ABSTRACT
Coronavirus disease-19 (COVID-19) has been spreading all over the world for more than two years. Though several kinds of vaccines are currently available, emergence of new variants, spike mutations and immune escape have raised new challenges. Pregnant women are vulnerable to respiratory infections due to their altered immune defence and surveillance functions. Besides, whether pregnant persons should receive a COVID-19 vaccine is still under debate because limited data are available on the efficacy and safety of receiving a vaccine during pregnancy. Physiological features and lack of effective protection making pregnant women at high risk of getting infected. Another concern is that pregnancy may trigger the onset of underlying existing neurological disease, which is highly similar to those neurological symptoms of pregnant women caused by COVID-19. These similarities interfere with diagnosis and delay timely and effective management. Therefore, providing efficient emergency support for pregnant women suffering from neurological symptoms caused by COVID-19 remains a challenge among neurologists and obstetricians. To improve the diagnosis and treatment efficiency of pregnant women with neurological symptoms, we propose an emergency management framework based on the clinicians' experience and available resources. This emergency care system aimed at addressing the conundrums faced by the emergency guarantee system under COVID-19 pandemic and could serve as a potential multisystem project for clinical practice and medical education.
ABSTRACT
Purpose: To evaluate the knowledge, anxiety, depression, and sleep quality toward COVID-19 among Chinese medical staff from tertiary and basic-level hospitals in central south areas of China. Method: A structured questionnaire was composed of Demographic and clinical characteristics of medical staff, Knowledge toward COVID-19 including epidemiology and clinical manifestations, The Self-rating anxiety scale (SAS), Center for Epidemiologic Studies Depression Scale (CES-D), and The Pittsburgh Sleep Quality Index (PSQI). It was administered to medical staff from tertiary hospitals (Group A) (n = 407) and basic-level hospitals (Group B) (n = 388) during February 2020 and May 2020. Results: Medical staff in group A had a stronger knowledge toward COVID-19 than group B (23.69 ± 5.83 & 18.15 ± 6.35, p < 0.001). Mild anxiety symptoms were found in both groups. The SAS scores (Mean ± SD) of group B were 58.87 ± 10.17, which was significantly higher than that of group A (52.59 ± 12.09, p < 0.001). There were no significant differences in CES-D scores between the two groups (p = 0.981). The mean score of total PSQI in group B (8.41 ± 3.03) was statistically higher than that of group A (7.31 ± 3.74, p < 0.001). Additionally, the scores of sub-components of group B, including subjective sleep quality, sleep latency, sleep disorder, sleeping medication use and daytime dysfunction, were significantly higher compared to Group A (p < 0.05). Conclusions: Our study showed greater anxiety, more severe depression and poorer sleep quality among medical staff in central south areas of China during the COVID-19 outbreak. Additionally, compared to the tertiary hospital group, medical staff from basic-level hospitals had poorer knowledge toward COVID-19 and worse mental health conditions. In addition, residence, specialty, title and education level may also be factors of knowledge of COVID-19 and psychiatry problems. In light of this information, more attention should be paid to early identification and intervention of symptoms of anxiety and depression in susceptible medical staff from the basic-level hospitals.
ABSTRACT
Purpose: To assess knowledge, attitudes, and social responsiveness toward COVID-19 among Chinese medical students. Methods: Self-administered questionnaires were used to collect data from 889 medical students in three well-known Chinese medical universities. The questionnaire was comprised of three domains which consisted of demographic characteristic collection, seven items for knowledge, and eight items for attitudes and social responsiveness toward COVID-19. Data from different universities were lumped together and were divided into different groups to compare the differences, including (1) students at the clinical learning stage (Group A) or those at the basic-medicine stage (Group B) and (2) students who have graduated and worked (Group C) or those newly enrolled (Group D). Results: Medical students at group B had a weaker knowledge toward COVID-19 than did students at group A, especially in the question of clinical manifestations (p < 0.001). The percentage of totally correct answers of COVID-19 knowledge in group C was higher than that in Group D (p < 0.001). There were significant differences between groups C and D in the attitudes and social responsiveness toward COVID-19. Surprisingly, we found that the idea of newly enrolled medical students could be easily affected by interventions. Conclusions: In light of this information, medical education should pay attention not only to the cultivation of professional knowledge and clinical skills but also to the positive interventions to better the comprehensive qualities including communicative abilities and empathy.
ABSTRACT
Idiopathic pulmonary fibrosis (IPF) is a chronic progressive lung disease with a poor prognosis. The current coronavirus disease 2019 (COVID-19) shares some similarities with IPF. SARS-CoV-2 related genes have been reported to be broadly regulated by N6-methyladenosine (m6A) RNA modification. Here, we identified the association between m6A methylation regulators, COVID-19 infection pathways, and immune responses in IPF. The characteristic gene expression networks and immune infiltration patterns of m6A-SARS-CoV-2 related genes in different tissues of IPF were revealed. We subsequently evaluated the influence of these related gene expression patterns and immune infiltration patterns on the prognosis/lung function of IPF patients. The IPF cohort was obtained from the Gene Expression Omnibus dataset. Pearson correlation analysis was performed to identify the correlations among genes or cells. The CIBERSORT algorithm was used to assess the infiltration of 22 types of immune cells. The least absolute shrinkage and selection operator (LASSO) and proportional hazards model (Cox model) were used to develop the prognosis prediction model. Our research is pivotal for further understanding of the cellular and genetic links between IPF and SARS-CoV-2 infection in the context of the COVID-19 pandemic, which may contribute to providing new ideas for prognosis assessment and treatment of both diseases.