Acoustic environments of intensive care units during the COVID-19 pandemic.

This study aims to investigate the typical noise levels and noise sources in an intensive care unit (ICU) during the COVID-19 pandemic. Acoustic experiments were conducted over 24 hrs in patient wards and at nurse stations in four Chinese hospitals. From the measurements, noise levels and sources were analysed in terms of the A-weighted equivalent sound pressure levels (L Aeq) and A-weighted maximum Fast time-weighted sound pressure levels (L AFmax) over three different time periods during the day (i.e. day, evening and night). Overall, noise levels (L Aeq) for 24 hrs in all hospitals exceeded the World Health Organisation's (WHO) guide levels, varying from 51.1 to 60.3 dBA. The highest maximum noise level reached 104.2 dBA. The single-bedded wards (side rooms) were quieter than multi-bedded wards, and night time noise levels were quieter than daytime and evening across all hospitals. It was observed that the most dominant noise sources were talking/voices, door-closing, footsteps, and general activities (e.g. noise from cleaning equipment and cutlery sound). Footsteps became an unexpected dominant noise source during the pandemic because of the staff's disposable shoe covers which made footsteps noisier. Patient alarms and coughing varied significantly between patients. Talking/voices produced the highest maximum median values of the sound exposure level (SEL) and the maximum noise level at all sites. Noise levels in all the patient rooms were more than the WHO guidelines. The pandemic control guidelines had little impact on the noise levels in the ICUs.

Spartan Face Mask Detection and Facial Recognition System.

According to the World Health Organization (WHO), wearing a face mask is one of the most effective protections from airborne infectious diseases such as COVID-19. Since the spread of COVID-19, infected countries have been enforcing strict mask regulation for indoor businesses and public spaces. While wearing a mask is a requirement, the position and type of the mask should also be considered in order to increase the effectiveness of face masks, especially at specific public locations. However, this makes it difficult for conventional facial recognition technology to identify individuals for security checks. To solve this problem, the Spartan Face Detection and Facial Recognition System with stacking ensemble deep learning algorithms is proposed to cover four major issues: Mask Detection, Mask Type Classification, Mask Position Classification and Identity Recognition. CNN, AlexNet, VGG16, and Facial Recognition Pipeline with FaceNet are the Deep Learning algorithms used to classify the features in each scenario. This system is powered by five components including training platform, server, supporting frameworks, hardware, and user interface. Complete unit tests, use cases, and results analytics are used to evaluate and monitor the performance of the system. The system provides cost-efficient face detection and facial recognition with masks solutions for enterprises and schools that can be easily applied on edge-devices.

Nervous system diseases are associated with the severity and mortality of patients with COVID-19: a systematic review and meta-analysis.

Coronavirus disease 2019 (COVID-19) has become a global pandemic. Previous studies showed that comorbidities in patients with COVID-19 are risk factors for adverse outcomes. This study aimed to clarify the association between nervous system diseases and severity or mortality in patients with COVID-19. We performed a systematic literature search of four electronic databases and included studies reporting the prevalence of nervous system diseases in COVID-19 patients with severe and non-severe disease or among survivors and non-survivors. The included studies were pooled into a meta-analysis to calculate the odds ratio (OR) with 95% confidence intervals (95%CI). We included 69 studies involving 17 879 patients. The nervous system diseases were associated with COVID-19 severity (OR = 3.19, 95%CI: 2.37 to 4.30, P < 0.001) and mortality (OR = 3.75, 95%CI: 2.68 to 5.25, P < 0.001). Specifically, compared with the patients without cerebrovascular disease, patients with cerebrovascular disease infected with COVID-19 had a higher risk of severity (OR = 3.10, 95%CI: 2.21 to 4.36, P < 0.001) and mortality (OR = 3.45, 95% CI: 2.46 to 4.84, P < 0.001). Stroke was associated with severe COVID-19 disease (OR = 1.95, 95%CI: 1.11 to 3.42, P = 0.020). No significant differences were found for the prevalence of epilepsy (OR = 1.00, 95%CI: 0.42 to 2.35, P = 0.994) and dementia (OR = 2.39, 95%CI: 0.55 to 10.48, P = 0.247) between non-severe and severe COVID-19 patients. There was no significant association between stroke (OR = 1.79, 95%CI: 0.76 to 4.23, P = 0.185), epilepsy (OR = 2.08, 95%CI: 0.08 to 50.91, P = 0.654) and COVID-19 mortality. In conclusion, nervous system diseases and cerebrovascular disease were associated with severity and mortality of patients with COVID-19. There might be confounding factors that influence the relationship between nervous system diseases and COVID-19 severity as well as mortality.

COVID-19 vaccine research focusses on safety, efficacy, immunoinformatics, and vaccine production and delivery: a bibliometric analysis based on VOSviewer.

Coronavirus Disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has affected tens of millions of people globally since it was declared a pandemic by the World Health Organization (WHO) on March 11, 2020. There is an urgent need for safe and effective preventive vaccines to curb this pandemic. A growing amount of related research has been published. This study aimed to provide the current status of COVID-19 vaccine using bibliometric analysis. We searched Embase.com and MEDLINE comprehensively and included articles, articles in press, reviews, short surveys, conference abstracts and conference papers about COVID-19 vaccine. VOSviewer1.6.11 (Leiden University, Leiden, Netherlands) was applied to perform the bibliometric analysis of these papers. A total of 1,312 papers were finally included. The BMJ has been the most popular journal in this field. The United States maintained a top position worldwide and has provided a pivotal influence, followed by China, India and United Kingdom. Among all the institutions, Harvard University was regarded as a leader for research collaboration. We analyzed the keywords and identified seven COVID-19 vaccine research hotspot clusters. COVID-19 vaccine research hotspots focus on clinical trials on vaccine safety and efficacy, research on vaccine immunology and immunoinformatics, and vaccine hesitancy. Our analysis results demonstrated that cooperation between countries, institutions, and authors were insufficient. The results suggested that clinical trials on vaccine safety, efficacy, immunology, immunoinformatics, production and delivery are research hotspots. Furthermore, we can predict that there will be a lot of research focusing on vaccine adverse reactions.

Prevalence of delirium, depression, anxiety, and post-traumatic stress disorder among COVID-19 patients: protocol for a living systematic review.

BACKGROUND: Previous studies on the impact of corona virus disease 2019 (COVID-19) on the mental health of the patients has been limited by the lack of relevant data. With the rapid and sustained growth of the publications on COVID-19 research, we will perform a living systematic review (LSR) to provide comprehensive and continuously updated data to explore the prevalence of delirium, depression, anxiety, and post-traumatic stress disorder (PTSD) among COVID-19 patients. METHODS: We will perform a comprehensive search of the following databases: Cochrane Library, PubMed, Web of Science, EMBASE, and Chinese Biomedicine Literature to identify relevant studies. We will include peer-reviewed cross-sectional studies published in English and Chinese. Two reviewers will independently assess the methodological quality of included studies using the Joanna Briggs Institute Prevalence Critical Appraisal tool and perform data extraction. In the absence of clinical heterogeneity, the prevalence estimates with a 95% confidence interval (CI) of delirium, depression, anxiety, and post-traumatic stress disorder (PTSD) will be calculated by using random-effects model to minimize the effect of between-study heterogeneity separately. The literature searches will be updated every 3 months. We will perform meta-analysis if any new eligible studies or data are obtained. We will resubmit an updated review when there were relevant changes in the results, i.e., when outcomes became statistically significant (or not statistically significant anymore) or when heterogeneity became substantial (or not substantial anymore). DISCUSSION: This LSR will provide an in-depth and up-to-date summary of whether the common neuropsychiatric conditions observed in patients hospitalized for severe acute respiratory syndrome (SARS-CoV) and Middle East respiratory syndrome (MERS) are also prevalent in a different stage of COVID-19 patients. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42020196610.