of knowledge of the pathogen, absence of awareness and short of personal protective equipment all posed threat to healthcare workers as well as other patients. As SARS-CoV-2 can be spread via droplets, direct contacts and potentially aerosols, the indoor air environment of hospitals, especially fever clinics, must have strict measures to prevent hospital-acquired infection. Thirty-two sensors were deployed in the Tsinghua University Affiliated Beijing Tsinghua Changgung Hospital (mentioned as Changgung Hospital hereinafter) from January 30, 2020, in order to monitor high-resolution real-time indoor environmental parameters at its fever clinic, isolation wards and other departments. One sensor monitors and records CO2 concentration, PM2.5 mass concentration, relative humidity, temperature and illuminance every 5 minutes. Six sensors were located at the fever clinic, where all patients with fever and/or other COVID-19 related symptoms firstly attended after arriving at the hospital. The clinic has two parts, one for diagnosis and the other for quarantine. Three sensors were placed in doctor's office, nursing station and waiting area in the diagnosis part, respectively. Natural ventilation was chosen to dilute the environment, as the flowrate of outdoor airflow was abundant in Beijing's winter. Atmospheric CO2 concentration surrounding Changgung Hospital was stable, and the rise of indoor CO2 concentration was caused by human exhalation. During this pandemic, CO2 concentration can be regarded as an indicator of room ventilation condition and hospital congestion, if all the people in hospital were regarded as potential infector of SARS-CoV-2. According to the usage pattern of the fever clinic, the maximum number of patients in each functional area was set as 4 for doctor's office, 4 for nursing station and 11 for waiting area. According to the ventilation regulation of infectious disease hospital, the air change rate at fever clinics should be at least 6 h(-1). In addition, the outdoor CO2 concentration was assumed to be 400 ppm. Based on these conditions, the upper limits of indoor CO2 concentration were 902, 864 and 867 ppm for doctor's office, nursing station and waiting area at the Changgung Hospital's fever clinic, respectively. Indoor CO2 concentration exceeding these thresholds stands for poor ventilation or overcrowds. Fortunately, this didn't happen during the monitoring period and indoor CO2 concentration didn't exceed 609-711 ppm. In another word, natural ventilation was sufficient and effective in this specific case at the Changgung Hospital's fever clinic. Moreover, together with environment disinfection and personal protective measures, good ventilation condition led to no COVID-19 hospitalacquired infection. To conclude, this article introduced a real-time environmental monitoring campaign at the Changgung Hospital's fever clinic. Similar methodology can help assess ventilation conditions and risk of hospital-acquired infection at the fever clinic during and after COVID-19 pandemic. Once indoor CO2 concentration exceeds the set thresholds, areas with high infection risk can be identified rapidly and timely, so that prevention measures can be taken in time.