Analysis of the mental health status of hospital staff during the COVID-19 pandemic.

BACKGROUND: Medical work is a complex and interpersonally sensitive job; clinicians interact with patients, colleagues and society-at-large daily, and they are under pressure from a variety of sources. The doctor-patient relationship is of particular concern. OBJECTIVE: To investigate the current mental health status of hospital staff and related influencing factors during the coronavirus disease 2019 (COVID-19) pandemic. METHODS: The Symptom Checklist-90 (SCL-90) and Generalized Anxiety Disorder Scale-7 (GAD-7) were used to survey the current mental health status of hospital employees. The resulting qualitative data was described in the form of frequency and percentage (%), and the quantitative data were expressed as mean±standard deviation (X¯±S). RESULTS: A total of 1,074 employees of The Third Xiangya Hospital of Central South University participated in the mental health survey, of whom 77.47% were women. The SCL-90 score was 133.89±48.87, and the three highest scoring factors were depression, somatisation and obsessions, with factor scores of 19.10±8.14, 16.78±6.21 and 16.27±6.39, respectively. The GAD-7 score was 3.74±4.17 for women and 2.14±3.55 for men. The number of women with anxiety disorders was higher compared with men. CONCLUSION: The mental health status of hospital workers with different demographic characteristics varied greatly during the COVID-19 pandemic. Active attention needs to be paid to the mental health status of hospital staff.

Health-Seeking Behavior and Its Associated Technology Use: Interview Study Among Community-Dwelling Older Adults.

BACKGROUND: Understanding older people's health-seeking behavior (HSB) is crucial for uncovering their health needs and priorities and developing appropriate policies to address them and avert their disease progression. Technologies play an active role in our daily lives and have been incorporated into health activities to support the older population and facilitate their HSB. However, previous studies of HSB have mainly focused on behaviors during illness, and there are limited studies on how technologies have been used in older people's health-seeking activities. OBJECTIVE: This study aimed to investigate HSB and the associated technology use among the older population, ultimately proposing implications for practice to address their unmet health needs. METHODS: This paper presents partial data from a large qualitative study, which has been approved by the institutional review board and used a phenomenological approach. Semistructured interviews were conducted between April 2022 and July 2022, either via Zoom (Zoom Video Communications Inc) or face-to-face sessions. Inclusion criteria were being aged ≥50 years, long-term residence in Singapore, and being able to speak English or Mandarin. The interviews were manually transcribed verbatim, and thematic analysis was performed, with the individual as the unit of analysis to understand the patterns of behaviors. RESULTS: In total, 15 interviews were conducted to reach thematic saturation. We identified 5 main consequences of HSB, which were aligned with the original HSB model. Regarding technology use in health seeking, 4 themes were extracted: the most widely used digital technologies are the mobile health apps and wearable devices with the associated wellness programs launched by the government and local companies, and they have the potential to enhance health communication, promote health maintenance, and increase access to health services; information communication technologies and social media, though not primarily designed for health purposes, play a substantial role in easing the process of seeking health information and managing symptoms. Although the outbreak of the COVID-19 pandemic has resulted in some alterations to older adults' well-being, it has catalyzed the adoption of telehealth as a complement to access health care services, and older adults have different considerations when selecting technologies to facilitate their health seeking and fulfill their health needs. Moreover, 4 archetypes were proposed based on our findings and the insights gained from our participants' observations in their social networks. These findings led to several implications for practice regarding health communication and promotion, health education, technology design and improvement, telemonitoring service implementation, and solutions to address the needs of each proposed archetype. CONCLUSIONS: Unlike the commonly held belief that older adults resist technologies and lack technological proficiency, our findings showed that technologies could play a promising role in facilitating older adults' health seeking. Our findings have implications for the design and implementation of health services and policies.

Compliance Challenges in a Longitudinal COVID-19 Cohort Using Wearables for Continuous Monitoring: Observational Study.

BACKGROUND: The WEAICOR (Wearables to Investigate the Long Term Cardiovascular and Behavioral Impacts of COVID-19) study was a prospective observational study that used continuous monitoring to detect and analyze biometrics. Compliance to wearables was a major challenge when conducting the study and was crucial for the results. OBJECTIVE: The aim of this study was to evaluate patients' compliance to wearable wristbands and determinants of compliance in a prospective COVID-19 cohort. METHODS: The Biostrap (Biostrap USA LLC) wearable device was used to monitor participants' biometric data. Compliance was calculated by dividing the total number of days in which transmissions were sent by the total number of days spent in the WEAICOR study. Univariate correlation analyses were performed, with compliance and days spent in the study as dependent variables and age, BMI, sex, symptom severity, and the number of complications or comorbidities as independent variables. Multivariate linear regression was then performed, with days spent in the study as a dependent variable, to assess the power of different parameters in determining the number of days patients spent in the study. RESULTS: A total of 122 patients were included in this study. Patients were on average aged 41.32 years, and 46 (38%) were female. Age was found to correlate with compliance (r=0.23; P=.01). In addition, age (r=0.30; P=.001), BMI (r=0.19; P=.03), and the severity of symptoms (r=0.19; P=.03) were found to correlate with days spent in the WEAICOR study. Per our multivariate analysis, in which days spent in the study was a dependent variable, only increased age was a significant determinant of compliance with wearables (adjusted R2=0.1; ß=1.6; P=.01). CONCLUSIONS: Compliance is a major obstacle in remote monitoring studies, and the reasons for a lack of compliance are multifactorial. Patient factors such as age, in addition to environmental factors, can affect compliance to wearables.

What Attributes Make United States Plastic Surgery Programs Desirable? Perspectives from Medical Students and Residents.

Background: Plastic surgery is a competitive specialty that attracts many qualified applicants. This study provides results of a survey conducted to identify the attributes that make integrated and independent plastic surgery residency programs in the United States most desirable. Medical students' preferences for virtual events during the 2020 to 2021 Match cycle were also investigated. Methods: An anonymous 7-question survey was deployed to plastic surgery residency program directors to forward to medical students and current residents during the 2021 to 2022 National Resident Matching Program cycle. Demographics and ranking of program attributes were collected in the survey, and data analysis consisted of both qualitative and quantitative results. Results: A total of 50 survey respondents from 28% of plastic surgery residency programs was received. The happiness and well-being of residents and training at their preferred geographical location were identified as the most desirable qualities of a program (P < 0.05). However, 82% of respondents opposed mandatory research (P = 0.002). There was a statistically significant larger proportion of students that highly valued faculty responsiveness to feedback over perceived program ranking (P < 0.05). In the context of COVID-19, virtual meet and greets, as well as resident happy hours, were rated most desirable (n = 35, 70%), followed by social media outreach (n = 23, 46%). Conclusions: Resident happiness and well-being, followed by preferred geographical location, were the most favorable program attributes. Plastic surgery residency programs may seek to better integrate these preferences within their curricula, as well as showcase them on social media, to attract high-quality applicants and optimize the training experience for matched residents.

Studying the Effect of Long COVID-19 Infection on Sleep Quality Using Wearable Health Devices: Observational Study.

BACKGROUND: Patients with COVID-19 have increased sleep disturbances and decreased sleep quality during and after the infection. The current published literature focuses mainly on qualitative analyses based on surveys and subjective measurements rather than quantitative data. OBJECTIVE: In this paper, we assessed the long-term effects of COVID-19 through sleep patterns from continuous signals collected via wearable wristbands. METHODS: Patients with a history of COVID-19 were compared to a control arm of individuals who never had COVID-19. Baseline demographics were collected for each subject. Linear correlations among the mean duration of each sleep phase and the mean daily biometrics were performed. The average duration for each subject's total sleep time and sleep phases per night was calculated and compared between the 2 groups. RESULTS: This study includes 122 patients with COVID-19 and 588 controls (N=710). Total sleep time was positively correlated with respiratory rate (RR) and oxygen saturation (SpO2). Increased awake sleep phase was correlated with increased heart rate, decreased RR, heart rate variability (HRV), and SpO2. Increased light sleep time was correlated with increased RR and SpO2 in the group with COVID-19. Deep sleep duration was correlated with decreased heart rate as well as increased RR and SpO2. When comparing different sleep phases, patients with long COVID-19 had decreased light sleep (244, SD 67 vs 258, SD 67; P=.003) and decreased deep sleep time (123, SD 66 vs 128, SD 58; P=.02). CONCLUSIONS: Regardless of the demographic background and symptom levels, patients with a history of COVID-19 infection demonstrated altered sleep architecture when compared to matched controls. The sleep of patients with COVID-19 was characterized by decreased total sleep and deep sleep.

Accurate Identification of Transcription Regulatory Sequences and Genes in Coronaviruses.

Transcription regulatory sequences (TRSs), which occur upstream of structural and accessory genes as well as the 5' end of a coronavirus genome, play a critical role in discontinuous transcription in coronaviruses. We introduce two problems collectively aimed at identifying these regulatory sequences as well as their associated genes. First, we formulate the TRS Identification problem of identifying TRS sites in a coronavirus genome sequence with prescribed gene locations. We introduce CORSID-A, an algorithm that solves this problem to optimality in polynomial time. We demonstrate that CORSID-A outperforms existing motif-based methods in identifying TRS sites in coronaviruses. Second, we demonstrate for the first time how TRS sites can be leveraged to identify gene locations in the coronavirus genome. To that end, we formulate the TRS and Gene Identification problem of simultaneously identifying TRS sites and gene locations in unannotated coronavirus genomes. We introduce CORSID to solve this problem, which includes a web-based visualization tool to explore the space of near-optimal solutions. We show that CORSID outperforms state-of-the-art gene finding methods in coronavirus genomes. Furthermore, we demonstrate that CORSID enables de novo identification of TRS sites and genes in previously unannotated coronavirus genomes. CORSID is the first method to perform accurate and simultaneous identification of TRS sites and genes in coronavirus genomes without the use of any prior information.

Experience and Perceptions of Chinese University Students Regarding the COVID-19 Pandemic: A Qualitative Analysis.

The COVID-19 pandemic has exerted a significant influence on university student's learning, life and mental health. Using the method of inductive content analysis, this study examined the experience and perceptions of Chinese university students regarding the COVID-19 pandemic. Eighteen university students including seven males and eleven females were involved in this study. In this study, we identified five themes concerning experience and perceptions of Chinese university students regarding the COVID-19 pandemic, that is, emotional reactions toward the COVID-19 pandemic, the influence of the COVID-19 pandemic on the participant's learning, the influence of the COVID-19 pandemic on the participant's daily life, participants' positive responses to the COVID-19 pandemic, and China's moves toward the COVID-19 pandemic. This study elaborated on experience and perceptions of Chinese university students regarding the COVID-19 pandemic, which would provide us a better understanding of how university students perceive the COVID-19 pandemic and to what extent the COVID-19 pandemic has influenced their learning and daily lives.

Optimization of Multisource Dynamic Model in TBO

When extracting flight data from airport terminal area, there are matters such as large volume, unclear features, and similar trend in time series. In order to deal with the related issues and to optimize the description, by combining with the TBO (Trajectory-Based Operation), an application proposed by the ICAO (International Civil Aviation Organization) in ASBU (Aviation System Block Upgrade), using multisource dynamic model to establish 4DDW (4D dynamic warping) algorithm, the multisource modeling integrated with evaluation system is proposed to realize the flight path optimization with time series characteristics and accord with the interval concept. The calculation results show that 4DDW can obtain the optimal solution for multiprofile calculation of TBO by comparing the composite trajectory deviation values and time dimension planning using the buffer and threshold values recommended by ICAO in airspace planning and flight procedure design. The results meet the requirements of high accuracy and convergence features of spatial waypoints and can improve the airport operation standards and terminal area capacity.

Volatility in natural resources, economic performance, and public administration quality: Evidence from COVID-19.

The recent Covid-19 pandemic outbreak caused a global economic recession and promoted uncertainty in the natural resources. Also, this uncertainty is linked with the demand and supply of natural resources such as oil and natural gas, which is a substantial factor of industrial and economic activities. Declining natural resource demands substantially drop such activities that adversely affect economic performance. This attracts the attention of policy-makers and governors to efficiently tackle the issue. This study investigates the association of natural resources volatility, global economic performance, and public administration in earlier and Covid-19 pandemic peak periods. The study covers the period from 1990 to 2020 for the global data. The empirical findings of the cointegration test suggested that the variables are cointegrated. This study utilizes three long-run estimators, i.e., fully modified ordinary least square (FMOLS), dynamic OLS (DOLS), and Canonical Cointegrating Regression (CCR). The empirical findings suggest that natural resources volatility (TNR) negatively and significantly affect global economic performance. While natural gas rents, oil rents, and public administration quality (QPA) promote global economic performance. Besides, the results also indicate that the interaction of QPA and TNR enhances economic performance. This study demonstrates that volatility in natural resources is detrimental to global economic performance. However, improved public administrative quality could play a significant role in transforming the negative influence. of natural resources volatility into a positive effect. The findings are robust as validated by Robust regression. This study provides some practical policy insights for the governors and policy-makers to tackle the mentioned issues.

Design and Evaluation of a Novel Peptide-Drug Conjugate Covalently Targeting SARS-CoV-2 Papain-like Protease.

Coronavirus disease 2019 (COVID-19) pandemic, a global health threat, was caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The SARS-CoV-2 papain-like cysteine protease (PLpro) was recognized as a promising drug target because of multiple functions in virus maturation and antiviral immune responses. Inhibitor GRL0617 occupied the interferon-stimulated gene 15 (ISG15) C-terminus-binding pocket and showed an effective antiviral inhibition. Here, we described a novel peptide-drug conjugate (PDC), in which GRL0617 was linked to a sulfonium-tethered peptide derived from PLpro-specific substrate LRGG. The EM-C and EC-M PDCs showed a promising in vitro IC50 of 7.40 ± 0.37 and 8.63 ± 0.55 µM, respectively. EC-M could covalently label PLpro active site C111 and display anti-ISGylation activities in cellular assays. The results represent the first attempt to design PDCs composed of stabilized peptide inhibitors and GRL0617 to inhibit PLpro. These novel PDCs provide promising opportunities for antiviral drug design.

A cardiovascular clinic patients' survey to assess challenges and opportunities of digital health adoption during the COVID-19 pandemic.

BACKGROUND: COVID-19 boosted healthcare digitalization and personalization in cardiology. However, understanding patient attitudes and engagement behaviors is essential to achieve successful acceptance and implementation of digital health technologies in personalized care. OBJECTIVE: This study aims to understand current and future trends in wearable device and telemedicine use in the cardiology clinic patient population, recognize patients' attitude towards digital health before and after COVID-19, and identify potential socioeconomic and racial/ethnic differences in adoption of digital health tools in a New Orleans patient population. METHODS: A cross-sectional survey was distributed to Tulane Cardiology Clinic patients between September 2020 and January 2021. Basic demographic information, medical comorbidities, device usage, and opinions on digital health tools were collected. RESULTS: Survey responses from 299 participants (average age = 54 years, 50.8% female, 24.4% African American) showed that digital health use was more prevalent in younger, healthier, and more educated individuals. Wearable use was also higher among White patients compared to African American patients. Patients cited costs and technology knowledge as primary deterrents for using wearables, despite being more inclined to use wearables for disease monitoring (41%). While wearable use did not increase after COVID-19 (36.6% pre-COVID vs 35.4% post-COVID, P = .77), telemedicine use rose significantly (10.8% pre-COVID vs 24.3% during COVID, P < .0001). Patients mostly noted telemedicine's effectiveness in overcoming difficult healthcare access barriers. Additionally, most patients are in support of wearables and telemedicine either complementing or replacing routine tests and traditional clinical visits. CONCLUSION: Demographic and socioeconomic disparities negatively impact wearable health device and telemedicine adoption within cardiovascular clinic patients. Although telemedicine use increased after COVID-19, this effect was not observed for wearables, reflecting significant economic and digital literacy challenges underlying wearable acceptance.

Exploiting Shared Knowledge From Non-COVID Lesions for Annotation-Efficient COVID-19 CT Lung Infection Segmentation.

The novel Coronavirus disease (COVID-19) is a highly contagious virus and has spread all over the world, posing an extremely serious threat to all countries. Automatic lung infection segmentation from computed tomography (CT) plays an important role in the quantitative analysis of COVID-19. However, the major challenge lies in the inadequacy of annotated COVID-19 datasets. Currently, there are several public non-COVID lung lesion segmentation datasets, providing the potential for generalizing useful information to the related COVID-19 segmentation task. In this paper, we propose a novel relation-driven collaborative learning model to exploit shared knowledge from non-COVID lesions for annotation-efficient COVID-19 CT lung infection segmentation. The model consists of a general encoder to capture general lung lesion features based on multiple non-COVID lesions, and a target encoder to focus on task-specific features based on COVID-19 infections. We develop a collaborative learning scheme to regularize feature-level relation consistency of given input and encourage the model to learn more general and discriminative representation of COVID-19 infections. Extensive experiments demonstrate that trained with limited COVID-19 data, exploiting shared knowledge from non-COVID lesions can further improve state-of-the-art performance with up to 3.0% in dice similarity coefficient and 4.2% in normalized surface dice. In addition, experimental results on large scale 2D dataset with CT slices show that our method significantly outperforms cutting-edge segmentation methods metrics. Our method promotes new insights into annotation-efficient deep learning and illustrates strong potential for real-world applications in the global fight against COVID-19 in the absence of sufficient high-quality annotations.

Ninety Days in: A Comprehensive Review of the Ongoing COVID-19 Outbreak

According to folk beliefs, the meats of the wild game have natural healing powers and are thus often used in "dietary therapy" and traditional Chinese medicine. The most common diagnosis tool has been quantitative reverse transcriptase PCR (qRT-PCR) but medical professionals have been pushing to establish diagnostic criteria based on high definition chest CT, in order to circumvent the limited capacity of the PCR kits [69]. [...]Japan, one of the most severely affected countries other than China, is also the host nation for the upcoming 2020 Summer Olympics [21]. [...]Japan is under great pressure to keep the epidemic in check ahead of the world's greatest support event, where heavy international traffic may exacerbate the spread of the virus [51]. [...]there is a speculation that SARSCoV-1 and SARS-CoV-2 have similar transmissions, since they are both coronaviruses.