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Objective To explore the sociodemographic and psychological factors influencing the continuity of treatment of patients with chronic kidney disease under the regular epidemic prevention and control of coronavirus disease 2019 (COVID-19). Methods A total of 277 patients with chronic kidney disease who were admitted to Department of Nephrology, The First Affiliated Hospital of Naval Medical University (Second Military Medical University) from Apr. 2020 to Mar. 2021 were enrolled and divided into 3 groups: non-dialysis group (n=102), hemodialysis (HD) group (n=108), and peritoneal dialysis (PD) group (n=67). All patients were investigated by online and offline questionnaires, including self-designed basic situation questionnaire, self-rating anxiety scale (SAS), and self-rating depression scale (SDS). The general sociodemographic data, anxiety and depression of the 3 groups were compared, and the influence of sociodemographic and psychological factors on the interruption or delay of treatment was analyzed by binary logistic regression model. Results There were significant differences in age distribution, marital status, occupation, medical insurance type, caregiver type, whether there was an urgent need for hospitalization and whether treatment was delayed or interrupted among the 3 groups (all P0.05). The average SAS score of 65 PD patients was 38.15+/-15.83, including 53 (81.5%) patients without anxiety, 7 (10.8%) patients with mild anxiety, and 5 (7.7%) patients with moderate to severe anxiety. The average SAS score of 104 patients in the HD group was 36.86+/-14.03, including 81 (77.9%) patients without anxiety, 18 (17.3%) patients with mild anxiety, and 5 (4.8%) patients with moderate to severe anxiety. There were no significant differences in the mean score of SAS or anxiety severity grading between the 2 groups (both P0.05). The mean SDS scores of 65 PD patients were 53.42+/-13.30, including 22 (33.8%) patients without depression, 21 (32.3%) patients with mild depression, and 22 (33.8%) patients with moderate to severe depression. The mean SDS scores of 104 patients in the HD group were 50.79+/-10.76, including 36 (34.6%) patients without depression, 56 (53.8%) patients with mild depression, and 12 (11.6%) patients with moderate to severe depression. There were no significant differences in mean SDS scores or depression severity grading between the 2 groups (both P0.05). The results of intra-group comparison showed that the incidence and severity of depression were higher than those of anxiety in both groups. Multivariate binary logistic regression analysis showed that high school education level (odds ratio OR=5.618, 95% confidence interval CI) 2.136-14.776, P0.01), and unmarried (OR=6.916, 95% CI 1.441-33.185, P=0.016), divorced (OR= 5.588, 95% CI 1.442-21.664, P=0.013), urgent need for hospitalization (OR=8.655, 95% CI 3.847-19.476, P0.01) could positively promote the continuity of treatment in maintenance dialysis patients under the regular epidemic prevention and control of COVID-19. In the non-dialysis group, no sociodemographic and psychological factors were found to be associated with the interruption or delay of treatment (P0.05). Conclusion Education, marital status, and urgent need for hospitalization are correlated with the continuity of treatment in patients with chronic kidney disease on maintenance dialysis.Copyright © 2022, Second Military Medical University Press. All rights reserved.
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Objective To explore the sociodemographic and psychological factors influencing the continuity of treatment of patients with chronic kidney disease under the regular epidemic prevention and control of coronavirus disease 2019 (COVID-19). Methods A total of 277 patients with chronic kidney disease who were admitted to Department of Nephrology, The First Affiliated Hospital of Naval Medical University (Second Military Medical University) from Apr. 2020 to Mar. 2021 were enrolled and divided into 3 groups: non-dialysis group (n=102), hemodialysis (HD) group (n=108), and peritoneal dialysis (PD) group (n=67). All patients were investigated by online and offline questionnaires, including self-designed basic situation questionnaire, self-rating anxiety scale (SAS), and self-rating depression scale (SDS). The general sociodemographic data, anxiety and depression of the 3 groups were compared, and the influence of sociodemographic and psychological factors on the interruption or delay of treatment was analyzed by binary logistic regression model. Results There were significant differences in age distribution, marital status, occupation, medical insurance type, caregiver type, whether there was an urgent need for hospitalization and whether treatment was delayed or interrupted among the 3 groups (all P<0.05). The average SAS score of 65 PD patients was 38.15+/-15.83, including 53 (81.5%) patients without anxiety, 7 (10.8%) patients with mild anxiety, and 5 (7.7%) patients with moderate to severe anxiety. The average SAS score of 104 patients in the HD group was 36.86+/-14.03, including 81 (77.9%) patients without anxiety, 18 (17.3%) patients with mild anxiety, and 5 (4.8%) patients with moderate to severe anxiety. There were no significant differences in the mean score of SAS or anxiety severity grading between the 2 groups (both P>0.05). The mean SDS scores of 65 PD patients were 53.42+/-13.30, including 22 (33.8%) patients without depression, 21 (32.3%) patients with mild depression, and 22 (33.8%) patients with moderate to severe depression. The mean SDS scores of 104 patients in the HD group were 50.79+/-10.76, including 36 (34.6%) patients without depression, 56 (53.8%) patients with mild depression, and 12 (11.6%) patients with moderate to severe depression. There were no significant differences in mean SDS scores or depression severity grading between the 2 groups (both P>0.05). The results of intra-group comparison showed that the incidence and severity of depression were higher than those of anxiety in both groups. Multivariate binary logistic regression analysis showed that high school education level (odds ratio [OR]=5.618, 95% confidence interval [CI]) 2.136-14.776, P<0.01), and unmarried (OR=6.916, 95% CI 1.441-33.185, P=0.016), divorced (OR= 5.588, 95% CI 1.442-21.664, P=0.013), urgent need for hospitalization (OR=8.655, 95% CI 3.847-19.476, P<0.01) could positively promote the continuity of treatment in maintenance dialysis patients under the regular epidemic prevention and control of COVID-19. In the non-dialysis group, no sociodemographic and psychological factors were found to be associated with the interruption or delay of treatment (P>0.05). Conclusion Education, marital status, and urgent need for hospitalization are correlated with the continuity of treatment in patients with chronic kidney disease on maintenance dialysis.Copyright © 2022, Second Military Medical University Press. All rights reserved.
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With the development of Medical Internet of Things (MIoT) technology and the global COVID-19 pandemic, hospitals gain access to patients' health data from remote wearable medical equipment. Federated learning (FL) addresses the difficulty of sharing data in remote medical systems. However, some key issues and challenges persist, such as heterogeneous health data stored in hospitals, which leads to high communication cost and low model accuracy. There are many approaches of federated distillation (FD) methods used to solve these problems, but FD is very vulnerable to poisoning attacks and requires a centralised server for aggregation, which is prone to single-node failure. To tackle this issue, we combine FD and blockchain to solve data sharing in remote medical system called FedRMD. FedRMD use reputation incentive to defend against poisoning attacks and store reputation values and soft labels of FD in Hyperledger Fabric. Experimenting on COVID-19 radiography and COVID-Chestxray datasets shows our method can reduce communication cost, and the performance is higher than FedAvg, FedDF, and FedGen. In addition, the reputation incentive can reduce the impact of poisoning attacks. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
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The identification of the mechanisms by which T-cell receptors (TCRs) interact with human antigens provides a crucial opportunity to develop new vaccines, diagnostics and immunotherapies. However, the accurate prediction and recognition of TCR–antigen pairing represents a substantial computational challenge in immunology. Existing tools only learn the binding patterns of antigens from many known TCR binding repertoires and fail to recognize antigens that have never been presented to the immune system or for which only a few TCR binding repertoires are known. However, the binding specificity for neoantigens or exogenous peptides is crucial for immune studies and immunotherapy. Therefore, we developed Pan-Peptide Meta Learning (PanPep), a general and robust framework to recognize TCR–antigen binding, by combining the concepts of meta-learning and the neural Turing machine. The neural Turing machine adds external memory to avoid forgetting previously learned tasks, which is used here to accurately predict TCR binding specificity with any peptide, particularly unseen ones. We applied PanPep to various challenging clinical tasks, including (1) qualitatively measuring the clonal expansion of T cells;(2) efficiently sorting responsive T cells in tumour neoantigen therapy;and (3) accurately identifying immune-responsive TCRs in a large cohort from a COVID-19 study. Our comprehensive tests show that PanPep outperforms existing tools. PanPep also offers interpretability, revealing the nature of peptide and TCR interactions in 3D crystal structures. We believe PanPep can be a useful tool to decipher TCR–antigen interactions and that it has broad clinical applications. © 2023, The Author(s), under exclusive licence to Springer Nature Limited.
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Objective: To analyze the risk factors of fatal outcome in patients with severe COVID-19. Method(s): The clinical characteristics of 107 patients with severe COVID-19 admitted in Renmin Hospital of Wuhan University from February 12 to March 12, 2020 were retrospectively analyzed. During the hospitalization 49 patients died (fatal group) and 58 patients survived (survival group). The clinical characteristics, baseline laboratory findings were analyzed using R and Python statistical software. The risk factors of fatal outcome in patients with severe COVID-19 were analyzed with multivariate logistic regression. Result(s): Univariate analysis showed that the two groups had statistically significant differences in age, clinical classification, dry cough, dyspnea and laboratory test indicators (P<0.05 or <0.01). The random forest model was used to rank the significance of the statistically significant variables in the univariate analysis, and the selected variables were included in the binary logistic regression model. After stepwise regression analysis, the patient's clinical type, age, neutrophil count, and the proportion of CD3 cells are independent risk factors for death in severe COVID-19 patients. Dry cough is an independent protective factor for the death of severe COVID-19 patients. Conclusion(s): COVID-19 patients with fatal outcome are more likely to have suppressed immune function, secondary infection and inflammatory factor storm. These factors may work together in severe patients, leading to intractable hypoxemia and multiple organ dysfunction and resulting in fatal outcome of patients. The study indicates that timely intervention and treatment measures against above factors may be effective to save the lives of patients with severe COVID-19.Copyright © 2020 by the Chinese Medical Association.
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Objective: To analyze the risk factors of fatal outcome in patients with severe COVID-19. Method(s): The clinical characteristics of 107 patients with severe COVID-19 admitted in Renmin Hospital of Wuhan University from February 12 to March 12, 2020 were retrospectively analyzed. During the hospitalization 49 patients died (fatal group) and 58 patients survived (survival group). The clinical characteristics, baseline laboratory findings were analyzed using R and Python statistical software. The risk factors of fatal outcome in patients with severe COVID-19 were analyzed with multivariate logistic regression. Result(s): Univariate analysis showed that the two groups had statistically significant differences in age, clinical classification, dry cough, dyspnea and laboratory test indicators (P<0.05 or <0.01). The random forest model was used to rank the significance of the statistically significant variables in the univariate analysis, and the selected variables were included in the binary logistic regression model. After stepwise regression analysis, the patient's clinical type, age, neutrophil count, and the proportion of CD3 cells are independent risk factors for death in severe COVID-19 patients. Dry cough is an independent protective factor for the death of severe COVID-19 patients. Conclusion(s): COVID-19 patients with fatal outcome are more likely to have suppressed immune function, secondary infection and inflammatory factor storm. These factors may work together in severe patients, leading to intractable hypoxemia and multiple organ dysfunction and resulting in fatal outcome of patients. The study indicates that timely intervention and treatment measures against above factors may be effective to save the lives of patients with severe COVID-19.Copyright © 2020 by the Chinese Medical Association.
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Objective: To analyze the risk factors of fatal outcome in patients with severe COVID-19. Method(s): The clinical characteristics of 107 patients with severe COVID-19 admitted in Renmin Hospital of Wuhan University from February 12 to March 12, 2020 were retrospectively analyzed. During the hospitalization 49 patients died (fatal group) and 58 patients survived (survival group). The clinical characteristics, baseline laboratory findings were analyzed using R and Python statistical software. The risk factors of fatal outcome in patients with severe COVID-19 were analyzed with multivariate logistic regression. Result(s): Univariate analysis showed that the two groups had statistically significant differences in age, clinical classification, dry cough, dyspnea and laboratory test indicators (P<0.05 or <0.01). The random forest model was used to rank the significance of the statistically significant variables in the univariate analysis, and the selected variables were included in the binary logistic regression model. After stepwise regression analysis, the patient's clinical type, age, neutrophil count, and the proportion of CD3 cells are independent risk factors for death in severe COVID-19 patients. Dry cough is an independent protective factor for the death of severe COVID-19 patients. Conclusion(s): COVID-19 patients with fatal outcome are more likely to have suppressed immune function, secondary infection and inflammatory factor storm. These factors may work together in severe patients, leading to intractable hypoxemia and multiple organ dysfunction and resulting in fatal outcome of patients. The study indicates that timely intervention and treatment measures against above factors may be effective to save the lives of patients with severe COVID-19.Copyright © 2020 by the Chinese Medical Association.
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Green innovation is an important driving force for sustainable development. However China often imposes a wide variety of government regulations on green innovation One important reason behind these government regulations is the confinement of the cultural market. However, does this confinement actually affect the green innovation in China? By employing a 278 Chinese cities' dataset, we examine the effect of cultural reform pilot project on green innovation. Through the spatial difference-in-difference approach with the time trend, our results show that cultural reform pilot project (CRPP) is a significant determinist affecting the green innovation in China. Specifically, implementing CRPP promote green innovation in pilot cities which resulting from labour productivity exaltation, marketization rate increasing. The CRPP also have a spatial ripple effect which resulting from economic density promotion. Furthermore, the green innovation promotion is greater in cities which participating into World Technopolis Association, being included in the National Historical and Cultural Cities List and having high political hierarchy. Our conclusions still robustness after adopting a series of tests and alternative analyses. This paper not only provide evidence for the further implementation of cultural reform pilot project nationwide, but also provide policy implications on sustainable development in the post Covid-19 era. © 2023 Economic Society of Australia, Queensland
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Biosafety issues have aroused global concern, especially after the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron strain of corona virus disease 2019 (COVID-19) caused incalculable human and property losses. Laboratory-acquired infections (LAIs) caused by improper operations or accidents are frequently reported. Research is urgently needed for a mobile biosafety level-4 (BSL-4) laboratory with a high risk for exposure. Deposition characteristics and the spatial distribution of bioaerosols under two typical cases were studied in this paper. Based on the age of air and simulation of airflow pattern, a detailed analysis of infection risk and the distribution of bioaerosols was conducted. The deposition characteristics of particles on different surfaces were analyzed based on particle tracking technology. The results showed that the removal rate of bioaerosols was lower in the space area of the laboratory from 1.6 m above the ground. The distribution of high-risk areas is affected by the coupling of equipment layout and pollution sources, mainly located downstream of the main airflow in the laboratory, and the particle concentration was eight times that of the low-risk areas. More than half of bioaerosol particles are deposited on laboratory equipment and walls. The number of particles deposited on the wall was the largest, accounting for 25.02% of the total. The unit area deposition ratio of the experimental table was the highest, which was 6.14 %/m2. The main deposition area of each surface was determined, which could be of guiding significance to the determination of the key disinfection location of the mobile BSL-4 laboratory. © 2023 Elsevier Ltd
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Bus operators have to make trade-offs between transporting more passengers and maintaining social distancing to reduce ridership congregation amid Corona Virus Disease 2019 (COVID-19) outbreak. The traditional bus boarding mode could easily lead passengers fully occupy the bus available capacity at one stop, and it would prevent subsequent passengers from boarding. It is crucial to establish a new operating mode and strategy to ensure all passengers have opportunities to ride and to collaboratively optimise the bus timetable. In this paper, the boarding limit strategy that considers the fairness of passenger boarding probability is proposed to address the inequitable problem with minimise the passenger travel time and the number of stranded passengers. The coupling relationship between bus dwell time and passenger flow is used to collaboratively optimise the bus timetable. Case studies are conducted to illustrate the performance of the boarding limit strategy in improving passenger boarding equity. © 2023 Hong Kong Society for Transportation Studies Limited.
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Introduction: During the COVID-19 pandemic, clinical sites have closed their doors to student placements, leading to the implementation of remote rotations. The purpose was to determine best practices for distance preceptorship from the student's perspective. Method(s): A survey was sent to the pharmacy students at the Leslie Dan Faculty of Pharmacy who have completed at least one remote rotation. Result(s): Forty-eight out of 121 students (39%) completed the survey. It was found that 83% of the students were motivated during the start of their rotations, while 48% remained motivated throughout. Students who remained motivated had clear expectations set from the beginning, felt comfortable communicating issues regarding their assigned work with their preceptor, had similar rapport with remote preceptors as with in-person preceptors, had a preceptor who is always available for questions, and had a work environment free of distractions. Discussion(s):There are numerous best practices students and preceptors can utilise during a remote rotation to help students remain motivated. Preceptors and students should work together so that students remain motivated throughout their rotation. Setting expectations, having good communication, getting to know their preceptor, and having a work environment free of distractions are key factors for conducting a remote rotation. Copyright © 2020 FIP.
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A passenger flow time series forecasting method based on empirical mode decomposition (EMD) and K-nearest neighbor nonparametric regression (KNN) was proposed. Based on the principle of EMD and KNN algorithm, the EMD-KNN combined algorithm flow was constructed on the basis of improving KNN prediction method. According to the characteristics that the time series trend of passenger flow has changed obviously due to the influence of COVID-19 epidemic situation in the example stations. BP structural breakpoint detection method was used to identify three structural breakpoints, and the time series segment with the closest passenger flow change trend to the forecast day was selected for empirical mode decomposition. The decomposed sequences were reorganized into high-frequency, low-frequency and trend sequences, and then the K-nearest neighbor algorithm considering weight was used to predict, and the final prediction results were obtained by superposition, and compared with the prediction results of single KNN algorithm and ARIMA model. The results show that the prediction accuracy of EMD-KNN combination algorithm is higher than that of single KNN algorithm and ARIMA model, and it can effectively capture the changing trend of passenger flow. © 2022, Editorial Department of Journal of Wuhan University of Technology. All right reserved.
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As one of the most polluted provinces in China, air pollution events occur frequently in Shandong. Based on the hourly (or daily) concentrations of six air pollutants (PM2.5, PM10, O3, NO2, SO2 and CO), the situations of air quality improvement in three kinds of cities (key cities, coastal cities and general cities) are assessed comprehensively during 2014-2020. Contrary to the daily maximum 8-h average ozone (MDA8 O3), the annual average concentrations of other pollutants show the downward trends during 2014-2020. Therein, the improvement rates of annual average concentrations of air pollutants in key cities are highest. By 2020, the day proportions of O3 as the primary pollutant are up to 38% in three kinds of cities. Besides, due to the impact of COVID-19, the monthly average concentrations of PM2.5, PM10, NO2, SO2 and CO in February 2020 decrease by 32.1-49.5% year-on-year. There are still about 50% of population exposed to high-risk regions (R i > 2), which are mainly concentrated in main urban areas and industrial areas. Thus, the adjustment of industrial structure and energy composition in the context of carbon peak and carbon neutrality should be implemented in the future. Supplementary Information: The online version contains supplementary material available at 10.1007/s13762-022-04651-5.
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Background: MELD and Child-Pugh scores have traditionally been used as prognostic indicators in patients with cirrhosis. Albumin infusions in outpatients have been associated with improved outcomes, but not in transplant waitlisted patients or inpatients. This aim of this study was to assess whether low serum albumin (sAlb) on admission alone is a poor prognostic indicator among cirrhotic inpatients from a new multi-national cohort. Method(s): The CLEARED study is a global study that enrolled consecutive non-electively admitted inpatients without organ transplant or COVID-19 from 6 continents. Admission demographics, medical history, laboratory data, inpatient course, death/hospice transfer and mortality at 30 days post-discharge were recorded. Patients were divided into 3 groups: sAlb <28gm/L(A), sAlb >=28 but <35gm/L (B), and sAlb>=35gm/L (C) were compared. Multi-variable logistic regression was performed using inpatient mortality and overall 30-day mortality as outcomes. Result(s): 2429 patients were enrolled from 21 countries worldwide. The distribution was A:49%, B:39%, C:12%. Gp A patients were significantly younger (54yrs vs. 57yrs vs 58yrs p<0.0001) but with similar gender distribution, and higher MELD-Na score of 25 vs. 20 vs. 17 (p<0.0001). Gp A patients were more likely to have alcohol as etiology of cirrhosis (49% vs. 45% vs 38%, p=0.004), and were more likely to have either infection (27% vs. 18% vs. 13%, p<0.0001), HE (39% vs. 33% vs. 23%, p=0.005) or fluid related issues as a reason for admission (p<0.0001). More patients in Gp A received albumin infusion during their hospital stay (120gm vs. 100gm vs. 100gm p=0.0004), mostly for the indications of AKI (47% vs. 49% vs. 47%, p=0.79) and performance of large volume paracentesis (44% vs. 42% vs. 41%, p=0.80), followed by bacterial peritonitis indication (22% vs. 17% vs. 11%, p=0.01). Group A patients had longer hospital stays (9 days vs. 8 days vs. 7 days (p<0.001), but similar ICU transfer (23% vs. 22% vs. 20%, p=0.55). group A patients were more likely to die while inpatients (19% vs. 11% vs. 5%, p<0.0001), or by 30 days post-discharge (29% vs. 20% vs. 9%, p<0.0001). Table shows the admission variables associated with a poor outcome. Conclusion(s): Hypoalbuminemia is extremely common among admitted cirrhotic patients, with sAlb of <28gm/L occurring in almost half. Together with MELD-Na score and infection at admission, a low sAlb is associated with a poor outcome in these patients. Future studies will need to validate these findings and to assess whether albumin infusions will improve the outcome of these patients. (Figure Presented).
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Background: Although cirrhosis is a major cause of mortality worldwide, there could be disparities in outcomes. This needs a global consortium to study disparities in inpatient cirrhosis care Aim: Define the impact of location in prediction of outcomes in inpts with cirrhosis. Method(s): CLEARED prospectively enrolled non-electively admitted cirrhosis pts without COVID from all continents. To ensure equity, we allowed only 50 pts/site. Admission details, cirrhosis history, inpatient & 30-day course were recorded. World bank classification of low/low middle income (LMI), upper middle (UMI) & High income (HI) were used. Cirrhosis details, inpatient & 30-day outcomes were compared between groups. Multi-variable regression was performed using inpatient & 30-day mortality as outcomes. Result(s): 2758 pts from 21 countries from all continents, including Africa & Australia, were included.727 were L/LMI, 1050 UMI & 981 pts were from HICs. More men & younger pts were in LMI. Cirrhosis details: More pts in HI gp had 6M hospitalizations & infections, HE & ascites while prior variceal bleeding was higher in LMI . Prior HCC & transplant listings were lower in LMI but similar in UMI/HI. Alcohol & NASH was highest in HI. Viral hepatitis & cryptogenic were highest in UMI.Admissions: Admission MELD was highest in LMI. LMI pts were admitted more for GI Bleed, HE, & DILI, while anasarca & HBV flares were higher in UMI. Higher SBP (36% vs 24% vs 21% p<0.0001) & lowest skin/soft-tissue infections were in LMI (5% vs 5% vs 10% p=0.008);rest were similar. Nosocomial infections, driven by UTI were highest in LMI & HI pts (15% vs 14% vs 11% UMI, p=0.03). Admission diuretics, PPIs, Lactulose & statins were highest & antivirals lower in HI. SBP prophylaxis & rifaximin were highest in LMI pts. Outcome(s): More LMI pts needed ICU & had more organ failures (Fig B). Discharge MELD was highest in LMI. In-hospital mortality was highest & transplant lowest in LMI. This extended to 30-day mortality & transplant in LMI patients vs HI pts.Regression: In-hospital mortality was linked with age, infections, MELD & being in a LMI/UMI vs HIC while being on a transplant list, diabetes, & SBP prophylaxis were protective (Fig C). 30-day mortality predicted by age, ascites, HCC, discharge MELD, organ failures, LMI/UMI vs HIC but rifaximin was protective(Fig D). In-hospital transplant was higher with high MELD, admission rifaximin & listed pts &lower in LMI (OR 0.26) & UMI (OR 0.22) & age. 30-day transplant was higher in those with hyponatremia, ascites & HRS, on the list & on rifaximin and lower in LMI (OR 0.24) & UMI (OR 0.59) vs HI. Conclusion(s): In a global study of inpatients with cirrhosis, there were major differences in outcomes. Not being in a high-income country significantly increased the risk of inpatient and 30-day mortality independent of demographics, medications, in-hospital course, and cirrhosis severity likely due to disparities in access to transplant, which should be accounted for in global models. (Figure Presented).
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Objectives: Coronavirus disease 2019 (COVID-19) has had a global impact and is spreading quickly. ChuanKeZhi injection (CKZI) is widely used in asthma patients. In this paper, we aimed to explore active compounds of CKZ and determine potential mechanisms against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) through network pharmacology, molecular docking and dynamic simulation studies. Materials and Methods: We used the Systematic Pharmacology Database and Analysis Platform of Traditional Chinese Medicine (TCMSP) to screen active compounds and potential target proteins of CKZ. COVID-19 target genes were screened via the American National Center for Biotechnology Information (NCBI) gene database and human gene database (GeenCards). The protein interaction network was constructed by the Protein Interaction Network Database (Search Tool for the Retrieval of Interacting Genes/Proteins (STRING)) platform. GO enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed by the Metascape database. The main active compounds of CKZ were docked with angiotensin-converting enzyme 2 (ACE2), spike protein S1, and SARS-CoV-2-3CL pro and also docked with hub targets. We performed molecular dynamics (MD) simulation studies for validation. Results: We finally obtained 207 CKZ potential targets and 4681 potential COVID-19 targets. Key targets included mainly AKT1, TNF, IL6, VEGFA, IL1B, TP53, JUN, CASP3, etc. There were 217 Gene Ontology (GO) items in the GO enrichment analysis (p < 0.05). The main KEGG pathways included the advanced glycation end products (AGE)- receptor for AGE (RAGE) signalling pathway in diabetic complications, rheumatoid arthritis, chemical carcinogenesis-receptor activation, alcoholic liver disease, etc. Molecular docking and dynamics simulation studies both exhibited great binding capacity. Conclusions: Network pharmacology, molecular docking and dynamics simulation studies were used to identify the potential and key targets, pharmacological functions, and therapeutic mechanisms of CKZI in the treatment of COVID-19. CKZI may be an effective and safe drug in COVID-19 treatment. However, further work is needed for validation.
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To observe the synergistic effect of garlic essential oil in patients with novel coronavirus disease (COVID-19), in addition to the routine treatment, we used garlic essential oil in COVID-19 patients with mild to moderate symptoms and compared their results to those of patients who did not receive the essential oil. We conducted a quasi-experimental study with COVID-19 patients from 3 hospitals. In the experimental group, 97 patients received garlic essential oil combined with conventional treatment. In the control group, 100 patients received only the conventional treatment for COVID-19. The effectiveness and safety of the garlic essential oil were assessed. Compared to the control group, the group receiving garlic essential oil showed a shorter duration of symptoms, shorter time to negative nucleic acid testing (NAT) results and shorter time to improvement on the computed tomography (CT). In the same period, the experimental group showed an increase in the rate of the disappearance of symptoms and the improvement rates of NAT and CT. Due to its effectiveness and safety in patients with COVID-19, garlic essential oil is recommended as a preventive measure or a supportive therapy during the COVID-19 pandemic.
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Identifying and understanding the hesitancy degree of public COVID-19 vaccine in emergency may be helpful to the dissemination of vaccine-related public health information. Through a survey among the adult population of Chinese mainland (N = 1080) after the COVID-19 vaccine was approved for mass vaccination, it is found that although more than 80% of the public (87.8%) have a low hesitancy attitude towards COVID-19 vaccine, a considerable number of people still have a medium hesitancy and a high hesitancy attitude towards COVID-19 vaccine (the middle hesitancy rate is 9.8% and the high hesitancy rate is 2.4%). By multiple logistic regression, the subjective and objective knowledge levels of medium-high hesitancy group and low hesitancy group in COVID-19 vaccine were compared. The results showed that there were significant differences in subjective and objective knowledge levels between medium-high hesitancy group and low-hesitancy group in COVID-19 vaccine. Compared with those with low hesitancy, those with medium and high hesitancy have lower subjective knowledge level and objective knowledge level. The influence of subjective knowledge level on public vaccine hesitancy is significantly greater than that of objective knowledge. In addition, through multiple linear regression, the study found that the information channel had a significant impact on the public's subjective and objective knowledge. Receiving vaccine information from television, web pages, health professionals, health departments can promote subjective knowledge and objective knowledge, while receiving vaccine information from family and friends reduces subjective knowledge and objective knowledge. Considering the geographical location of the population in this study, the research results in this paper cannot be extended to the public in other countries. However, the method used in this paper is helpful for researchers to understand the hesitancy degree of COVID-19 vaccines in other places and its relationship with the public knowledge level of COVID-19 vaccines. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Unmanned aerial vehicles (UAVs) extend the traditional ground-based Internet of Things (IoT) into the air. UAV mobile edge computing (MEC) architectures have been proposed by integrating UAVs into MEC networks during the current novel coronavirus disease (COVID-19) era. UAV mobile edge computing (MEC) shares personal data with external parties (such as edge servers) during intelligent medical analytics. However, this technique raises privacy concerns about patients' health data. More recently, the concept of federal learning (FL) has been set up to protect mobile user data privacy. Compared to traditional machine learning, federated learning requires a decentralized distribution system to enhance trust for UAVs. Blockchain technology provides a secure and reliable solution for FL settings between multiple untrusted parties with anonymous, immutable, and distributed features. Therefore, blockchain-enabled FL provides both theories and techniques to improve the performance of intelligent UAV edge computing networks from various perspectives. This survey begins by discussing the current state of research on blockchain and FL. Then, compare the leading technologies and limitations. Second, we will discuss how to integrate blockchain and FL into UAV edge computing networks and the associated challenges and solutions. Finally, we discuss the fundamental research challenges and future directions.