Acceptance of a computer vision facilitated protocol to measure adherence to face mask use: a single-site, observational cohort study among hospital staff.

OBJECTIVES: Mask adherence continues to be a critical public health measure to prevent transmission of aerosol pathogens, such as SARS-CoV-2. We aimed to develop and deploy a computer vision algorithm to provide real-time feedback of mask wearing among staff in a hospital. DESIGN: Single-site, observational cohort study. SETTING: An urban, academic hospital in Boston, Massachusetts, USA. PARTICIPANTS: We enrolled adult hospital staff entering the hospital at a key ingress point. INTERVENTIONS: Consenting participants entering the hospital were invited to experience the computer vision mask detection system. Key aspects of the detection algorithm and feedback were described to participants, who then completed a quantitative assessment to understand their perceptions and acceptance of interacting with the system to detect their mask adherence. OUTCOME MEASURES: Primary outcomes were willingness to interact with the mask system, and the degree of comfort participants felt in interacting with a public facing computer vision mask algorithm. RESULTS: One hundred and eleven participants with mean age 40 (SD15.5) were enrolled in the study. Males (47.7%) and females (52.3%) were equally represented, and the majority identified as white (N=54, 49%). Most participants (N=97, 87.3%) reported acceptance of the system and most participants (N=84, 75.7%) were accepting of deployment of the system to reinforce mask adherence in public places. One third of participants (N=36) felt that a public facing computer vision system would be an intrusion into personal privacy.Public-facing computer vision software to detect and provide feedback around mask adherence may be acceptable in the hospital setting. Similar systems may be considered for deployment in locations where mask adherence is important.

A new PASS approach for tourism policymaking against COVID-19: learning from China’s experiences at early stages of the pandemic

The COVID-19 pandemic has caused unprecedented and far-reaching impacts on the tourism industry worldwide. To fight against COVID-19 while maintaining economic development, China has adopted its unique policymaking system and made certain achievements in preventing the pandemic and promoting the recovery of domestic tourism, which may have reference values to other countries. By collecting the national-level tourism policies on COVID-19 in China and analyzing the connections between the keywords of these policies, this study conceptually proposes a new PASS (P: Pause-promote;A: Avoid-alternate;S: Supervise-stabilize;S: Support-sustain) approach for comprehensive and seamless policymaking for the public health crisis management in the tourism context. Even though the new PASS is derived based on China’s pandemic policymaking experiences, its general values are well supported by the practices in other countries. Policy recommendations from perspectives of key stakeholders are summarized. Policymaking challenges are finally discussed. © 2022 Asia Pacific Tourism Association.

Birds of a Feather Flock Together: How Set Bias Helps to Deanonymize You via Revealed Intersection Sizes

Secure two-party protocols that compute intersection-related statistics have attracted much attention from the industry. These protocols enable two organizations to jointly compute a function (e.g., count and sum) over the intersection of their sets without explicitly revealing this intersection. However, most of such protocols will reveal the intersection size of the two sets in the end. In this work, we are interested in how well an attacker can leverage the revealed intersection sizes to infer some elements' membership of one organization's set. Even disclosing an element's membership of one organization's set to the other organization may violate privacy regulations (e.g., GDPR) since such an element is usually used to identify a person between two organizations. We are the first to study this set membership leakage in intersection-size-revealing protocols. We propose two attacks, namely, baseline attack and feature-aware attack, to evaluate this leakage in realistic scenarios. In particular, our feature-aware attack exploits the realistic set bias that elements with specific features are more likely to be the members of one organization's set. The results show that our two attacks can infer 2.0 similar to 72.7 set members on average in three realistic scenarios. If the set bias is not weak, the feature-aware attack will outperform the baseline one. For example, in COVID-19 contact tracing, the feature-aware attack can find 25.9 tokens of infected patients in 135 protocol invocations, 1.5 x more than the baseline attack. We discuss how such results may cause negative real-world impacts and propose possible defenses against our attacks.

AI-aided on-chip nucleic acid assay for smart diagnosis of infectious disease

Global pandemics such as COVID-19 have resulted in significant global social and economic disruption. Although polymerase chain reaction (PCR) is recommended as the standard test for identifying the SARS-CoV-2, conventional assays are time-consuming. In parallel, although artificial intelligence (AI) has been employed to contain the disease, the implementation of AI in PCR analytics, which may enhance the cognition of diagnostics, is quite rare. The information that the amplification curve reveals can reflect the dynamics of reactions. Here, we present a novel AI-aided on-chip approach by integrating deep learning with microfluidic paper-based analytical devices (mu PADs) to detect synthetic RNA templates of the SARS-CoV-2 ORFlab gene. The mu PADs feature a multilayer structure by which the devices are compatible with conventional PCR instruments. During analysis, real-time PCR data were synchronously fed to three unsupervised learning models with deep neural networks, including RNN, LSTM, and GRU. Of these, the GRU is found to be most effective and accurate. Based on the experimentally obtained datasets, qualitative forecasting can be made as early as 13 cycles, which significantly enhances the efficiency of the PCR tests by 67.5% (similar to 40 min). Also, an accurate prediction of the end-point value of PCR curves can be obtained by GRU around 20 cycles. To further improve PCR testing efficiency, we also propose AI-aided dynamic evaluation criteria for determining critical cycle numbers, which enables real-time quantitative analysis of PCR tests. The presented approach is the first to integrate AI for on-chip PCR data analysis. It is capable of forecasting the final output and the trend of qPCR in addition to the conventional end-point Cq calculation. It is also capable of fully exploring the dynamics and intrinsic features of each reaction. This work leverages methodologies from diverse disciplines to provide perspectives and insights beyond the scope of a single scientific field. It is universally applicable and can be extended to multiple areas of fundamental research.

Is Zoom viable for sociophonetic research? A comparison of in-person and online recordings for vocalic analysis

In this study, we explore whether Zoom is a viable method for collecting data for sociophonetic research, focusing on vocalic analysis. We investigate whether recordings collected through Zoom yield different acoustic measurements than recordings collected through in-person recording equipment, for the exact same speech. We analyze vowel formant data from 18 speakers who recorded Zoom conversations at the same time as they recorded themselves with portable recording equipment. We find that, overall, Zoom recordings yield lower raw F1 values and higher F2 values than recording equipment. We also tested whether normalization affects discrepancies between recording methods and found that while discrepancies still appear after normalizing with the Watt and Fabricius modified method, Lobanov normalization largely minimizes discrepancies between recording methods. Discrepancies are also mitigated with a Zoom recording setup that involves the speaker wearing headphones and recording with an external microphone.

Deep Learning-Based COVID-19 Twitter Analysis

Since the outbreak of COVID-19, the pandemic has impacted billions of people's lives around the world. Social media, such as Twitter, has been one of the major platforms where people express their emotions and thoughts about the unprecedented pandemic. In this paper, we perform Twitter sentiment analysis to gain insights into the development of Twitter users' sentiments during the period from February 1 to December 31, 2020. We use Long Short-term Memory (LSTM), a deep learning-based Natural Language Processing (NLP) method, to detect multiple sentiments out of eleven kinds. We also picked a number of topics of interest, such as social justice, mental health, vaccines, and misinformation, and conducted theme-specific sentiment analysis. In order to delve deeper into the meaning behind the sentiment trends, we used the Latent Dirichlet Allocation (LDA) algorithm to perform theme-specific topic modeling, which reveals interesting results. © 2021 ACM.

Laryngoscope8: Laryngeal image dataset and classification of laryngeal disease based on attention mechanism

Laryngeal disease is a common disease worldwide. However, currently there are no public laryngeal image datasets, which hinders the development of automatic classification of laryngeal disease. In this work, we build a new laryngeal image dataset called Laryngoscope8, which comprises 3057 images of 1950 unique individuals, and the images have been labeled with one of eight labels (including seven pathological labels and one normal label) by professional otolaryngologists. We also propose a laryngeal disease classification method, which uses attention mechanism to obtain the critical area under the supervision of image labels for laryngeal disease classification. That is, we first train a CNN model to classify the laryngeal images. If the classification result is correct, the region with strong response is most likely a critical area. The regions with strong responses are used as training data to train an object localization model that can automatically locate the critical area. Given an image for classification, the trained object localization model is employed to locate the critical area. Then, the located critical area is employed for image classification. The entire process only requires image-level labels and does not require manual labeling of the critical area. Experiment results show that the proposed method achieves promising performance in laryngeal disease classification. (C) 2021 Elsevier B.V. All rights reserved.

Reliability and validity of the Chinese version of fear of coronavirus disease 2019 scale

Objective To translate the English version of fear of coronavirus disease 2019 (COVID-19) scale (FCV-19S) into Chinese and assess its reliability and validity. Methods FCV-19S was translated and culture-adapted to form a Chinese version of fear of COVID-19 scale (FCV-19S-C). A total of 334 questionnaires were sent out online, including FCV-19S-C, Chinese version of depression, anxiety and stress scale (DASS-C21), impact of event scale-revised (IES-R) and public stigma scale. The reliability and validity of FCV-19S-C and the influencing factors of COVID-19 fear were analyzed with the survey data. Results FCV-19S-C contained 7 items. One common factor was extracted by exploratory factor analysis, reflecting that all items in the scale belonged to the common factor, which could explain 69.5% of total variation. The load of item factors ranged from 0.780 to 0.873, showing good construct validity. The total score of FCV-19S-C was positively correlated with the total scores of DASS-C21 and IES-R (r＝0.403 and 0.471, both P＜0.01), indicating that the scale had good concurrent validity. The Cronbach’s α coefficient of FCV-19S-C was 0.924, showing good reliability. Linear regression analysis showed that the influences of COVID-19 on the psychological level and family income could predict the total score of FCV-19S-C (β＝0.62 and 0.20, both P＜0.01). The total score of FCV-19S-C could predict the total score of the public stigma scale (β＝0.37, P＜0.01). Conclusion FCV-19S-C has good reliability and validity, and can be used as a tool to understand the public fear of COVID-19 in China. The fear of COVID-19 is related to the loss of family income and the public stigma of COVID-19 patients.

A retrospective study of the clinical characteristics of COVID-19 patients with gastrointestinal symptoms

Objective: To investigate the gastrointestinal (GI) manifestations of COVID-19 and retrospectively analyze the clinical characteristics of COVID-19 patients with GI symptoms. Methods: Data of 137 COVID-19 inpatients treated in Renmin Hospital of Wuhan University from February 1 to February 29, 2020 were collected. Patients were divided into GI group and Non-GI group according to the presence of digestive system abnormalities and gastrointestinal symptoms before and during admission. General data, clinical data, and relevant laboratory examination results of 137 patients were collected. SPSS 23.0 software was used for statistical analysis to compare the differences of various indicators between the two groups. Results: There was no statistically significant difference between the clinical data and the clinical manifestations of fever and dry cough between the GI group and the Non-GI group (P>0.05). The proportion of patients with fatigue in the GI group was higher than that in the Non-GI group (P<0.001). The proportion of critically ill patients was greater than that of the Non-GI group (P=0.011), and the proportion of GI combined with hypertension, diabetes, cardiovascular disease and chronic liver disease was higher than that of the Non-GI group (all P<0.05). GI group mortality rate was much higher than that of Non-GI group (P<0.001). Patients with GI group had higher white blood cell count and neutrophil count than Non-GI (P<0.001). The proportion of neutrophils, lymphocytes as well as lymph between the two groups had no statistical difference (P>0.05). The proportion of monocytes in the GI group was lower than that in the Non-GI group (P=0.033). There was no statistical difference in platelet count and C-reactive protein level between the two groups (P>0.05). LDH, TBIL, and Urea levels of GI group were higher than those of Non-GI group (P<0.05). There was no statistical difference in other cardiac, liver and kidney function tests, PT and APTT values between the two groups (P>0.05), but D-dimer in GI group was higher than in Non-GI group (P<0.001). Conclusion: Gastrointestinal symptoms are common in COVID-19 patients, and patients with other underlying diseases are at greater risk for developing gastrointestinal symptoms. COVID-19 patients with gastrointestinal symptoms progress more rapidly, have a higher mortality rate, and exhibit certain concomitant symptoms and laboratory tests that are specific to COVID-19. Therefore, more attention should be paid to the digestive system abnormalities and gastrointestinal symptoms in COVID-19 patients during clinical work. © 2021, Editorial Board of Medical Journal of Wuhan University. All right reserved.

Clinical characteristics of patients with COVID-19 combined with cardiovascular disease

Objective: To retrospectively analyze the clinical characteristics of coronavirus disease 2019 (COVID-19) and the impact of cardiovascular disease (CVD) on the clinical manifestations of COVID-19. Methods: A total of 128 patients diagnosed with COVID-19 in Renmin Hospital of Wuhan University from February 1 to February 29, 2020 were divided into CVD group (n=62) and non-CVD group (n=66). The general data, admission symptoms and laboratory examination results including blood routine, immunity, heart, liver and kidney function were obtained and statistically analyzed by SPSS 22.0 statistical software. The differences of various indexes between CVD group and non-CVD group were compared. Results: There was no significant difference in gender between CVD group and non-CVD group(P>0.05).The average age of CVD group was higher than that of non-CVD group (P<0.001). The proportion of fever and cough, severe and critical patients was higher than that respectively of non-CVD group (all P<0.05). There was no significant difference in the incidence of fatigue, dyspnea, and asymptomatic between the two groups (all P>0.05). The average levels of eukocyte count, neutrophil ratio, neutrophil count, monocyte count, and C-reactive protein in CVD group were higher than those in non-CVD group (all P<0.05), while the average lymphocyte proportion in CVD group was lower than that in non-CVD group (P<0.05). There was no significant difference in lymphocyte count and platelet count between the two groups (both P>0.05). The average LDH, myohemoglobin, CK-MB, NT-proBNP, TBIL, and Urea in CVD group were higher than those in non-CVD group (all P<0.05), but there was no significant difference in ALT, AST, and Cr between the two groups (all P>0.05). Conclusion: Compared with non-CVD patients with COVID-19, CVD patients with COVID-19 are older, have more obvious symptoms, with a higher risksin heart, liver, and kidney injury, but the mechanism is not clear yet. © 2021, Editorial Board of Medical Journal of Wuhan University. All right reserved.

Psychiatric comorbidities of epilepsy and COVID-19

Objective: To evaluate the status and influencing factors of psychiatric comorbidities of patients with epilepsy (PWEs) in Hubei province during the outbreak of COVID-19. Methods: From February 23, 2020 to March 5, 2020, a network questionnaire survey (including demographic characteristics, seizures, Generalized Anxiety Disorder Scale-7 score, Patient Health Questionnaire-9 score, Insomnia Severity Index score) was conducted among 570 PWEs who visited the Epilepsy Center of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology during April 1, 2019 and January 20, 2020. SPSS 22.0 software was used for correlation analysis of sociodemographic characteristics, epilepsy related factors, perceived threat to the COVID-19 and psychiatric comorbidity (depression, anxiety and insomnia) of PWEs during the COVID-19 epidemic. Results: A total of 362 valid questionnaires were included for analysis (the response rate was 63.51%,362/570). Thirty-four (9.4%), forty-seven (13.0%) and seventy-one (19.6%) patients suffered from anxiety, depression and insomnia, respectively. Patients with seizure frequency ≥2 times/month before the epidemic (OR=3.395,95%CI 1.561-7.384, P=0.002), poor subjective quality of life during the epidemic (OR=10.753,95%CI 1.938-59.654, P=0.024), and moderate to severe worry about bad impact of the epidemic on epilepsy (OR=3.077, 95%CI 1.382-6.853, P=0.006) were more likely to be anxious. Patients with poor subjective quality of life during the epidemic (OR=6.188, 95%CI 1.317-29.079, P=0.021) were more likely to be depressed. Patients with COVID-19 related symptoms (OR=3.609, 95%CI 1.674-7.778, P=0.001), children (OR=3.090, 95%CI 1.759-5.431, P<0.001), seizure frequency ≥2 times/month before the epidemic (OR=1.907, 95%CI 1.017-3.575, P=0.044), and moderate to severe worry about unanticipated seizures (OR=2.555, 95%CI 1.370-4.764, P=0.003) were more likely to suffer from insomnia. Conclusions: During the COVID-19 epidemic, parts of PWEs suffered from anxiety, depression and insomnia. PWEs with poor subjective quality of life, high frequency of epileptic seizures before the epidemic, excessive worry about bad impact of the epidemic on epilepsy and excessive worry about unanticipated seizures were prone to anxiety, depression and insomnia.

Informing the public health response to COVID-19: a systematic review of risk factors for disease, severity, and mortality.

BACKGROUND: Severe Acute Respiratory Syndrome coronavirus-2 (SARS-CoV-2) has challenged public health agencies globally. In order to effectively target government responses, it is critical to identify the individuals most at risk of coronavirus disease-19 (COVID-19), developing severe clinical signs, and mortality. We undertook a systematic review of the literature to present the current status of scientific knowledge in these areas and describe the need for unified global approaches, moving forwards, as well as lessons learnt for future pandemics. METHODS: Medline, Embase and Global Health were searched to the end of April 2020, as well as the Web of Science. Search terms were specific to the SARS-CoV-2 virus and COVID-19. Comparative studies of risk factors from any setting, population group and in any language were included. Titles, abstracts and full texts were screened by two reviewers and extracted in duplicate into a standardised form. Data were extracted on risk factors for COVID-19 disease, severe disease, or death and were narratively and descriptively synthesised. RESULTS: One thousand two hundred and thirty-eight papers were identified post-deduplication. Thirty-three met our inclusion criteria, of which 26 were from China. Six assessed the risk of contracting the disease, 20 the risk of having severe disease and ten the risk of dying. Age, gender and co-morbidities were commonly assessed as risk factors. The weight of evidence showed increasing age to be associated with severe disease and mortality, and general comorbidities with mortality. Only seven studies presented multivariable analyses and power was generally limited. A wide range of definitions were used for disease severity. CONCLUSIONS: The volume of literature generated in the short time since the appearance of SARS-CoV-2 has been considerable. Many studies have sought to document the risk factors for COVID-19 disease, disease severity and mortality; age was the only risk factor based on robust studies and with a consistent body of evidence. Mechanistic studies are required to understand why age is such an important risk factor. At the start of pandemics, large, standardised, studies that use multivariable analyses are urgently needed so that the populations most at risk can be rapidly protected. REGISTRATION: This review was registered on PROSPERO as CRD42020177714 .

COVID-19 end-of-life care: symptoms and supportive therapy use in an Australian hospital.

BACKGROUND: Descriptions of symptoms and medication use at end of life in COVID-19 are limited to small cross-sectional studies, with no Australian longitudinal data. AIMS: To describe end-of-life symptoms and care needs of people dying of COVID-19. METHODS: This retrospective cohort study included consecutive admitted patients who died at a Victorian tertiary referral hospital from 1 January to 30 September directly due to COVID-19. Clinical characteristics, symptoms and use of supportive therapies, including medications and non-pharmacological interventions in the last 3 days of life were extracted. RESULTS: The cohort comprised 58 patients (median age 87 years, interquartile range (IQR) 81-90) predominantly admitted from home (n = 30), who died after a median of 11 days (IQR 6-28) in the acute medical (n = 31) or aged care (n = 27) wards of the hospital. The median Charlson Comorbidity Score was 7 (IQR 5-8). Breathlessness (n = 42), agitation (n = 36) and pain (n = 33) were the most frequent clinician-reported symptoms in the final 3 days of life, with most requiring opioids (n = 52), midazolam (n = 40), with dose escalation commonly being required. While oxygen therapy was commonly used (n = 47), few (n = 13) required an anti-secretory agent. CONCLUSIONS: This study presents one of the first and largest Australian report of the end of life and symptom experience of people dying of COVID-19. This information should help clinicians to anticipate palliative care needs of these patients, for example, recognising that higher starting doses of opioids and sedatives may help reduce prevalence and severity of breathlessness and agitation near death.

Bibliometric analysis on global research trends of airborne microorganisms in recent ten years (2011-2020)

In recent years, exposure to bioaerosols—airborne particles of biological origin—has become a significant public health concern. Hence, this study aims to provide a bibliometric analysis of global trends in research on airborne microorganisms in the last ten years (2011–2020). Using the Web of Science (WoS) Core Collection database, a total of 1087 articles published during this period were selected for analysis. Firstly, we identified 11 co-citation clusters: potential pan microbiome, bioaerosol science, beneficial microbe, urban area, fungal microbiota, wastewater treatment plant, airborne microbial aerosol, modern practice, composting facilities— a review, airborne microbial biodiversity, and acidic electrolyzed water. Based on the co-occurrence between keywords in this literature, we concluded that particle-attached microorganisms, community structures of urban airborne microbes, and biological aerosols have inspired the hotspots in research during recent years, which suggests that bioaerosols are currently a popular topic in the field of air microbiology, with bacteria being the most frequently studied airborne microorganisms. We also discovered that interest in coronavirus disease 2019 (COVID-19) has continually risen during the past eight months, with the number of relevant articles exceeding 19,880, of which 106 have been frequently cited. Analyzing 500 recent publications on this topic, we found a high co-occurrence of COVID-19, pandemic, and coronavirus as well as of anxiety, depression, and stress. The greatest number of articles on airborne microorganisms in the last decade have been contributed by the USA, followed by China and France. Moreover, according to this metric, the leading institutions are Colorado State University and Peking University, and the top three journals are Applied and Environmental Microbiology, Atmospheric Environment, and Science of the Total Environment. The annual publication volume for this subject shows an increasing trend, indicating that interest in airborne microorganisms continues to grow. Our bibliometric analysis reveals the recent research hotspots and topic trends in air microbiology, thus offering potential clues for further examination. © The Author(s).

Current status and future prospect of management of biosafety laboratories for emerging infectious diseases

In recent years, there have been several outbreaks of infectious diseases around the world, including severe acute respiratory syndrome, Ebola virus disease, Middle East respiratory syndrome, and corona virus disease 2019. Experience suggests that the detection and research of emergent infectious diseases play a crucial role in the process of responding to the epidemic, which also brings great challenges to biosafety laboratories. In the face of unknown biological risk factors, the non-standard biosafety protection measures have a serious impact on the life safety of laboratory staff and the research of infectious diseases, which stresses the necessity of safety protection in biosafety laboratories. This article will briefly review the current status and future prospect of management of biosafety laboratories both in China and other countries in terms of safety protection measures during new sudden infectious disease incidents.