Assessment of relationship between aging and contaminant-carryover for different filter layer of surgical mask under urban environmental stressors

Abundant disposable surgical masks (SMs) remain in the environment and continue to age under urban environmental stressors. This study aimed to investigate the aging characteristics of SMs and the effect of different aged layers of SMs on phenanthrene (PHE), tylosin (TYL), and sulfamethazine (SMT) under two different urban environmental stressors (UV and ozone). The results show that UV exposure causes more severe aging of the SM layers than ozone. The middle layer, made of melt-brown fabric, has displayed the highest degree of aging due to its smaller diameter and mechanical strength. The two-dimensional correlation spectroscopy (2D-COS) analysis reveals the different aging sequences of functional groups and three layers in aged SMs under the two urban environmental stressors. Whether the SMs are aged or not, the adsorptions of three organic pollutants on SMs are positively correlated with the octanol-water partition coefficient. Furthermore, except for the dominant hydrophobic interaction, aged SMs can promote the adsorption of three organic pollutants by accessory interactions (hydrogen bonding and partition), depending on their structures. These findings highlight the environmental effects of new microplastic (MP) sources and coexisting pollutants under the influence of COVID-19, which is helpful in accurately evaluating the biological toxicity of SMs. © 2022 Elsevier B.V.

Are categorical EPU indices predictable for carbon futures volatility? Evidence from the machine learning method

The purpose of this paper is to explore whether the categorical Economic Policy Uncertainty (EPU) indices are predictable for the volatility of carbon futures, in the mixed data sampling (MIDAS) regression framework. The prediction methods include the MIDAS-RV model, the MIDAS models extended by individual categorical EPU index, combination prediction approaches, the MIDAS models extended by dimensionality reduction techniques as well as the machine learning methods on the basis of MIDAS model and Markov regime switching method. We find firstly that categorical EPU indices are predictable for carbon futures volatility, but the predictive power of individual categorical EPU indices is not robust. Secondly, machine learning methods, especially the machine learning method considering the Markov regime switching structure, help to obtain valid information from multiple categorical EPU indices and produce robust and superior prediction accuracy for carbon futures volatility. The results of the extension analysis also found that machine learning methods, especially the machine learning method considering the Markov regime switching structure help to produce higher investment performance and more accurate long-term carbon futures volatility forecasts. Meanwhile, we also find the advantages of the MIDAS based machine learning methods over the traditional AR based machine learning methods. Finally, the forecasting performance of the machine learning method which considering Markov regime switching structure are superior during both the low and high volatility regimes and even during the COVID-19 pandemic. © 2022 Elsevier Inc.

Designing boarding limit strategy by considering stop-level fairness amid the COVID-19 outbreak

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.

Research on Prediction of Medical Training Effect Based on PSO-BP Neural Network

Today, the world is still suffering from Coronavirus disease 2019(COVID-19) and other disasters. Therefore, it is critical to improve medical emergency professional training, and ensuring the training effect has become the top priority. As a result, this paper builds a Particle Swarm Optimization Back Propagation(PSO-BP) neural network model using training data from the National Disaster Life Support(NDLS) course to predict NDLS training outcomes. The PSO algorithm is used to calculate the initial weights of the BP network, and the model is then trained using error back propagation to obtain the predicted value of the training effect. When compared to the standard BP neural network prediction results, experimental analysis shows that the prediction model's accuracy reaches 93.24 percentage, and the prediction accuracy is improved by 11.71 percentage. It is also better in terms of convergence speed, minimum error, global search ability, and learning smoothness. This approach is suitable for medical training effect prediction and additionally to assist the training providers in grasping trainees' learning effects in advance to improve training quality. © 2022 IEEE.

[Enlightment of routine vaccination under the prevention and control of COVID-19 based on the circulating event of type â ¢ vaccine-derived poliovirus in Shanghai].

Since the Global Polio Eradication Initiative was launched by the World Health Assembly in 1988, significant progress has been made in global polio prevention and control. But the occurrence of vaccine-associated paralytic poliomyelitis cases and vaccine-derived poliovirus related cases have become a major challenge during the post-polio era. While coronavirus disease 2019(COVID-19) has brought serious disease burden and economic burden to all countries in the world, prevention and control of vaccine-preventable infectious diseases such as polio should not be neglected under the background of the global common fight against COVID-19. Taking the type Ⅲ VDPV cycle event in Shanghai as an example, the paper discussed how to do a good job of routine inoculation under the prevention and control of COVID-19 to strictly prevent the outbreak of vaccine-preventable infectious diseases.

Childhood Bacille Calmette-Guerin Vaccination and Its Association With Less Severe COVID-19 Pneumonia.

INTRODUCTION: The potential for Bacille Calmette-Guerin vaccination to mitigate COVID-19 severity and perhaps infection susceptibility has been hypothesized, attracting global attention given its off-target benefits shown in several respiratory viral infections. METHODS: In this retrospective study, patients with laboratory-confirmed COVID-19 from Wuhan Pulmonary Hospital, China were categorized into Bacille Calmette-Guerin‒vaccinated and nonvaccinated groups. Clinical records, demography, laboratory results, and chest computed tomography scans were extracted from electronic medical records and compared between the 2 groups. RESULTS: No adverse events were observed, except for an increased frequency of chills in the Bacille Calmette-Guerin‒vaccinated group compared with that in the unvaccinated group (p=0.014). There were no significant differences in oxygen demand for breathing, computed tomography scans, treatments, or outcomes between the 2 groups. However, Bacille Calmette-Guerin‒vaccinated group had significantly less severe pneumonia (p=0.028) and milder deficiency in liver function, consistent with a lower death rate than in the unvaccinated group. CONCLUSIONS: Bacille Calmette-Guerin vaccination received in childhood is associated with less severe COVID-19 pneumonia and milder liver function deficiency in addition to a lower death rate in Bacille Calmette-Guerin‒vaccinated patients than in nonvaccinated individuals.

Analysis of 8 274 cases of novel coronavirus nucleic acid detection and co-infection in Wuhan

Objective: To investigate the positive rate for 2019-nCoV tests and co-infections in Wuhan district. Methods: A total of 8 274 cases in Wuhan were enrolled in this cross-sectional study during January 20 to February 9 in 2020, and were tested for 2019-nCoV using fluorescence quantitative PCR. Both respiratory tract samples (nasopharynx, oropharynx, sputum and alveolar lavage fluid) and non-respiratory tract samples (urine, feces, anal swabs, blood and conjunctival sac swabs) were collected. If both orf1ab and N genes are positive, they are classified as nucleic acid test positive group;if both orf1ab and N genes are negative, they are classified as negative group;if single gene target is positive, they are classified as suspicious group. Individuals were divided into male group and female group according to sex. At the same time, 316 patients were tested for 13 respiratory pathogens by multiplex PCR. Results: Among the 8 274 subjects, 2 745 (33.17%) were 2019-nCoV infected;5 277 (63.77%) subjects showed negative results in the 2019-nCoV nucleic acid test;and 252 cases (3.05%) was not definitive (inconclusive result). The age of cases with COVID-19 patients and inconclusive cases was significantly higher than that of cases without 2019-nCoV infection (56>40, t=27.569, P<0.001;52>40, t=6.774, P<0.001). The positive rate of 13 respiratory pathogens multiple tests was significantly lower in 104 subjects who were positive for 2019-nCoV compared with those in subjects who were negative for 2019-nCoV test (5.77% vs 18.39%, χ2=24.105, P=0.003). Four types of respiratory tract samples and five types of non-respiratory tract sampleswere found to be positive for 2019-nCoV nucleic acid test. Conclusion: The 2019-nCoV nucleic acid positive rate inmale is higher than infemale. Co-infections should be pay close attention in COVID-19 patients. 2019-nCoV nucleic acid can be detected in non-respiratory tract samples.

[Clinical features and outcome of treatment for novel coronavirus pneumonia: a meta-analysis].

Objective: To investigate the clinical features and outcome of treatment for novel coronavirus pneumonia. Methods: Literature on novel coronavirus pneumonia was retrieved from PubMed and EMBASE databases. The relevant data was extracted and a meta-analysis was performed using StatsDirect statistical software V.2.8.0 to calculate the combined odds ratio. Results: Seven studies were included, consisting of 1594 cases. The meta-analysis result showed that the most common clinical symptoms of the novel coronavirus pneumonia were fever (91.6%) and cough (64.5%), followed by dyspnea (32.8%) and sputum (28.1%). Headache (10.5%), sore throat (11.2%), hemoptysis (3.2%), diarrhea (6.6%) and the other symptoms were relatively rare. Aspartate aminotransferase (29%), alanine transaminase (22.7%), and total bilirubin (11.7%) levels were elevated, except for serum albumin levels (80.4%). The common therapeutic agents used were antibiotics (87.7%), antiviral drugs (75.5%), and glucocorticoids (26.6%), while antifungal agents (7.7%) were used in few. Mechanical ventilation (13.4%), extracorporeal membrane oxygenation (1.9%), and continuous renal replacement therapy (3.8%) were used in severe cases. The rate of mortality in hospital was 7.7%, respectively. Heterogeneity between studies was significant; however, subgroup and sensitivity analysis had failed to identify clear sources of heterogeneity. Conclusion: Fever, cough and liver dysfunction are the main clinical manifestations of this disease and the mortality rate is low.