Job burnout and its influencing factors in Chinese medical staffs under China's prevention and control strategy for the COVID-19 pandemic.

OBJECTIVE: This study aimed to investigate the influencing factors of burnout among grassroots medical staff in China so as to provide a reference for improving their physical, psychological, and social statuses under China's prevention and control strategy for the COVID-19 pandemic and ensuring the sustainable supply of high-quality medical resources. METHODS: This study was performed on medical staff in five primary hospitals in Jiangsu Province, China, from May 1, 2022, to June 1, 2022, using a general information questionnaire and Maslach Burnout Inventory Scale. SPSS 25.0 and Stata 15.0 were used for two-track data entry and analysis. The OLS regression model was established to analyze the influencing factors for the job burnout of health care personnel. RESULTS: Two hundred seventy valid questionnaires were analyzed. The total score of job burnout was (30.16 ± 10.99). The scores of emotional exhaustion, depersonalization, and self-achievement were (9.88 ± 3.839), (11.99 ± 5.68), and (8.29 ± 5.18), respectively. Feeling depressed and stressed after the pandemic, days working over the past week, and work hours per shift had a positive impact on the Maslach Burnout total score. Increased income and hours working every week had a negative impact on the Maslach Burnout total score. However, sex, age in years, degree, professional title, job category, workplace, marital status, years in practice, health status, active management of health, idea of resignation, and promotion after the pandemic did not affect the Maslach Burnout total score. CONCLUSION: The job burnout of medical staff is affected by health conditions, working conditions, the psychological consequences of a pandemic, wages and marital status. Hospital managers should formulate incentive measures according to different psychological changes in medical staff to create a good medical working environment under the normalization of COVID-19 pandemic prevention and control.

Thyroid diseases are associated with coronavirus disease 2019 infection.

Background: In 2019, there was a global outbreak of new coronary pneumonia. Studies have found that the severity of patients with new coronary pneumonia may be related to their comorbidities. This article discusses the impact of thyroid disease on the severity of new coronary pneumonia through a meta-analysis and provides new treatment ideas for the later treatment and recovery of new coronary pneumonia. Methods: Databases including PubMed, Embase, Cochrane Library, SINOMED, China national knowledge infrastructure (CNKI), and Wanfang for coronavirus disease 2019 (COVID-19) infection and thyroid diseases were searched. Reference lists of all eligible articles and related previous review articles were handsearched. Fifty-three articles were included to conduct the meta-analysis. Results: Fifty-three articles with 12,022 COVID-19 infection patients were included in this meta-analysis. The proportion of patients with thyroid diseases in all COVID-19 infection patients fluctuates between 0% and 88.46%. Of the 53 included studies, 22 studies reported the severity of COVID-19 infection and grouped. The fixed-effects model was used to merge odds ratio (OR) values, and the pooled effect size in favor of non-severe patients is 2.62 (95% CI = 1.96-3.49, P < 0.0001), which means that patients with severe COVID-19 infection are more likely to have thyroid diseases. The analysis subgrouped into Asia and Europe shows that patients with COVID-19 severe infection in Asia are 3.77 times more likely to have thyroid diseases than non-severe patients (fixed-effects model: OR = 3.77, 95% CI = 2.66-5.35, P < 0.00001). No significant statistical heterogeneity was found by the heterogeneity analysis (chi-square = 19.85, P = 0.34, I 2 = 9%). Severe COVID-19 infection patients are more likely to be complicated by hypothyroidism and low T3 syndrome. The pooled ORs with fixed-effects model are 3.72 (95% CI = 1.62-8.58, P = 0.002) and 5.86 (95% CI = 2.79-12.33, P < 0.00001), respectively. Conclusion: COVID-19 infection patients with thyroid diseases are very common, and severe patients are more likely to have thyroid diseases. Asian COVID-19 infection, hypothyroidism patients, and patients with low T3 syndrome are more likely to progress to severe condition. Systematic Review Registration: https://inplasy.com, identifier INPLASY202190079.

EEG-Based Driver Mental Fatigue Recognition in COVID-19 Scenario Using a Semi-Supervised Multi-View Embedding Learning Model

With the spread of COVID-19 in recent years, wearing masks has increased the difficulty of driver mental fatigue recognition. Electroencephalogram (EEG) signal has become an important physiological signal index to reflect the driver's mental state. However, the drivers' EEG data is plagued by inadequate labels and multi-view data, which makes classification difficult. To solve this problem, this study proposes a semi-supervised multi-view sparse regularization and graph embedding learning (SMSG) model. To obtain discriminative feature representations of semi-supervised EEG data, SMSG fully mines diverse information from multiple views based on sparse regularization embedding and graph embedding technology. SMSG employs the graph embedding to capture the discriminative structure and local manifold structure on multi-view data. Furthermore, SMSG learns the common shared regularization embedding and private regularization embedding factors to preserve the consistency and diversity of the multi-view data. Through self-adaptive learning, the weights of each view can be directly solved adaptively. This works also introduces kernel trick to project the SMSG model into the nonlinear reproducing kernel Hilbert space (RKHS), which can obtain more approximate EEG feature representation. Experiments on the real dataset verify the effectiveness of the SMSG model for EEG-based driver mental fatigue recognition.

Investigating the relationships between emotional experiences and behavioral responses amid the Covid‐19 pandemic: A cross‐sectional survey

The Covid‐19 pandemic produced a complex combination of intense negative emotions among the general public, influencing people's coping reactions toward the pandemic. Yet each discrete emotion may affect people's behaviors in different ways. Unveiling the specific emotion–behavior relationships can provide valuable implications for designing effective intervention programs. Through the lens of the appraisal theory of emotion, we assessed the relationships between negative emotions and pandemic‐related behaviors among the Chinese population midst the early outbreak of the pandemic. An anonymous online survey was distributed to mainland Chinese participants (n = 2976), which assessed individuals' emotional states and behavioral reactions to the pandemic. Consistent with the differential appraisal theme underlying each negative emotion as delineated by the appraisal theory, mixed relationships between emotions and pandemic‐related behaviors were revealed. Specifically, anxiety was positively associated with behaviors of seeking pandemic‐related information, sharing such information, and stockpiling preventive goods, yet, contrary to prediction, anxious people were reluctant to adopt preventive measures, which is maladaptive. Sad people sought information less frequently and exhibited lower intention to stockpile preventive goods;but, opposing prediction, they shared information less frequently. Angry people were more active in sharing information and in stockpiling preventive goods. These findings suggest that public health practitioners can utilize the emotion–behavior relationships to identify the vulnerable individuals who tend to adopt maladaptive coping behaviors, help them address emotional distress, and encourage their adoption of effective coping behaviors. This research unveils the distinct relationships between Chinese public's discrete negative emotions (anxiety, anger, and sadness) elicited by the Covid‐19 pandemic and their behavioral reactions (seeking information, sharing information, preventive health behavior, and stockpiling) toward the pandemic. Unveiling these specific emotion–behavior relationships can help to identify the vulnerable individuals who tend to adopt maladaptive coping behaviors and encourage their adoption of effective coping behaviors, and help to design effective intervention programs.

Do patients with and survivors of COVID-19 benefit from telerehabilitation? A meta-analysis of randomized controlled trials

Background Coronavirus disease 2019 (COVID-19) significantly impacts physical, psychological, and social functioning and reduces quality of life, which may persist for at least 6 months. Given the fact that COVID-19 is a highly infectious disease and therefore healthcare facilities may be sources of contagion, new methods avoiding face-to-face contact between healthcare workers and patients are urgently needed. Telerehabilitation is the provision of rehabilitation services to patients at a distance via information and communication technologies. However, high-quality evidence of the efficacy of telerehabilitation for COVID-19 is still lacking. This meta-analysis aimed to investigate the efficacy of telerehabilitation for patients with and survivors of COVID-19. Methods We searched the Cochrane Library, EMBASE, Medline (via PubMed), PEDro, ClinicalTrials.gov, and WHO International Clinical Trials Registry Platform from January 1st, 2020 to April 30th, 2022 for randomized controlled trials published in English, which aimed to evaluate the efficacy of telerehabilitation vs. face-to-face rehabilitation, usual care, or no treatment for COVID-19. Methodological quality and overall evidence quality of the included studies were assessed. The statistical reliability of the data was quantified using the trial sequential analysis. Results Seven randomized controlled trials with eight comparisons were included and all of them were used for meta-analysis. The meta-analyses of absolute values showed the superiority of telerehabilitation over no treatment or usual care for dyspnea (Borg scale: mean difference = −1.88, −2.37 to −1.39;Multidimensional dyspnea-12: mean difference = −3.70, −5.93 to −1.48), limb muscle strength (mean difference = 3.29;2.12 to 4.47), ambulation capacity (standardized mean difference = 0.88;0.62 to 1.14), and depression (mean difference = −5.68;−8.62 to −2.74). Significant improvement in these variables persisted in the meta-analyses of change scores. No significant difference was found in anxiety and quality of life. No severe adverse events were reported in any of the included studies. Conclusions Moderate- to very low-quality evidence demonstrates that telerehabilitation may be an effective and safe solution for patients with and survivors of COVID-19 in dyspnea, lower limb muscle strength, ambulation capacity, and depression. Further well-designed studies are required to evaluate the long-term effects, cost-effectiveness, and satisfaction in larger samples.

Thyroid diseases are associated with coronavirus disease 2019 infection

Background In 2019, there was a global outbreak of new coronary pneumonia. Studies have found that the severity of patients with new coronary pneumonia may be related to their comorbidities. This article discusses the impact of thyroid disease on the severity of new coronary pneumonia through a meta-analysis and provides new treatment ideas for the later treatment and recovery of new coronary pneumonia. Methods Databases including PubMed, Embase, Cochrane Library, SINOMED, China national knowledge infrastructure (CNKI), and Wanfang for coronavirus disease 2019 (COVID-19) infection and thyroid diseases were searched. Reference lists of all eligible articles and related previous review articles were handsearched. Fifty-three articles were included to conduct the meta-analysis. Results Fifty-three articles with 12,022 COVID-19 infection patients were included in this meta-analysis. The proportion of patients with thyroid diseases in all COVID-19 infection patients fluctuates between 0% and 88.46%. Of the 53 included studies, 22 studies reported the severity of COVID-19 infection and grouped. The fixed-effects model was used to merge odds ratio (OR) values, and the pooled effect size in favor of non-severe patients is 2.62 (95% CI = 1.96–3.49, P < 0.0001), which means that patients with severe COVID-19 infection are more likely to have thyroid diseases. The analysis subgrouped into Asia and Europe shows that patients with COVID-19 severe infection in Asia are 3.77 times more likely to have thyroid diseases than non-severe patients (fixed-effects model: OR = 3.77, 95% CI = 2.66–5.35, P < 0.00001). No significant statistical heterogeneity was found by the heterogeneity analysis (chi-square = 19.85, P = 0.34, I2 = 9%). Severe COVID-19 infection patients are more likely to be complicated by hypothyroidism and low T3 syndrome. The pooled ORs with fixed-effects model are 3.72 (95% CI = 1.62–8.58, P = 0.002) and 5.86 (95% CI = 2.79–12.33, P < 0.00001), respectively. Conclusion COVID-19 infection patients with thyroid diseases are very common, and severe patients are more likely to have thyroid diseases. Asian COVID-19 infection, hypothyroidism patients, and patients with low T3 syndrome are more likely to progress to severe condition. Systematic Review Registration https://inplasy.com, identifier INPLASY202190079.

Highly pathogenic coronavirus N protein aggravates inflammation by MASP-2-mediated lectin complement pathway overactivation.

Excessive inflammatory responses contribute to the pathogenesis and lethality of highly pathogenic human coronaviruses, but the underlying mechanism remains unclear. In this study, the N proteins of highly pathogenic human coronaviruses, including severe acute respiratory syndrome coronavirus (SARS-CoV), middle east respiratory syndrome coronavirus (MERS-CoV) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), were found to bind MASP-2, a key serine protease in the lectin pathway of complement activation, resulting in excessive complement activation by potentiating MBL-dependent MASP-2 activation, and the deposition of MASP-2, C4b, activated C3 and C5b-9. Aggravated inflammatory lung injury was observed in mice infected with adenovirus expressing the N protein. Complement hyperactivation was also observed in SARS-CoV-2-infected patients. Either blocking the N protein:MASP-2 interaction, MASP-2 depletion or suppressing complement activation can significantly alleviate N protein-induced complement hyperactivation and lung injury in vitro and in vivo. Altogether, these data suggested that complement suppression may represent a novel therapeutic approach for pneumonia induced by these highly pathogenic coronaviruses.

Digital Patient Experience: Umbrella Systematic Review.

BACKGROUND: The adoption and use of technology have significantly changed health care delivery. Patient experience has become a significant factor in the entire spectrum of patient-centered health care delivery. Digital health facilitates further improvement and empowerment of patient experiences. Therefore, the design of digital health is served by insights into the barriers to and facilitators of digital patient experience (PEx). OBJECTIVE: This study aimed to systematically review the influencing factors and design considerations of PEx in digital health from the literature and generate design guidelines for further improvement of PEx in digital health. METHODS: We performed an umbrella systematic review following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology. We searched Scopus, PubMed, and Web of Science databases. Two rounds of small random sampling (20%) were independently reviewed by 2 reviewers who evaluated the eligibility of the articles against the selection criteria. Two-round interrater reliability was assessed using the Fleiss-Cohen coefficient (k1=0.88 and k2=0.80). Thematic analysis was applied to analyze the extracted data based on a small set of a priori categories. RESULTS: The search yielded 173 records, of which 45 (26%) were selected for data analysis. Findings and conclusions showed a great diversity; most studies presented a set of themes (19/45, 42%) or descriptive information only (16/45, 36%). The digital PEx-related influencing factors were classified into 9 categories: patient capability, patient opportunity, patient motivation, intervention technology, intervention functionality, intervention interaction design, organizational environment, physical environment, and social environment. These can have three types of impacts: positive, negative, or double edged. We captured 4 design constructs (personalization, information, navigation, and visualization) and 3 design methods (human-centered or user-centered design, co-design or participatory design, and inclusive design) as design considerations. CONCLUSIONS: We propose the following definition for digital PEx: "Digital patient experience is the sum of all interactions affected by a patient's behavioral determinants, framed by digital technologies, and shaped by organizational culture, that influence patient perceptions across the continuum of care channeling digital health." In this study, we constructed a design and evaluation framework that contains 4 phases-define design, define evaluation, design ideation, and design evaluation-and 9 design guidelines to help digital health designers and developers address digital PEx throughout the entire design process. Finally, our review suggests 6 directions for future digital PEx-related research.

Family function and life satisfaction of postgraduate medical students during the COVID-19 pandemic: the mediating role of meaning in life and depression.

Recent studies have documented life satisfaction of people have changed during the COVID-19 pandemic. However, it is unknown about the influential factors and mechanisms of life satisfaction of postgraduate medical students during the COVID-19 pandemic. There is a strong link among life satisfaction and individual quality of life and achievement, so it is important to explore the influence mechanism of life satisfaction of postgraduate medical students and explore ways to improve life satisfaction for the development of postgraduate medical students. The current study was based on the Circumplex Model of Marital and Family System, The Theory of Family Functioning, The Meaning Maintenance Model, The Theory of Personal Meaning and Existential Theory to construct theoretical framework and examine whether meaning in life and depression would mediate the link between family function and postgraduate medical students' life satisfaction. By convenient sampling method, a total of 900 postgraduate medical students (Mage = 27.01 years, SD = 3.33) completed questionnaires including Family APGAR Scale, Chinese Version of Meaning In Life Questionnaire, Patient Health Questionnaire, and Satisfaction With Life Scale. In this study, SPSS 25.0 was used for correlation analysis, regression analysis and common method bias test, and AMOS 23.0 was used for structural equation modeling analysis. The results showed that (a) family function could predict life satisfaction of postgraduate medical students significantly; (b) both meaning in life and depression mediated the association between family function and life satisfaction in a parallel manner; (c) meaning in life and depression sequentially mediated the link between family function and life satisfaction of postgraduate medical students. The study illuminates the role of meaning in life and depression in improving life satisfaction and implies that it is necessary to focus on the changes of life satisfaction of postgraduate medical students during the COVID-19 pandemic, and medical educator can improve the sense of meaning in life of postgraduate medical students through improving their family function, further decreasing the risk of depression, finally improving their life satisfaction.

Design-Relevant Factors Affecting the Patient Experience in Digital Health: Preliminary Results of an Umbrella Systematic Review.

Since Covid-19, digital health interventions (DHIs) have been embraced as never before. The pandemic led to many new challenges, including the patient experience in digital health care delivery. In this literature study, we identified and synthesized factors that impact patient experience in digital health (dPEx), and reviewed the methods and strategies relevant to its design and implementation. We conducted an umbrella review including 15 reviews representing 543 studies. Four themes were identified that describe design-relevant factors that impact dPEx: individual context, content, technical issues, and design features. We propose a preliminary framework to explain the relationship between each factor and support user-centered design efforts. Further research is needed to identify which factors have the most impact.

Quantifying Cross-Correlations between Economic Policy Uncertainty and Bitcoin Market: Evidence from Multifractal Analysis

We investigate the dynamic correlation between the Bitcoin price (BTC) and the U.S. economic policy uncertainty index (USEPU) from the perspective of multifractality. Utilizing the multifractal detrended cross-correlation analysis (MF-DCCA), we confirm a long-range cross-correlation between BTC and USEPU. Moreover, the empirical results of MF-DCCA show that the power-law properties and multifractal characteristics between BTC and USEPU are significant. We further examine the long-range dependency of cross-correlation between BTC and USEPU series via the Hurst exponent test and confirm the durable cross-correlation. Finally, we introduce another multifractal indicator and examine the extent of multifractality among time series. The empirical results indicate that the BTC series, USEPU series, and the cross-correlation of BTC-USEPU present apparent multifractality, where BTC shows the strongest degree of multifractality.

Basic influent sewage quality reflects sewershed characteristics in Tokyo city.

Sewage comprises multifarious information on sewershed characteristics. For instance, influent sewage quality parameters (ISQPs) (e.g., total nitrogen (TN)) are being monitored regularly at all treatment plants. However, the relationship between ISQPs and sewershed characteristics is rarely investigated. Therefore, this study statistically investigated relationships between ISQPs and sewershed characteristics, covering demographic, social, and economic properties in Tokyo city as an example of a megacity. To this end, we collected ISQPs and sewershed characteristic data from 2015 to 2020 in 10 sewersheds in Tokyo city. By principal component analysis, spatial variability of ISQPs was aggregated into two principal components (89.8% contribution in total), indicating organics/nutrients and inorganic salts, respectively. Concentrations of organics/nutrients were significantly correlated with the population in sewersheds (daytime population density, family size, age distribution, etc.). Inorganic salts are significantly correlated with land cover ratios. Finally, a multiple regression model was developed for estimating the concentration of TN based on sewershed characteristics (R2=0.97). Scenario analysis using the regression model revealed that possible population movements in response to the coronavirus pandemic would substantially reduce the concentration of TN. These results indicate close relationships between ISQPs and sewershed characteristics and the potential applicability of big data of ISQPs to estimate sewershed characteristics and vice versa.

Network analysis of risk transmission among energy futures: An industrial chain perspective

The fluctuation of energy prices has a great impact on the economy, making it essential to analyze the risk transmission among energy commodities. In this paper, we use the minimum spanning tree (MST) approach and connectedness method to study the risk transmission among energy futures in China. Eleven commodities, including two globally traded crude oil, are considered. Four major results are obtained. First, MST analysis provides evidence of industry clustering. Second, the risk transmission is generally from the petrochemical sector to the coal sector;while the risk transmission in each sector mainly spills from upstream to the downstream products. Third, “methanol - polyethylene” bridges the coal sector and petrochemical sector, through which the link between the two sectors has been strengthened since the outbreak of COVID-19. Fourth, the systemic risk of the energy market has increased, and several commodities have experienced a role reversal in the network since the pandemic began, especially in the petrochemical sector.

Identification of the Key Immune-Related Genes in Chronic Obstructive Pulmonary Disease Based on Immune Infiltration Analysis.

Purpose: Chronic obstructive pulmonary disease (COPD) is a major cause of death and morbidity worldwide. A better understanding of new biomarkers for COPD patients and their complex mechanisms in the progression of COPD are needed. Methods: An algorithm was conducted to reveal the proportions of 22 subsets of immune cells in COPD samples. Differentially expressed immune-related genes (DE-IRGs) were obtained based on the differentially expressed genes (DEGs) of the GSE57148 dataset, and 1509 immune-related genes (IRGs) were downloaded from the ImmPort database. Functional enrichment analyses of DE-IRGs were conducted by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses and Ingenuity Pathway Analysis (IPA). We defined the DE-IRGs that had correlations with immune cells as hub genes. The potential interactions among the hub genes were explored by a protein-protein interaction (PPI) network. Results: The CIBERSORT results showed that lung tissue of COPD patients contained a greater number of resting NK cells, activated dendritic cells, and neutrophils than normal samples. However, the fractions of follicular helper T cells and resting dendritic cells were relatively lower. Thirty-eight DE-IRGs were obtained for further analysis. Functional enrichment analysis revealed that these DE-IRGs were significantly enriched in several immune-related biological processes and pathways. Notably, we also observed that DE-IRGs were associated with the coronavirus disease COVID-19 in the progression of COPD. After correlation analysis, six DE-IRGs associated with immune cells were considered hub genes, including AHNAK, SLIT2 TNFRRSF10C, CXCR1, CXCR2, and FCGR3B. Conclusion: In the present study, we investigated immune-related genes as novel diagnostic biomarkers and explored the potential mechanism for COPD based on CIBERSORT analysis, providing a new understanding for COPD treatment.

Herb-Drug Interaction Between Xiyanping Injection and Lopinavir/Ritonavir, Two Agents Used in COVID-19 Pharmacotherapy.

The global epidemic outbreak of the coronavirus disease 2019 (COVID-19), which exhibits high infectivity, resulted in thousands of deaths due to the lack of specific drugs. Certain traditional Chinese medicines (TCMs), such as Xiyanping injection (XYPI), have exhibited remarkable benefits against COVID-19. Although TCM combined with Western medicine is considered an effective approach for the treatment of COVID-19, the combination may result in potential herb-drug interactions in the clinical setting. The present study aims to verify the effect of XYPI on the oral pharmacokinetics of lopinavir (LPV)/ritonavir (RTV) using an in vivo rat model and in vitro incubation model of human liver microsomes. After being pretreated with an intravenous dose of XYPI (52.5 mg/kg) for one day and for seven consecutive days, the rats received an oral dose of LPV/RTV (42:10.5 mg/kg). Except for the t1/2 of LPV is significantly prolonged from 4.66 to 7.18 h (p < 0.05) after seven consecutive days pretreatment, the pretreatment resulted in only a slight change in the other pharmacokinetic parameters of LPV. However, the pharmacokinetic parameters of RTV were significantly changed after pretreatment with XYPI, particularly in treatment for seven consecutive days, the AUC0-∞ of RTV was significantly shifted from 0.69 to 2.72 h µg/mL (p < 0.05) and the CL exhibited a tendency to decrease from 2.71 L/h to 0.94 L/h (p < 0.05), and the t1/2 of RTV prolonged from 3.70 to 5.51 h (p < 0.05), in comparison with the corresponding parameters in untreated rats. After administration of XYPI, the expression of Cyp3a1 protein was no significant changed in rats. The in vitro incubation study showed XYPI noncompetitively inhibited human CYP3A4 with an apparent Ki value of 0.54 mg/ml in a time-dependent manner. Our study demonstrated that XYPI affects the pharmacokinetics of LPV/RTV by inhibiting CYP3A4 activity. On the basis of this data, patients and clinicians can take precautions to avoid potential drug-interaction risks in COVID-19 treatment.

Basic Characteristics, Status, and Challenges of Integrative Oncology in China.

Integrative oncology has developed for about 20 years in some countries; however, integrative oncology is still a relative new term for most China's oncologists. Thus, it is essential to summarize the experience and expertise, share details of differing existing models and discuss future perspectives to help define and guide practice in integrative oncology in China. This study presents a summary of the basic characteristics, status, and challenges of integrative oncology in China, and also reports on China's integrative physicians' service delivery, clinical practice and research patterns of integrative oncology by an online national survey, including 405 oncologists. It is easy for cancer patients to access to integrative therapies in China. Public funding is sufficient for integrative oncology in China, and services are often provided through general hospitals and academic hospitals. Most (95.3%) of oncologists showed a positive attitude toward the development of integrative oncology. More than half (55.6%) of the oncologists worried about the influence on integrative oncology of COVID-19, especially for routine treatment, follow-up and holding seminars. We found that integrative oncology in China has swiftly developed in recent years. However, we suggest that standard diagnosis and treatment patterns and national professional guidelines should be set up as soon as possible.

The Impact of the Corona Virus Disease 2019 Pandemic on Chinese Middle School Students' Self-Perceived Emotional States: A Wuhan Perspective.

This mixed-methods study examined the impact of the COVID-19 pandemic on 1493 Grades 7, 8, and 9 students' self-perceived emotional states in Wuhan, China when it was locked down for the pandemic on January 23, 2020 and when the lockdown was lifted on April 8, 2020, as well as the changes of their emotional states over the 1-year period after the lockdown was lifted. A five-point Likert scale survey was administered to the participants between March 1 and April 1, 2020 when Wuhan was blocked down; and three focus group interviews were conducted between May 1 and May 31, 2021, 1 year after the lockdown was lifted. The results showed that these students in Wuhan experienced feelings of loss of control and negative emotions when the city was locked down and they were home quarantined; furthermore, there were significant differences for their self-perceived feelings of loss of control and negative emotions across demographic variables of gender, grade level, physical activity, social economic status, and family cohesion; finally, their emotional states changed substantially at different time nodes during this pandemic. Implications for students, parents, and schools are discussed.

Metabolic Reprogramming in COVID-19.

Plenty of research has revealed virus induced alternations in metabolic pathways, which is known as metabolic reprogramming. Studies focusing on COVID-19 have uncovered significant changes in metabolism, resulting in the perspective that COVID-19 is a metabolic disease. Reprogramming of amino acid, glucose, cholesterol and fatty acid is distinctive characteristic of COVID-19 infection. These metabolic changes in COVID-19 have a critical role not only in producing energy and virus constituent elements, but also in regulating immune response, offering new insights into COVID-19 pathophysiology. Remarkably, metabolic reprogramming provides great opportunities for developing novel biomarkers and therapeutic agents for COVID-19 infection. Such novel agents are expected to be effective adjuvant therapies. In this review, we integrate present studies about major metabolic reprogramming in COVID-19, as well as the possibility of targeting reprogrammed metabolism to combat virus infection.

Research progress on potential drug interactions of anti-Coronavirus disease 2019 drugs

From the December 2019, coronavirus disease 2019 (COVID-19) was outbreak at home and abroad. Besides antiviral therapy, patients should be treated for complications, therefore a combination of drugs for treatment in Clinic need to be taken. Although recently published guidelines have repeatedly highlighted the drug interactions between the anti-COVID-l 9 medicines, it has not been detailed. The potential drug - drug interactions were reviewed 0f the anti-COVID-19 drugs, and in order to provide references for the clinical safety and rational use of the anti-COVID-19 drugs.

Antiviral Drug Delivery System for Enhanced Bioactivity, Better Metabolism and Pharmacokinetic Characteristics.

Antiviral drugs (AvDs) are the primary resource in the global battle against viruses, including the recent fight against corona virus disease 2019 (COVID-19). Most AvDs require multiple medications, and their use frequently leads to drug resistance, since they have poor oral bioavailability and low efficacy due to their low solubility/low permeability. Characterizing the in vivo metabolism and pharmacokinetic characteristics of AvDs may help to solve the problems associated with AvDs and enhance their efficacy. In this review of AvDs, we systematically investigated their structure-based metabolic reactions and related enzymes, their cellular pharmacology, and the effects of metabolism on AvD pharmacodynamics and pharmacokinetics. We further assessed how delivery systems achieve better metabolism and pharmacology of AvDs. This review suggests that suitable nanosystems may help to achieve better pharmacological activity and pharmacokinetic behavior of AvDs by altering drug metabolism through the utilization of advanced nanotechnology and appropriate administration routes. Notably, such AvDs as ribavirin, remdesivir, favipiravir, chloroquine, lopinavir and ritonavir have been confirmed to bind to the severe acute respiratory syndrome-like coronavirus (SARS-CoV-2) receptor and thus may represent anti-COVID-19 treatments. Elucidating the metabolic and pharmacokinetic characteristics of AvDs may help pharmacologists to identify new formulations with high bioavailability and efficacy and help physicians to better treat virus-related diseases, including COVID-19.