To what extent the traffic restriction policies can improve its air quality? An inspiration from COVID-19.

In hazy days, several local authorities always implemented the strict traffic-restriction measures to improve the air quality. However, owing to lack of data, the quantitative relationships between them are still not clear. Coincidentally, traffic restriction measures during the COVID-19 pandemic provided an experimental setup for revealing such relationships. Hence, the changes in air quality in response to traffic restrictions during COVID-19 in Spain and United States was explored in this study. In contrast to pre-lockdown, the private traffic volume as well as public traffic during the lockdown period decreased within a range of 60-90%. The NO2 concentration decreased by approximately 50%, while O3 concentration increased by approximately 40%. Additionally, changes in air quality in response to traffic reduction were explored to reveal the contribution of transportation to air pollution. As the traffic volume decreased linearly, NO2 concentration decreased exponentially, whereas O3 concentration increased exponentially. Air pollutants did not change evidently until the traffic volume was reduced by less than 40%. The recovery process of the traffic volume and air pollutants during the post-lockdown period was also explored. The traffic volume was confirmed to return to background levels within four months, but air pollutants were found to recover randomly. This study highlights the exponential impact of traffic volume on air quality changes, which is of great significance to air pollution control in terms of traffic restriction policy. Supplementary Information: The online version contains supplementary material available at 10.1007/s00477-022-02351-7.

The levels and related factors of mental workload among nurses: A systematic review and meta-analysis.

AIM: The aim was to determine the overall levels and related factors of mental workload assessed using the NASA-TLX tool among nurses. BACKGROUND: Mental workload is a key element that affects nursing performance. However, there exists no review regarding mental workload assessed using the NASA-TLX tool, focusing on nurses. DESIGN: A systematic review and meta-analysis. DATA SOURCES: PubMed, MEDLINE, Web of Science, EMBASE, PsycINFO, Scopus, CINAHL, CNKI, CBM, Weipu and WanFang databases were searched from 1 January 1998 to 30 February 2022. REVIEW METHODS: Following the PRISMA statement recommendations, review methods resulted in 31 quantitative studies retained for inclusion which were evaluated with the evaluation criteria for observational studies as recommended by the Agency for Healthcare Research and Quality. The data were pooled and a random-effects meta-analysis conducted. RESULTS: Findings showed the pooled mental workload score was 65.24, and the pooled prevalence of high mental workload was 54%. Subgroup analysis indicated nurses in developing countries and emergency departments experienced higher mental workloads, and the mental workloads of front-line nurses increased significantly during the COVID-19 pandemic. CONCLUSION: These findings highlight that nurses experience high mental workloads as assessed using the NASA-TLX tool and there is an urgent need to explore interventions to decrease their mental workloads.

Anxiety, depression and post-traumatic stress disorder and related factors among Chinese population during the COVID-19 pandemic: A cross-sectional study.

The sudden outbreak of coronavirus disease 2019 (COVID-19) has deep and wide negative mental impacts on the public, and studies on the impact of COVID-19 on social and mental well-being are necessary. This study aimed to evaluate mental distress, including anxiety, depression, and post-traumatic stress disorder (PTSD), and its related risk factors in Chinese adults in the early stages of the COVID-19 pandemic. This study used a large-scale cross-sectional design. A total of 2067 adult participants completed the online survey via REDcap from 1st to 15th of March 2020 during the COVID-19 outbreak in China. Anxiety, depression, PTSD, and related risk factors, including self-efficacy, coping style, and social support, were measured using valid and reliable instruments. The data were analyzed using multiple linear regression. We found that 201 (9.7%) participants reported moderate-to-severe anxiety, 669 (33.8%) reported depression, and 368 (17.8%) reported symptoms of PTSD. Self-efficacy, coping style, and social support significantly affected anxiety, depression, and PTSD symptoms. Participants' sociodemographic characteristics, COVID-19 pandemic-related factors, low self-efficacy, low social support, and negative coping were predictors of mental distress during the COVID-19 pandemic. Our study will help healthcare professionals carry out early predictions and identification of high-risk groups and provide appropriate interventions to target groups during public health emergencies that plague the world.

Anxiety, depression and post-traumatic stress disorder and related factors among Chinese population during the COVID-19 pandemic: A cross-sectional study

The sudden outbreak of coronavirus disease 2019 (COVID-19) has deep and wide negative mental impacts on the public, and studies on the impact of COVID-19 on social and mental well-being are necessary. This study aimed to evaluate mental distress, including anxiety, depression, and post-traumatic stress disorder (PTSD), and its related risk factors in Chinese adults in the early stages of the COVID-19 pandemic. This study used a large-scale cross-sectional design. A total of 2067 adult participants completed the online survey via REDcap from 1st to 15th of March 2020 during the COVID-19 outbreak in China. Anxiety, depression, PTSD, and related risk factors, including self-efficacy, coping style, and social support, were measured using valid and reliable instruments. The data were analyzed using multiple linear regression. We found that 201 (9.7%) participants reported moderate-to-severe anxiety, 669 (33.8%) reported depression, and 368 (17.8%) reported symptoms of PTSD. Self-efficacy, coping style, and social support significantly affected anxiety, depression, and PTSD symptoms. Participants' sociodemographic characteristics, COVID-19 pandemic-related factors, low self-efficacy, low social support, and negative coping were predictors of mental distress during the COVID-19 pandemic. Our study will help healthcare professionals carry out early predictions and identification of high-risk groups and provide appropriate interventions to target groups during public health emergencies that plague the world.

A Machine Learning-Based Risk Prediction Model for Post-Traumatic Stress Disorder during the COVID-19 Pandemic.

Background and Objectives: The COVID-19 pandemic has caused global public panic, leading to severe mental illnesses, such as post-traumatic stress disorder (PTSD). This study aimed to establish a risk prediction model of PTSD based on a machine learning algorithm to provide a basis for the extensive assessment and prediction of the PTSD risk status in adults during a pandemic. Materials and Methods: Model indexes were screened based on the cognitive-phenomenological-transactional (CPT) theoretical model. During the study period (1 March to 15 March 2020), 2067 Chinese residents were recruited using Research Electronic Data Capture (REDCap). Socio-demographic characteristics, PTSD, depression, anxiety, social support, general self-efficacy, coping style, and other indicators were collected in order to establish a neural network model to predict and evaluate the risk of PTSD. Results: The research findings showed that 368 of the 2067 participants (17.8%) developed PTSD. The model correctly predicted 90.0% (262) of the outcomes. Receiver operating characteristic (ROC) curves and their associated area under the ROC curve (AUC) values suggested that the prediction model possessed an accurate discrimination ability. In addition, depression, anxiety, age, coping style, whether the participants had seen a doctor during the COVID-19 quarantine period, and self-efficacy were important indexes. Conclusions: The high prediction accuracy of the model, constructed based on a machine learning algorithm, indicates its applicability in screening the public mental health status during the COVID-19 pandemic quickly and effectively. This model could also predict and identify high-risk groups early to prevent the worsening of PTSD symptoms.

Single-Side Superhydrophobicity in Si3N4-Doped and SiO2-Treated Polypropylene Nonwoven Webs with Antibacterial Activity.

Meltblown (MB) nonwovens as air filter materials have played an important role in protecting people from microbe infection in the COVID-19 pandemic. As the pandemic enters the third year in this current global event, it becomes more and more beneficial to develop more functional MB nonwovens with special surface selectivity as well as antibacterial activities. In this article, an antibacterial polypropylene MB nonwoven doped with nano silicon nitride (Si3N4), one of ceramic materials, was developed. With the introduction of Si3N4, both the average diameter of the fibers and the pore diameter and porosity of the nonwovens can be tailored. Moreover, the nonwovens having a single-side moisture transportation, which would be more comfortable in use for respirators or masks, was designed by imparting a hydrophobicity gradient through the single-side superhydrophobic finishing of reactive organic/inorganic silicon coprecipitation in situ. After a nano/micro structural SiO2 precipitation on one side of the fabric surfaces, the contact angles were up to 161.7° from 141.0° originally. The nonwovens were evaluated on antibacterial activity, the result of which indicated that they had a high antibacterial activity when the dosage of Si3N4 was 0.6 wt%. The bacteriostatic rate against E. coli and S. aureus was up to over 96%. Due to the nontoxicity and excellent antibacterial activity of Si3N4, this MB nonwovens are promising as a high-efficiency air filter material, particularly during the pandemic.

Using a System Pharmacology Method to Search for the Potential Targets and Pathways of Yinqiaosan against COVID-19.

The first reported case of coronavirus disease 2019 (COVID-19) occurred in Wuhan, Hubei, China. Thereafter, it spread through China and worldwide in only a few months, reaching a pandemic level. It can cause severe respiratory illnesses such as pneumonia and lung failure. Since the onset of the disease, the rapid response and intervention of traditional Chinese medicine (TCM) have played a significant role in the effective control of the epidemic. Yinqiaosan (YQS) was used to treat COVID-19 pneumonia, with good curative effects. However, a systematic overview of its active compounds and the therapeutic mechanisms underlying its action has yet to be performed. The purpose of the current study is to explore the compounds and mechanism of YQS in treating COVID-19 pneumonia using system pharmacology. A system pharmacology method involving drug-likeness assessment, oral bioavailability forecasting, virtual docking, and network analysis was applied to estimate the active compounds, hub targets, and key pathways of YQS in the treatment of COVID-19 pneumonia. With this method, 117 active compounds were successfully identified in YQS, and 77 potential targets were obtained from the targets of 95 compounds and COVID-19 pneumonia. The results show that YQS may act in treating COVID-19 pneumonia and its complications (atherosclerosis and nephropathy) through Kaposi sarcoma-related herpesvirus infection and the AGE-RAGE signaling pathway in diabetic complications and pathways in cancer. We distinguished the hub molecular targets within pathways such as TNF, GAPDH, MAPK3, MAPK1, EGFR, CASP3, MAPK8, mTOR, IL-2, and MAPK14. Five of the more highly active compounds (acacetin, kaempferol, luteolin, naringenin, and quercetin) have anti-inflammatory and antioxidative properties. In summary, by introducing a systematic network pharmacology method, our research perfectly forecasts the active compounds, potential targets, and key pathways of YQS applied to COVID-19 and helps to comprehensively clarify its mechanism of action.

A systematic review and meta-analysis on the effectiveness of web-based psychosocial interventions among patients with colorectal cancer.

AIMS: To synthesize the effectiveness of web-based psychosocial interventions on self-efficacy, anxiety, depression, quality of life (QoL), non-specific psychological and cancer-specific distress among patients with colorectal cancer (CRC). DESIGN: A systematic review and meta-analysis. DATA SOURCES: Six databases (PubMed, PsycINFO, Embase, Scopus, CINAHL and CNKI) were searched from inception to December 2021. REVIEW METHODS: Experimental/quasi-experimental studies involving patients with CRC for the improvement of aforementioned outcomes were included. Two reviewers screened and extracted the data, and assessed studies' methodological quality using risk of bias tools. Meta-analyses and narrative syntheses were performed. RESULTS: Nineteen studies consisting of 1386 participants were identified. Cognitive-behavioural therapy delivered online was the most common trialled web-based psychosocial intervention. Meta-analyses revealed no positive effect for self-efficacy (standardized mean difference 0.93, 95% CI: 0.52 to 1.35, p < .01) and minimal benefit for QoL (mean difference [MD] 2.83, 95% CI: -0.31 to 5.98, p = .08) but significant positive effects for anxiety (MD -2.23, 95% CI: -3.31 to -1.14, p < .01) and depression (MD -2.84, 95% CI: -4.09 to -1.59, p < .01) among CRC survivors in the intervention group as compared with the control group. Narrative synthesis suggested possible benefits in reducing distress. CONCLUSION: Web-based psychosocial interventions are promising alternatives to conventional delivery methods in reducing patients' anxiety, depression and distress. However, evidence on self-efficacy and QoL remains inconsistent. More adequately powered, well-designed trials with targeted and theory-based interventions are required to ascertain findings. IMPACT: By highlighting the potential of web-based psychosocial interventions in reducing anxiety and depression among CRC survivors, this review has put forth beneficial information supporting the use and acceptance of web-based care delivery in light of COVID-19 restrictions and nationwide lockdowns. Meanwhile, the paucity of empirical support reflects the necessity of more extensive research to test and improve other health outcomes. PROSPERO registration number: CRD42021261396.

Measuring the impact of an exogenous factor: An exponential smoothing model of the response of shipping to COVID-19.

Following the outbreak of the COVID-19 pandemic, various lockdown strategies restrained global economic growth bringing a significant decline in maritime transportation. However, the previous studies have not adequately recognized the specific impacts of COVID-19 on maritime transportation. In this study, a series of analyses of the Baltic Dry Index (BDI), the China Coastal Bulk Freight Index (CCBFI) and of container throughputs with and without the impact of COVID-19 were carried out to assess changing trends in dry bulk and container transportation. The results show that global dry bulk transportation was largely affected by lockdown policies in the second month during COVID-19, and BDI presented a year-on-year decrease of approximately 35.5% from 2019 to 2020. The CCBFI showed an upward trend in the second month during COVID-19, one month ahead of the BDI. The container throughputs at Shanghai Port, the Ports of Hong Kong, the Ports of Singapore and the Ports of Los Angeles from 2019 to 2020 presented the largest year-on-year drops of approximately 19.6%, 7.1%, 10.6% and 30.9%, respectively. In addition, the authors developed exponential smoothing models of BDI, CCBFI, and container transportation, and calculated the percentage prediction error between the observed and predicted values to examine the impact of exogenous effects on the shipping industry due to the outbreak of COVID-19. The results are consistent with the conclusions obtained from the comparison of BDI, CCBFI, and container transportation during the same period in 2020 and 2019. Finally, on the basis of the findings, smart shipping and special support policies are proposed to reduce the negative impacts of COVID-19.

Spatiotemporal evolution of NO2 diffusion in Beijing in response to COVID-19 lockdown using complex network.

The COVID-19 pandemic and the corresponding lockdown measures have been confirmed to reduce the air pollution in major megacities worldwide. Especially at some monitoring hotspots, NO2 has been verified to show a significant decrease. However, the diffusion pattern of these hotspots in responding to COVID-19 is not clearly understood at present stage. Hence, we selected Beijing, a typical megacity with the strictest lockdown measures during COVID-19 period, as the studied city and attempted to discover the NO2 diffusion process through complex network method. The improved metrics derived from the topological structure of the network were adopted to describe the performance of diffusion. Primarily, we found evidences that COVID-19 had significant effects on the spatial diffusion distribution due to combined effect of changed human activities and meteorological conditions. Besides, to further quantify the impacts of disturbance caused by different lockdown measures, we discussed the evolutionary diffusion patterns from lockdown period to recovery period. The results displayed that the difference between normal operation and pandemic operation firstly increased at the cutoff of lockdown measures but then declined after the implement of recovery measures. The source areas had greater vulnerability and lower resilience than receptors areas. Furthermore, based on the conclusion that the diffusion pattern changed during different periods, we explored the key stations on the path of diffusion process to further gain information. These findings could provide references for comprehending spatiotemporal pattern on city scale, which might be help for high-resolution air pollution mapping and prediction.

A Theory-Based, Multidisciplinary Approach to Cocreate a Patient-Centric Digital Solution to Enhance Perioperative Health Outcomes Among Colorectal Cancer Patients and Their Family Caregivers: Development and Evaluation Study.

BACKGROUND: Elective colorectal cancer (CRC) surgeries offer enhanced surgical outcomes but demand high self-efficacy in prehabilitation and competency in self-care and disease management postsurgery. Conventional strategies to meet perioperative needs have not been pragmatic, and there remains a pressing need for novel technologies that could improve health outcomes. OBJECTIVE: The aim of this paper was to describe the development of a smartphone-based interactive CRC self-management enhancement psychosocial program (iCanManage) in order to improve health outcomes among patients who undergo elective CRC surgeries and their family caregivers. METHODS: A multidisciplinary international team comprising physicians, specialist nurses, a psychologist, software engineers, academic researchers, cancer survivors, patient ambassadors, and ostomy care medical equipment suppliers was formed to facilitate the development of this patient-centric digital solution. The process occurred in several stages: (1) review of current practice through clinic visits and on-site observations; (2) review of literature and findings from preliminary studies; (3) content development grounded in an underpinning theory; (4) integration of support services; and (5) optimizing user experience through improving interface aesthetics and customization. In our study, 5 participants with CRC performed preliminary assessments on the quality of the developed solution using the 20-item user version of the Mobile App Rating Scale (uMARS), which had good psychometric properties. RESULTS: Based on the collected uMARS data, the smartphone app was rated highly for functionality, aesthetics, information quality, and perceived impact, and moderately for engagement and subjective quality. Several limiting factors such as poor agility in the adoption of digital technology and low eHealth literacy were identified despite efforts to promote engagement and ensure ease of use of the mobile app. To overcome such barriers, additional app-training sessions, an instruction manual, and regular telephone calls will be incorporated into the iCanManage program during the trial period. CONCLUSIONS: This form of multidisciplinary collaboration is advantageous as it can potentially streamline existing care paths and allow the delivery of more holistic care to the CRC population during the perioperative period. Should the program be found to be effective and sustainable, hospitals adopting this digital solution may achieve better resource allocation and reduce overall health care costs in the long run. TRIAL REGISTRATION: ClinicalTrials.gov NCT04159363; https://clinicaltrials.gov/ct2/show/NCT04159363.

A Regional Industrial Economic Forecasting Model Based on a Deep Convolutional Neural Network and Big Data

To accurately predict the economic development of each industry in different types of regions, a deep convolutional neural network model was designed for predicting the annual GDP;GDP growth index;and primary, secondary and tertiary industry growth values of each. In the model, raw industrial data are preprocessed by a normalization operation and subsequently transformed by the BoxCox method to approach the normal distribution. Panel data of consecutive years are constructed and used as input to the deep convolutional neural network, and industrial data of year t + 1 are used as the output of the network. Simulation experiments were conducted to analyze 23 years of industrial economic data from 31 provinces, municipalities, and autonomous regions in China. The experimental results show that R-squared value is larger than 0.91 for all 31 provinces and root mean squared log errors (RMSLE) of all regions are less than 0.1, which demonstrate that the proposed method achieves high prediction accuracy with generalization capability and can accurately predict the economic growth trends of different types of regions.

An Ultrafast One-Step Quantitative Reverse Transcription-Polymerase Chain Reaction Assay for Detection of SARS-CoV-2.

We developed an ultrafast one-step RT-qPCR assay for SARS-CoV-2 detection, which can be completed in only 30 min on benchtop Bio-Rad CFX96. The assay significantly reduces the running time of conventional RT-qPCR: reduced RT step from 10 to 1 min, and reduced the PCR cycle of denaturation from 10 to 1 s and extension from 30 to 1 s. A cohort of 60 nasopharyngeal swab samples testing showed that the assay had a clinical sensitivity of 100% and a clinical specificity of 100%.

Characterizing the interruption-recovery patterns of urban air pollution under the COVID-19 lockdown in China.

The COVID-19 pandemic provides an opportunity to study the effects of urban lockdown policies on the variation in pollutant concentrations and to characterize the recovery patterns of urban air pollution under the interruption of COVID-19 lockdown policies. In this paper, interruption-recovery models and regression discontinuity design were developed to characterize air pollution interruption-recovery patterns and analyze environmental impacts of the COVID-19 lockdown, using air pollution data from four Chinese metropolises (i.e., Shanghai, Wuhan, Tianjin, and Guangzhou). The results revealed the air pollutant interruption-recovery curve represented by the three lockdown response periods (Level I, Level II and Level III) during COVID-19. The curve decreased during Level I (A 25.3%-48.8% drop in the concentration of NO2 has been observed in the four metropolises compared with the same period in 2018-2019.), then recovered around reopening, but decreased again during Level III. Moreover, the interruption-recovery curve of the year-on-year air pollution difference suggests a process of first decreasing during Level I and gradually recovering to a new equilibrium during Level III (e.g., the unit cumulative difference of NO2 mass concentrations in Shanghai was 21.7, 22.5, 11.3 (µg/m3) during Level I, II, and III and other metropolises shared similar results). Our findings reveal general trends in the air quality externality of different lockdown policies, hence could provide valuable insights into air pollutant interruption-recovery patterns and clear scientific guides for policymakers to estimate the effect of different lockdown policies on urban air quality.

Clinical outcomes of hemodialysis patients infected with severe acute respiratory syndrome coronavirus 2 and impact of proactive chest computed tomography scans.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is an emerging infectious disease that first manifested in humans in Wuhan, Hubei Province, China, in December 2019, and has subsequently spread worldwide. METHODS: We conducted a retrospective, single-center case series of the seven maintenance hemodialysis (HD) patients infected with COVID-19 at Zhongnan Hospital of Wuhan University from 13 January to 7 April 2020 and a proactive search of potential cases by chest computed tomography (CT) scans. RESULTS: Of 202 HD patients, 7 (3.5%) were diagnosed with COVID-19. Five were diagnosed by reverse transcription polymerase chain reaction (RT-PCR) because of compatible symptoms, while two were diagnosed by RT-PCR as a result of screening 197 HD patients without respiratory symptoms by chest CT. Thirteen of 197 patients had positive chest CT features and, of these, 2 (15%) were confirmed to have COVID-19. In COVID-19 patients, the most common features at admission were fatigue, fever and diarrhea [5/7 (71%) had all these]. Common laboratory features included lymphocytopenia [6/7 (86%)], elevated lactate dehydrogenase [3/4 (75%)], D-dimer [5/6 (83%)], high-sensitivity C-reactive protein [4/4 (100%)] and procalcitonin [5/5 (100%)]. Chest CT showed bilateral patchy shadows or ground-glass opacity in the lungs of all patients. Four of seven (57%) received oxygen therapy, one (14%) received noninvasive and invasive mechanical ventilation, five (71%) received antiviral and antibacterial drugs, three (43%) recieved glucocorticoid therapy and one (14%) received continuous renal replacement therapy. As the last follow-up, four of the seven patients (57%) had been discharged and three patients were dead. CONCLUSIONS: Chest CT may identify COVID-19 patients without clear symptoms, but the specificity is low. The mortality of COVID-19 patients on HD was high.

Impact of the COVID-19 lockdown on roadside traffic-related air pollution in Shanghai, China.

The outbreak of COVID-19 has significantly inhibited global economic growth and impacted the environment. Some evidence suggests that lockdown strategies have significantly reduced traffic-related air pollution (TRAP) in regions across the world. However, the impact of COVID-19 on TRAP on roadside is still not clearly understood. In this study, we assessed the influence of the COVID-19 lockdown on the levels of traffic-related air pollutants in Shanghai. The pollution data from two types of monitoring stations-roadside stations and non-roadside stations were compared and evaluated. The results show that NO2, PM2.5, PM10, and SO2 had reduced by ~30-40% at each station during the COVID-19 pandemic in contrast to 2018-2019. CO showed a moderate decline of 28.8% at roadside stations and 16.4% at non-roadside stations. In contrast, O3 concentrations increased by 30.2% at roadside stations and 5.7% at non-roadside stations. This result could be resulted from the declined NOx emissions from vehicles, which lowered O3 titration. Full lockdown measures resulted in the highest reduction of primary pollutants by 34-48% in roadside stations and 18-50% in non-roadside stations. The increase in O3 levels was also the most significant during full lockdown by 64% in roadside stations and 33% in non-roadside stations due to the largest decrease in NO2 precursors, which promote O3 formation. Additionally, Spearman's rank correlation coefficients between NO2 and other pollutants significantly decreased, while the values between NO2 and O3 increased at roadside stations.

The Efficacy of WeChat-Based Parenting Training on the Psychological Well-being of Mothers With Children With Autism During the COVID-19 Pandemic: Quasi-Experimental Study.

BACKGROUND: During the COVID-19 pandemic, special education schools for children in most areas of China were closed between the end of January and the beginning of June in 2020. The sudden interruption in schooling and the pandemic itself caused parents to be anxious and even to panic. Mobile-based parenting skills education has been demonstrated to be an effective method for improving the psychological well-being of mothers with children with autism. However, whether it can improve the psychological states of mothers in the context of the COVID-19 pandemic is a subject that should be urgently investigated. OBJECTIVE: The aim of this study is to evaluate the efficacy of WeChat-based parenting training on anxiety, depression, parenting stress, and hope in mothers with children with autism, as well as the feasibility of the program during the COVID-19 pandemic. METHODS: This was a quasi-experimental trial. A total of 125 mothers with preschool children with autism were recruited in January 2020. The participants were assigned to the control group (n=60), in which they received routine care, or the intervention group (n=65), in which they received the 12-week WeChat-based parenting training plus routine care, according to their preferences. Anxiety, depression, parenting stress, hope, satisfaction, and adherence to the intervention were measured at three timepoints: baseline (T0), postintervention (T1), and a 20-week follow-up (T2). RESULTS: In total, 109 mothers completed the T1 assessment and 104 mothers completed the T2 assessment. The results of the linear mixed model analysis showed statistically significant group × time interaction effects for the intervention on anxiety (F=14.219, P<.001), depression (F=26.563, P<.001), parenting stress (F=68.572, P<.001), and hope (F=197.608, P<.001). Of all mothers in the intervention group, 90.4% (48.8/54) reported that they were extremely satisfied with the WeChat-based parenting training. In total, 40.0% (26/65) logged their progress in home training each week and 61.5% (40/65) logged their progress more than 80% of the time for all 20 weeks. CONCLUSIONS: The WeChat-based parenting training is acceptable and appears to be an effective approach for reducing anxiety, depression, and parenting stress, as well as increasing hope in mothers with children with autism during the global COVID-19 pandemic. Future studies with rigorous designs and longer follow-up periods are needed to further detect the effectiveness of the WeChat-based parenting training. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR2000031772; http://www.chictr.org.cn/showproj.aspx?proj=52165.

Significant concurrent decrease in PM2.5 and NO2 concentrations in China during COVID-19 epidemic.

The strict control measures and social lockdowns initiated to combat COVID-19 epidemic have had a notable impact on air pollutant concentrations. According to observation data obtained from the China National Environmental Monitoring Center, compared to levels in 2019, the average concentration of NO2 in early 2020 during COVID-19 epidemic has decreased by 53%, 50%, and 30% in Wuhan city, Hubei Province (Wuhan excluded), and China (Hubei excluded), respectively. Simultaneously, PM2.5 concentration has decreased by 35%, 29%, and 19% in Wuhan, Hubei (Wuhan excluded), and China (Hubei excluded), respectively. Less significant declines have also been found for SO2 and CO concentrations. We also analyzed the temporal variation and spatial distribution of air pollutant concentrations in China during COVID-19 epidemic. The decreases in PM2.5 and NO2 concentrations showed relatively consistent temporal variation and spatial distribution. These results support control of NOx to further reduce PM2.5 pollution in China. The concurrent decrease in NOx and PM2.5 concentrations resulted in an increase of O3 concentrations across China during COVID-19 epidemic, indicating that coordinated control of other pollutants is needed.

Experience of comprehensive interventions in reducing occupational exposure to COVID-19.

IMPORTANCE: The infection of medical personnel with COVID-19 was a disaster for both patients and doctors. However, some effective measures can prevent medical staff from becoming infected. This article introduces those measures and thus provides a reference for other hospitals. OBJECTIVE: In order to reduce the risk of occupational exposure and of the infection of medical staff, this article analyzed the factors, causes and experience of medical personnel on their occupational exposure to COVID-19. Some effective and targeted intervention measures can be implemented in order to avoid the occupational exposure of medical staff to COVID-19. EVIDENCE REVIEW: In this single-center case series involving 196 medical personnel, occupational exposure to COVID-19 was present. Nursing staff accounted for 67.35% of those cases. The relationships with an exposure source were found to be as follows: doctors and patients (87.24%), colleagues (10.20%), and roommates (2.55%). Occupational exposure was found to be present in the clinical department, radiology department, central sterile supply department, as well as in the outpatient clinics and operating rooms. The non-surgical departments accounted for 72.96% and direct contact accounted for 84.69% while failure to wear surgical masks (84.18%) and operating on the patient without wearing goggles/face shield (8.16%) were the main causes of occupational exposure. The occurrence of occupational exposure to COVID-19 declined to 0.19% after an extensive and comprehensive intervention program. CONCLUSIONS AND RELEVANCE: Some effective measures such as hand hygiene, wearing surgical masks in and around the hospital, reasonable use of goggles/face screens, raising awareness of protective measures, minimizing the number of elective operations, strengthening training as well as many other control measures were instrumental in reducing occupational exposure. For any medical institution there is room for improvement in terms of personal protection to reduce occupational exposure.