Outbreak Of Coronavirus Disease Research Articles

Insights on interferon-independent induction of interferon-stimulated genes shaping the lung's response in early SARS-CoV-2 infection

While mRNA vaccine efficacy against the 2019 coronavirus disease (COVID-19) outbreak remains high, research on antiviral innate immune responses in the early stages of infection is essential to develop strategies to prevent the dissemination of SARS-CoV-2. In this study, we investigated the induction of both interferon (IFN)-stimulated genes (ISGs) and IFN-independently upregulated ISGs following SARS-CoV-2 infection in Syrian golden hamsters. The viral titers were highest at 3 days post-infection (dpi). Over time, the viral titer gradually decreased while ISGs such as Mx1, Ifit2, Ifit3, Ifi44, and Rsad2 were markedly induced in the lung. The transcription of ISGs significantly increased from 2 dpi, and SARS-CoV-2-induced ISGs were maintained in the hamster lung until 7 dpi. The transcription of Ifnb and Ifng was minimally elevated, while Ifnl2/3 was significantly induced in the lung at 5 days after SARS-CoV-2 infection. RNA sequencing results also showed that at 3 dpi, SARS-CoV-2 initiated the activation of ISGs, with lesser increases of Ifnl2 and Ifnl3 transcription. In addition, Ddx58 and cGAS, which encode factors for virus sensing, Stat1, Stat2, and IFN regulatory factor 7 and 9 mRNA levels were also induced at the initial stage of infection. Our data demonstrate that ISGs might be upregulated in the lung in response to SARS-CoV-2 during the early stages of infection, and the rapid induction of ISGs was not associated with the activation of IFNs. Elucidation of IFN-independent induction of ISGs could further our understanding of alternative defense mechanisms employed by the lungs against SARS-CoV-2 and provide more effective antiviral strategies for patients with severe COVID-19.

Targeting Glutamine Dependence through GLS1 Inhibition Suppress Multiple Myeloma and Phenomenon of Sars-Cov-2 Infectious Disease

Introduction: Multiple myeloma (MM) is characterized by the clonal expansion of plasma cells in the bone marrow. The treatment of MM patients has been dramatically changed by new agents, however, many patients will relapse even if new agents provide therapeutic advantages. Therefore, a new strategy is still needed to increase MM patient survival. Metabolic reprogramming is recognized as one of the hallmarks of cancer cells. Glutamine is the most abundant circulating amino acid in blood, glutamine metabolism through glutaminolysis may be associated with myeloma cell survival. The outbreak of coronavirus disease 2019 (COVID-19), caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection, has rapidly spread to more than 196 countries worldwide. Myeloma patients present an increased risk for severe COVID-19 infection and poor outcomes. Materials and Methods: In this study, we investigated whether glutaminase (GLS) inhibitor, telaglenastat could suppress myeloma cells and enhance the sensitivity of myeloma cells to histone deacetylase (HDAC) inhibition. We also investigated whether telaglenastat suppress the SARS-CoV-2 phenomenon by using mouse macrophage cell line, RAW264. Results: We first investigated the GLS gene expression against MM patients from the online Gene Expression Omnibus (GEO) data. GLS1 expression was increased in monoclonal gammopathy of undetermined significance (MGUS) and myeloma patients compared to normal controls. We next investigated the glutaminolysis in myeloma cells. Deprivation of glutamine from the culture medium revealed that cellular growth inhibition and increased caspase 3/7 activity. We next evaluated the effect of GLS inhibitor, telaglenastat. MM cells were inhibited by telaglenastat in a dose dependent manner. Cellular cytotoxicity was also increased. Glutamine is converted by GLS into glutamate and alpha-ketoglutarate (α-KG), and related nicotinamide adenine dinucleotide phosphate (NADP) production. Intracellular α-KG and NADPH were reduced by telaglenastat. As metabolites are the substrates used to generate chromatin modification including acetylation of histone, we investigated HDAC inhibitor, panobinostat in myeloma cells. Myeloma cells were inhibited by panobinostat and histone acetylation, caspase 3/7 activity and cytotoxicity were increased. Combined treatment with panobinostat and telaglenastat caused more cytotoxicity. Sub-G1 phase was increased by cell cycle analysis. Caspase 3/7 activity and cellular cytotoxicity were also increased. Proteasomal activity was reduced. Immunoblot analysis revealed cleaved caspase 3 and γ- H2AX were increased by panobinostat and telaglenastat treatment. GLS shRNA transfectant cells were inhibited cellular proliferation and GLS shRNA transfectant cells were increased the sensitivity of panobinostat. We next investigated the effects of COVID-19 S1 protein and teragrenastat by using RAW264 cells. Gene expression of inflammatory cytokines, such as tumor necrosis factor α (TNFα) was induced by COVID-19 S1 protein treatment and inhibited by telaglenastat. Intracellular reactive oxygen species (ROS) and NFkB-p65 (Ser536) phosphate activity was increased by COVID-19 S1 protein and inhibited by telaglenastat. Conclusion: The GLS is involved myeloma progress and GLS inhibitor suppresses myeloma cells and enhance cytotoxic effects of HDAC inhibitor. We also provide the GLS inhibitor is effective to SARS-CoV-2 phenomenon and promising clinical relevance as a candidate drug for treatment of myeloma patients in COVID-19 pandemic.

A Review on the Ethnopharmacology, Phytochemistry and Pharmacology of Natural Phytochemicals used for Ameliorating/preventing SARS-CoV-2

The global corona virus disease outbreak, also known as the COVID-19 pandemic outbreak, caused a major health crisis around the world. The morbidity and mode of transmission of COVID-19 appear to be more severe and uncontrollable. The main pathophysiology of this deadly disease is respiratory failure and subsequent cardiovascular complications. From a pharmacological standpoint, several therapeutic strategies have been proposed for the development of safe and effective treatment against the SARS-CoV-2 virus, but no specific treatment regimen has been developed for this viral infection to date. The current review focuses on the role of herbs and herbs-derived secondary metabolites in inhibiting the SARS-CoV-2 virus as well as in the management of post-COVID-19 complications. This strategy will promote and protect the use of medicinal plant resources to support the healthcare system. Plant-derived phytochemicals have already been shown to prevent viral infection and to alleviate post-COVID complications such as parkinsonism, kidney and heart failure, liver and lung damage, and mental problems. We investigated mechanistic approaches of herbal medicines and their phytochemicals as antiviral and post-COVID complications by modulating immunological and inflammatory states in this review.

Global stability analysis of transmission dynamics in a compartmental model of coronavirus disease with environmental reservoirs

This work proposes and analyzes a non-linear deterministic model which describes the outbreak of Coronavirus disease with fomites (environmental reservoirs) propagating the spread of the infection in human population. Suitable Lyapunov functions are constructed to demonstrate the Global Asymptotic Stability (GAS) of both the Endemic Equilibrium (EE) and Disease Free Equilibrium (DFE) of the system. The EE was also found to be epidemiologically relevant when the basic reproduction number of new infections is greater than unity.

Sistem Prediksi Kasus Covid-19 di Indonesia Menggunakan Algoritma Linear Regression

The Coronavirus disease outbreak caused by severe acute respiratory syndrome by coronavirus 2 was first reported in Wuhan, Hubei province, China in December 2019, until March 2, 2020, President Joko Widodo announced the first case of an Indonesian citizen who was confirmed positive for COVID-19. The development of new cases of COVID-19 patients in Indonesia is still being reported even though the pandemic has lasted for almost two years. Then need a way to determine predictions or predict the number of increases in Indonesia’s COVID-19 cases in the future using machine learning technology with the Linear Regression algorithm. Estimating the number of active cases adding positive COVID-19 cases in Indonesia over the next 3 months using the machine learning method using the Linear Regression algorithm. This study predicts COVID-19 cases using machine learning with the Linear Regression algorithm. The model results have a linear coefficient, so the model predicts very well for linear data on days 0 – 300, and on the day after that, the number of positive cases of the national COVID-19 virus does not continue to show a linear relationship, the model becomes inaccurate again. The results of the parameter evaluation show that the level of accuracy is low, but this model can be used as a reference for case predictions for the next month with the results of comparison of predicted data and actual data not much different.

Changes in Caregiver Burden in Older Adults' Caregivers During the COVID-19 Outbreak.

This study investigated the changes in caregiver burden during the coronavirus disease 2019 (COVID-19) pandemic. The study sample included 827 caregivers for older adults (65+) from the National Health and Aging Trends Study (NHATS) COVID-19 data set. We used paired t-tests to analyze data. The caregivers' assistance in basic and instrumental activities of daily living for care recipients, as well as the amount of time they provided, increased significantly (all ps < .001) during the pandemic compared with before. Approximately, 40% of caregivers reported changes in the amount of assistance they provided during the pandemic. Financial, emotional, and physical difficulties among caregivers have increased, with emotional difficulties being the most frequently reported. The majority of caregivers reported not utilizing any social services during the pandemic. The pandemic may have heightened the caregiver burden. It is important to ensure that caregivers have access to and utilize social services in a physically and psychologically safe manner.

The use of dietary supplements and vitamin consumption during and after the Covid pandemic in Vietnam: a perspective of user-generated content

This paper aims to examine the key determinants of User-Generated Content (UGC) affecting consumers’ purchase intention toward buying vitamin and dietary supplement products during the coronavirus disease (COVID-19) outbreak through the social networking site, Facebook. Five determinants of UGC have been investigated in this context to highlight the association with the purchase intention of Vietnamese consumers these days, including Quality, Quantity, Trust, Utilitarian Value, and Hedonic Value of generated content by experienced users. This cross-sectional study collected data through an online survey questionnaire among 331 respondents from three different regions throughout the whole of Vietnam. The results show that Quality, Quantity, Trust, and Utilitarian Value of UGC have positive relationships with consumers’ intention to buy vitamin and dietary supplement products on Facebook during the COVID-19 pandemic, while the Hedonic Value of UGC is not associated with their behavioral intention of consumption. Lastly, the findings are expected to contribute valuable knowledge about COVID-19 from UGC and its consequences on health and selected supplement products for recent emerging changes. Finally, the results are also a solid premise for future research to enlarge the scale of situational issues and perspectives during the global COVID-19 pandemic.

The emergence and successful elimination of SARS-CoV-2 dominant strains with increasing epidemic potential in Taiwan’s 2021 outbreak

Taiwan’s experience with severe acute respiratory syndrome coronavirus (SARS-CoV) in 2003 guided its development of strategies to defend against SARS-CoV-2 in 2020, which enabled the successful control of Coronavirus disease 2019 (COVID-19) cases from 2020 through March 2021. However, in late-April 2021, the imported Alpha variant began to cause COVID-19 outbreaks at an exceptional rate in Taiwan. In this study, we aimed to determine what epidemiological conditions enabled the SARS-CoV-2 Alpha variant strains to become dominant and decline later during a surge in the outbreak. In conjunction with contact-tracing investigations, we used our bioinformatics software, CoVConvert and IniCoV, to analyze whole-genome sequences of 101 Taiwan Alpha strains. Univariate and multivariable regression analyses revealed the epidemiological factors associated with viral dominance. Univariate analysis showed the dominant Alpha strains were preferentially selected in the surge’s epicenter (p = 0.0024) through intensive human-to-human contact and maintained their dominance for 1.5 months until the Zero-COVID Policy was implemented. Multivariable regression found that the epidemic periods (p = 0.007) and epicenter (p = 0.001) were two significant factors associated with the dominant virus strains spread in the community. These dominant virus strains emerged at the outbreak’s epicenter with frequent human-to-human contact and low vaccination coverage. The Level 3 Restrictions and Zero-COVID policy successfully controlled the outbreak in the community without city lockdowns. Our integrated method can identify the epidemiological conditions for emerging dominant virus with increasing epidemiological potential and support decision makers in rapidly containing outbreaks using public health measures that target fast-spreading virus strains.

Hepatic steatosis and skeletal muscle alterations during the COVID-19 lockdown in a cohort of patients with chronic liver disease in Japan.

Following the coronavirus disease outbreak, a state of public emergency was declared worldwide, which enforced lifestyle changes. This study therefore aimed to investigate the changes in lifestyle, body composition, hepatic steatosis, and fibrosis in patients with chronic liver disease (CLD) under lockdown. During the lockdown period, 1344 patients with CLD answered a lifestyle questionnaire. In 298 patients, body composition and liver stiffness measure (LSM)/controlled attenuation parameter (CAP) were analyzed by InBody and FibroScan, respectively, and serial data were obtained in 137 patients. More than half of the CLD patients answered decreases in physical activity and frequency of outings during lockdown, while diet was less affected. Overall, 58% of patients showed elevations in CAP values, which were not different statistically over time. Women, but not men, were more likely to increase CAP values during lockdown. Neither LSM nor serum fibrosis markers were elevated chronologically during lockdown. In men, body mass index (BMI), body fat percentage, and visceral fat area (VFA) were significantly increased, whereas in women, lower-limb muscle mass was significantly decreased. Patients with decreased SMI showed elevations in CAP and VFA values, and patients who exercised less showed increases in BMI. In response to lockdown, men tended to increase body fat but the degree of hepatic steatosis was less affected, while women were more likely to exacerbate hepatic steatosis with skeletal muscle loss among CLD patients. Gender-specific approaches need to be established for management of CLD patients to avoid exacerbation or comorbidity of steatotic liver disease.

Factors Driving China's Carbon Emissions after the COVID-19 Outbreak.

The outbreak of the coronavirus disease 2019 (COVID-19) may exert profound impacts on China's carbon emissions via structural changes. Due to a lack of data, previous studies have focused on quantifying the changes in carbon emissions but have failed to identify structural changes in the determinants of carbon emissions. Here, we use China's latest input-output table and apply structural decomposition analyses to understand the dynamic changes in the determinants of carbon emissions from 2012 to 2020, specifically the impact of COVID-19 on carbon emissions. We find that final demand per capita contributed to emissions growth at a slower pace, but production structure drove a greater carbon emissions increase than before the pandemic. Export-led emissions growth rebounded, and investment-led emissions were more concentrated in the construction sector. The carbon intensity of several heavy industries increased, e.g., the nonmetallic products sector, the metal products sector, and the petroleum, coking, and nuclear fuel sector. In addition, lower production efficiency and increased reliance on carbon-intensive inputs indicated a deterioration in production structure. For policy implications, efforts should be undertaken to increase investment in low-carbon industries and increase the proportion of consumption in GDP to shift investment-led growth to consumption-led growth for an inclusive and green recovery from the pandemic.

Search-engine-based surveillance using artificial intelligence for early detection of coronavirus disease outbreak

The search-engine-based surveillance methods for the early warning and prediction of infectious diseases cannot achieve search engine keywords automatic filtering and real-time updating, lead to powerless for the early warning of emerging infectious diseases. The aim of this study is to develop an artificial intelligence (AI) method for search-engine-based surveillance to improve the early warning ability for emerging infectious diseases. The 32 keywords (444 million search queries) that may be related to the coronavirus disease (COVID-19) outbreak was collected from December 18, 2019 to February 11, 2020 from Baidu’s search engine database. The graph convolution network (GCN) model was used to select search engine keywords automatically, and then, multiple linear regression was performed to explore the relationship between the daily query frequencies of keywords and daily new cases. The GCN model was used to automatically select keywords. The prediction trend of the GCN model was highly consistent with the true curve with a mean absolute error of 81.65. Three keywords including “epidemic”, “mask” and “coronavirus” were selected. The selection keywords in the search queries were highly correlated with the daily number of confirmed cases (r = 0.96, 0.94, and 0.89; P < 0.01). An abnormal initial peak (3.05 times the normal volume) in queries appeared on December 31, 2019, which could have served as an early warning signal for an outbreak. Of particular concern, 17.5% of query volume originated from the Hubei Province, 51.15% of which was from Wuhan City. The coefficients of determination (R2) of our constructed model were 0.88, 0.88, 0.84, 0.77, 0.77, 0.75, 0.73, and 0.73 for a time lag of 0–7 days, respectively, using selection keywords. The model we constructed was used in the Beijing Xinfadi outbreak as an independent test dataset, which successfully predicted the daily numbers of cases for the following days and detected an early signal during the Beijing Xinfadi outbreak (R2 = 0.79). In this paper search-engine-based surveillance based on the AI method was established for the early detection of the COVID-19 epidemic for the first time. The model achieves automatic filtering and real-time updating of search engine keywords and can effectively detect the early signals of emerging infectious diseases.

The relationship between sleep quality and daytime dysfunction among college students in China during COVID-19: a cross-sectional study.

College Students' sleep quality and daytime dysfunction have become worse since the COVID-19 outbreak, the purpose of this study was to explore the relationship between sleep quality and daytime dysfunction among college students during the COVID-19 (Corona Virus Disease 2019) period. This research adopts the form of cluster random sampling of online questionnaires. From April 5 to 16 in 2022, questionnaires are distributed to college students in various universities in Fujian Province, China and the general information questionnaire and PSQI scale are used for investigation. SPSS26.0 was used to conduct an independent sample t-test and variance analysis on the data, multi-factorial analysis was performed using logistic regression analysis. The main outcome variables are the score of subjective sleep quality and daytime dysfunction. During the COVID-19 period, the average PSQI score of the tested college students was 6.17 ± 3.263, and the sleep disorder rate was 29.6%, the daytime dysfunction rate was 85%. Being female, study liberal art/science/ engineering, irritable (due to limited outdoor), prolong electronic entertainment time were associated with low sleep quality (p < 0.001), and the occurrence of daytime dysfunction was higher than other groups (p < 0.001). Logistics regression analysis showed that sleep quality and daytime dysfunction were associated with gender, profession, irritable (due to limited outdoor), and prolonged electronic entertainment time (p < 0.001). During the COVID-19 epidemic, the sleep quality of college students was affected, and different degrees of daytime dysfunction have appeared, both are in worse condition than before the COVID-19 outbreak. Sleep quality may was inversely associated with daytime dysfunction.

Effects of the COVID-19 pandemic on mental health and sleep quality in the Chinese general populations during sporadic and outbreak periods.

The different coronavirus disease 2019 (COVID-19) outbreak patterns may have different impact on the mental health, but there is no such report at present. This study was aimed to investigate general populations mental health and sleep quality during the sporadic and outbreak of COVID-19 in Tianjin of China. Two cross-sectional studies included 1090 participants during the COVID-19 sporadic period from 28th January to 28th February 2022 and 2036 participants during the COVID-19 outbreak period from 1st January to 1st February 2023. We used the propensity score matching method to match the 2 samples (sporadic and outbreak group) with a ratio of 1:1. Finally, 1087 participants were included in each group. The demographics, Patient Health Questionnaire-9, Generalized Anxiety Disorder Scale-7, Connor-Davidson Resilience Scale, and Pittsburgh Sleep Quality Index were collected from all participants. There was a significant difference between the sporadic and outbreak groups for COVID-19-infected status, anxiety levels, sleep quality, and phycological resilience (all P < .05). Logistic regression analysis showed that female (P < .05) and depression status (P < .05) were the risk factors for the incidence rate of anxiety and poor sleep quality in sporadic group, and outbreak group, while phycological resilience were robust related to the sleep quality (P < .01). Poor sleep quality (P < .01) was related to the incidence of anxiety in sporadic group but not the outbreak group; anxiety levels (P < .01) were related to sleep quality in sporadic group but not the outbreak group. During the COVID-19 pandemic, general populations experienced high anxiety and poor sleep quality incidence rates, especially in the outbreak period. Different risk models for anxiety and sleep quality were found in the different outbreak patterns.

A data-driven framework to assess population dynamics during novel coronavirus outbreaks: A case study on Xiamen Island, China.

The outbreak of the Coronavirus Disease 2019 (COVID-19) has profoundly influenced daily life, necessitating the understanding of the relationship between the epidemic's progression and population dynamics. In this study, we present a data-driven framework that integrates GIS-based data mining technology and a Susceptible, Exposed, Infected and Recovered (SEIR) model. This approach helps delineate population dynamics at the grid and community scales and analyze the impacts of government policies, urban functional areas, and intercity flows on population dynamics during the pandemic. Xiamen Island was selected as a case study to validate the effectiveness of the data-driven framework. The results of the high/low cluster analysis provide 99% certainty (P < 0.01) that the population distribution between January 23 and March 16, 2020, was not random, a phenomenon referred to as high-value clustering. The SEIR model predicts that a ten-day delay in implementing a lockdown policy during an epidemic can lead to a significant increase in the number of individuals infected by the virus. Throughout the epidemic prevention and control period (January 23 to February 21, 2020), residential and transportation areas housed more residents. After the resumption of regular activities, the population was mainly concentrated in residential, industrial, and transportation, as well as road facility areas. Notably, the migration patterns into and out of Xiamen were primarily centered on neighboring cities both before and after the outbreak. However, migration indices from cities outside the affected province drastically decreased and approached zero following the COVID-19 outbreak. Our findings offer new insights into the interplay between the epidemic's development and population dynamics, which enhances the prevention and control of the coronavirus epidemic.

Treatment Options for Patients with Inflammatory Bowel Disease During the COVID-19 Epidemic: A Review

The Corona Virus Disease (COVID-19) outbreak is affecting life around the world. In particular, people with basic diseases are concerned about whether COVID-19 will affect their original disease,Increasing attention is whether patients with inflammatory bowel disease (IBD) are more susceptible to COVID-19.Whether the treatment plans need to be adjusted, Summary literature shows that IBD patients have comparable infection rates as those in the general population, patients with IBD using biologics are more likely to develop asymptomatic infections and have fewer symptoms than normal populations, The use of parenteral-selective biologic agents may have a protective effect in lung function, which may be associated with its mitigation of cytokine storm syndrome. So patients with IBD should not interrupt the original biological treatment regimen. However, in preparation selection, parenteral-selective biological agents are preferred.

ELECTROCHEMICAL BIOSENSORS FOR SARS-COV-2 DETECTION

The 2019 coronavirus disease (COVID-19) outbreak has become global. The unprecedented socioeconomic impact of the pandemic has highlighted the need to improve existing diagnostic methods and develop new methods to control the spread of the disease. Traditional technologies such as quantitative real-time polymerase chain reaction (qRT-PCR) have been considered the gold standard for testing for COVID-19 since the SARS-CoV-2 genome sequence was published. However, they are time-consuming, labor-intensive and do not guarantee the absence of false results. Electrochemical biosensors present alternative approaches to detect viral nucleic acids or viral antigens. High sensitivity, relatively low cost of sensors and equipment, convenient management, rapid analysis, and suitability for miniaturization may contribute to the development of point-of-care (POC) testing for COVID-19. The review examines and critically discusses modern electrochemical biosensors for SARS-CoV-2 detection and related technologies.

Assessment of oral emergency services during COVID-19: a retrospective study of 14,885 cases in Shanghai

BackgroundTo assess the impact of coronavirus disease-2019 (COVID-19) in its outbreak stage (Spring Festival in 2020) on oral emergency services.MethodsOral emergency cases in Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, during the Spring Festival after the outbreak of the COVID-19 epidemic in 2020 were collected and compared with those in 2018 and 2019. Electronic medical records including the visited department, age, sex, time, date, region, and diagnosis were collected and analyzed. The results were statistically analyzed using Pearson’s Chi-square test and one-way analysis of variance (ANOVA).ResultsCompared with that in 2018 and 2019, the total number of patients decreased during the Spring Festival in 2020 (p < 0.001), but the proportions of patients visiting Oral Surgery and Oral, Head, and Neck Oncology Emergency departments increased. The average age of patients increased, and the number of night visits decreased. Toothache diseases involving endodontic and periodontal diseases increased, while the proportion of maxillofacial trauma decreased. The wasn’t a linear association between diagnosis or genders (P > 0.001) across years. However, a linear-by-linear association between age groups and years, visited departments and years were observed (P < 0.001).ConclusionsThe study revealed that the transmission of COVID-19 affected the patient population and structure of disease types and oral services in 2020 during the Spring Festival, compared with those in the previous two years. The visits to oral emergency departments and the proportions of patients who were children and adolescents reduced; meanwhile, the percentage of the elderly people increased during the outbreak of COVID-19. The clear trend of age groups and visiting divisions could be used as a marker to reflect the severity of the COVID-19 pandemic. These results may serve as a reference for dental practitioners involved in oral emergency services and to allocate the limited emergency health resources.

To Assess The Knowledge And Perceptions Regarding Novel Coronavirus (Covid-19) During The First Surge Among Healthcare Workers Of Banke District, Nepal

Background: Human Coronaviruses (HCoVs), a large family of single-stranded RNA viruses in humans causes mainly respiratory tract infections. The current outbreak of coronavirus disease 2019 has emerged as pandemic causing severe public health issue of international concern. This study aims to assess the knowledge and perception regarding novel coronavirus (COVID-19) during first wave of pandemic among healthcare workers working at various Health care institutions. Method: This web-based cross-sectional study was conducted among 174 health care workers of Banke district, Nepal between May-July 2020 during first wave of COVID-19 pandemic using an online self-administered questionnaire. A 35-item self-administered survey instrument was developed using WHO course materials on emerging respiratory viruses, including COVID-19, and covered the domains of HCWs characteristics, knowledge, and perceptions of HCWs related to COVID-19. Descriptive statistics were reported as proportion and frequencies. Bivariate analysis was performed using chi-square test to assess the association of level of knowledge, and perception about COVID-19 with participants’ characteristics. A p-value of less than 0.05 was considered statistically significant. All the statistical analysis was performed using IBM SPSS software, version 23. Findings: Most of the participants (94.8%) had known about the virus from news (70.1%) and social media (68.4%). Among them only 36.2% participants had the opportunities to attend lectures, discussions, and training about the virus COVID-19. Overall, 55.7%, and 52.9% of the HCWs had adequate knowledge, and positive perception about COVID-19, respectively. Bivariate analysis showed no significant association of knowledge regarding COVID-19 among health care worker with participants characteristics whereas, the variables such as gender, occupation, working department showed significant association of perception regarding COVID-19 among health care worker. Conclusion: This study revealed lower level of knowledge and positive perception regarding COVID-19 and infection control measures among HCWs. There is a need of adequate training to enhance the knowledge and perception about covid-19 and safety measures to the HCWs.

The effects of psychosocial and behavioral interventions on depressive and anxiety symptoms during the COVID-19 pandemic: a systematic review and meta-analysis

Psychosocial and behavioral interventions have been shown to significantly reduce depressive and anxiety symptoms in different populations. Recent evidence suggests that the mental health of the general population has deteriorated significantly since the start of Coronavirus Disease 2019 (COVID-19) pandemic. We conducted a systematic review and meta-analysis of studies on the therapeutic effects of psychosocial and behavioral interventions on depression and anxiety during the COVID-19 pandemic. We systematically searched six electronic databases between December 2019 and February 2022 including PubMed, PsycINFO, Scopus, Web of Science, CNKI, and Wanfang Data. We included randomized clinical trials of psychosocial and behavioral interventions in individuals with depressive or anxiety symptoms during the COVID-19 outbreak compared to various control conditions. A total of 35 eligible studies with 5457 participants were included. The meta-analysis results showed that psychosocial and behavioral interventions had statistically significant moderate effects on depression [SMD = − 0.73, 95% CI (− 1.01, − 0.45), I2 = 90%] and large effects on anxiety [SMD = − 0.90, 95% CI (− 1.19, − 0.60), I2 = 92%], especially in the general population and COVID-19 survivors. Exercise and cognitive behavioral therapy were found to be the most effective treatments with moderate-to-large effect size for depression and anxiety during the outbreak of COVID-19. We also found the internet-based approach could also achieve almost equally significant effects on depression and anxiety compared with face-to-face traditional approach. Our findings suggest that cognitive behavioral therapy and physical exercise intervention are significantly effective for depression and anxiety related to the COVID-19 pandemic regardless of the delivery modes, and gender differences should be taken into consideration for better implementation of interventions in clinical and community practice.

Telehealth improves follow-up and monitoring of age-related macular degeneration during the COVID-19 pandemic.

To prevent vision loss, it is important to monitor patients with age-related macular degeneration (AMD) for the development of choroidal neovascularization. The coronavirus disease 2019 (COVID-19) pandemic caused many patients to miss or delay visits. To offset those gaps in care, providers utilized telehealth (TH) to evaluate patients for symptoms of disease progression and provide health education on the importance of continuous monitoring. This study evaluates the impact of TH encounters on the rate of return for recommended in-person examinations for 1103 patients with non-neovascular (dry) AMD seen in an outpatient ophthalmology clinic in 2019 and due for return evaluation after the outbreak of COVID-19 in 2020. Logistic regression analysis was used to identify demographic, clinical, and sociomedical factors associated with TH utilization and in-person return. 422 patients (38%) utilized TH during the study period. Patients who completed a TH encounter were more likely to return for an in-person examination as compared with those who did not receive TH (OR: 1.8, CI 95%: 1.4-2.3, P < 0.001). Completing a TH visit was associated with the detection of new wet AMD (OR: 3.3, 95% CI 1.04-10.6, P = 0.043), as well as with an earlier return for those patients who were found to have disease progression (62 ± 54days vs. 100 ± 57days, P = 0.049). Completing a TH visit increased the rate at which patients with dry AMD returned for recommended in-person eye examinations. In many cases, this permitted the earlier detection of wet AMD, which is linked with achieving better outcomes.