Teachers' Perceptions of Videoconferencing and Smartphones in a Retrospective Analysis 2009-2020: An Educational Social Internet of Things Framework

Schools have become increasingly influenced by what has been called the Social Internet of Things (SIoT). As many schools transitioned to online teaching and learning in 2020 because of the COVID-19 pandemic, there is a need to analyze data on technology adoption in schools for changes and continuity since the inception of increasingly popular smartphones since 2007 that has affected videoconferencing, blended learning, and the flipped classroom concept. In this study, videoconference technology is the main factor analyzed in relation to IoT and schools in which handheld devices are considered gateways to further integration of schools into an IoT framework. The researchers analyze a selection of a 2009 dataset and a 2019 dataset from the National Center for Education Statistics (United States) to discuss teachers' perceptions of technology, especially the use of handheld devices and videoconference technology. Recommendations for educator preparation programs (EPPs) are made based on the discussion of the results of this analysis in context with the events of 2020 for why EPPs could emphasize flipped classroom design models within an SIoT framework. © 2023 IEEE.

Donepezil-associated survival benefits among Alzheimer's disease patients are retained but not enhanced during COVID-19 infections.

Background and Aim: Donepezil is a front-line treatment for Alzheimer's disease. Donepezil treatment is associated with decreased risk of all-cause mortality. Specific protection is observed in pneumonia and cardiovascular disease. We hypothesized that donepezil treatment would improve mortality among Alzheimer's patients following infection with COVID-19. The objective of this study is to assess the influence of ongoing donepezil treatment on survival in Alzheimer's disease patients after polymerase chain reaction (PCR)-confirmed COVID-19 infection. Methods: This is a retrospective cohort study. We conducted a national survey of Veterans with Alzheimer's disease to assess the influence of ongoing donepezil treatment on survival in Alzheimer's disease patients after PCR-confirmed COVID-19 infection. We assessed all-cause 30-day mortality stratified by COVID-19 infection and donepezil use, estimating odds ratios using multivariate logistic regression. Results: Among people with Alzheimer's disease and COVID-19, all-cause 30-day mortality was 29% (47/163) for people taking donepezil compared with 38% (159/419) for those who were not. Among people with Alzheimer's disease without COVID-19, all-cause 30-day mortality was 5% (189/4189) for people taking donepezil compared with 7% (712/10,241) for those who were not. Adjusting for covariates, the decrease in mortality associated with donepezil did not differ between people with and without COVID-19 (interaction p = 0.710). Conclusion: The known survival benefits of donepezil were retained but not found to be specific to COVID-19 among people with Alzheimer's disease.

Analysis of the Features of Sustainable Development of the Mercosur Countries in the Context of the Covid-19 Pandemic

Purpose: The purpose of this article is to identify the features of sustainable development of the MERCOSUR countries in the context of the COVID-19 pandemic. Design/methodology/approach: The authors use comparative and retrospective analysis to identify the distinguishing characteristics of countries meeting the sustainable development goals. The objects of research are the MERCOSUR countries. Findings: It has been established that Uruguay is the undisputed leader in sustainable development in the context of COVID-19. Two countries: Brazil and Venezuela slowed down the pace of implementation of national sustainable development strategies due to the pandemic and other reasons. Originality/value: According to the results of the analysis, it was revealed that countries that have long-term national strategies for sustainable development are more stable in achieving sustainable development goals. The size of a national economy does not guarantee that it can successfully overcome an external shock such as the lockdown caused by the COVID-19 pandemic. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Pre-existing autoimmunity is associated with increased severity of COVID-19: A retrospective cohort study using data from the National COVID Cohort Collaborative (N3C).

BACKGROUND: Identifying individuals with a higher risk of developing severe COVID-19 outcomes will inform targeted or more intensive clinical monitoring and management. To date, there is mixed evidence regarding the impact of pre-existing autoimmune disease (AID) diagnosis and/or immunosuppressant (IS) exposure on developing severe COVID-19 outcomes. METHODS: A retrospective cohort of adults diagnosed with COVID-19 was created in the National COVID Cohort Collaborative enclave. Two outcomes, life-threatening disease, and hospitalization were evaluated by using logistic regression models with and without adjustment for demographics and comorbidities. RESULTS: Of the 2,453,799 adults diagnosed with COVID-19, 191,520 (7.81%) had a pre-existing AID diagnosis and 278,095 (11.33%) had a pre-existing IS exposure. Logistic regression models adjusted for demographics and comorbidities demonstrated that individuals with a pre-existing AID (OR = 1.13, 95% CI 1.09 - 1.17; P< 0.001), IS (OR= 1.27, 95% CI 1.24 - 1.30; P< 0.001), or both (OR = 1.35, 95% CI 1.29 - 1.40; P< 0.001) were more likely to have a life-threatening COVID-19 disease. These results were consistent when evaluating hospitalization. A sensitivity analysis evaluating specific IS revealed that TNF inhibitors were protective against life-threatening disease (OR = 0.80, 95% CI 0.66- 0.96; P=0.017) and hospitalization (OR = 0.80, 95% CI 0.73 - 0.89; P< 0.001). CONCLUSIONS: Patients with pre-existing AID, exposure to IS, or both are more likely to have a life-threatening disease or hospitalization. These patients may thus require tailored monitoring and preventative measures to minimize negative consequences of COVID-19.

Digital Transformation Strategies, Practices, and Trends: A Large-Scale Retrospective Study Based on Machine Learning

The purpose of this research is to identify the areas of interest, research topics, and application areas that reflect the research nature of digital transformation (DT), as well as the strategies, practices, and trends of DT. To accomplish this, the Latent Dirichlet allocation algorithm, a probabilistic topic modeling technique, was applied to 5350 peer-reviewed journal articles on DT published in the last ten years, from 2013 to 2022. The analysis resulted in the discovery of 34 topics. These topics were classified, and a systematic taxonomy for DT was presented, including four sub-categories: implementation, technology, process, and human. As a result of time-based trend analysis, "Sustainable Energy”, "DT in Health”, "E-Government”, "DT in Education”, and "Supply Chain” emerged as top topics with an increasing trend. Our findings indicate that research interests are focused on specific applications of digital transformation in industrial and public settings. Based on our findings, we anticipate that the next phase of DT research and practice will concentrate on specific DT applications in government, health, education, and economics. "Sustainable Energy” and "Supply Chain” have been identified as the most prominent topics in current DT processes and applications. This study can help researchers and practitioners in the field by providing insights and implications about the evolution and applications of DT. Our findings are intended to serve as a guide for DT in understanding current research gaps and potential future research topics.

Impact of Diabetes on Inpatient Length of Stay in Adult Mental Health Services in a Community Hospital Setting: A Retrospective Cohort Study.

OBJECTIVE: Our aim in this study was to characterise the relationship between comorbid mental health diagnosis and diabetes type on inpatient length of stay (LOS). METHODS: This retrospective, chart review study was conducted at a community hospital in Ontario, Canada. Individuals admitted to the inpatient mental health unit with a reported diagnosis of type 1 or type 2 diabetes were included in the analysis. Relevant data related to mental health conditions at admission and LOS were collected from the electronic health record. Analyses of variance and coviariance were used to determine the impact on LOS. RESULTS: A total of 249 encounters were included in the analyses. Overall, individuals with type 2 diabetes (mean, 14.70 days; standard deviation, 15.75 days) had a significantly longer LOS than individuals with type 1 diabetes (mean, 8.01 days; standard deviation, 7.27 days). Upon including sociodemographic factors, individuals older in age and with a most recent admission of <1 year also had a longer LOS. There was no significant relationship between the most responsible mental health diagnosis and LOS. CONCLUSIONS: Individuals with type 2 diabetes may be more likely to have a longer LOS in inpatient mental health settings than those with type 1 diabetes. More dedicated support would be beneficial for this population. Future work should focus on in-depth exploration of the challenges that lead to the observed LOS.

Comparative Analysis Of Myoma And Adenomyosis Surgical Intervention Rates In Women With Covid-19

In gynecology, uterine myoma and adenomyosis are among the most common diseases. We can observe a significant increase in the detection of uterine myoma and adenomyosis and their complications after the coronavirus pandemic. The study was conducted in the department of gynecology III multidisciplinary clinic TMA for the period 2018 - 2022, the case histories of patients admitted for inpatient treatment for uterine myoma and adenomyosis were studied. A retrospective analysis of pre-covid and post-covid periods was performed. Thus, the results of the study showed that the rate of operative treatment for uterine myoma was 48.4% of all the operations performed during the study period. Over the last 2 years (the pandemic period) there was a 1.3-fold increase in the frequency of radical organ-assisted operations. Frequent complicated forms of myoma and adenomyosis after covid is a cause of bleeding and an indication for the operative treatment. Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.

Response to the COVID-19 Pandemic in Classrooms at the University of the Basque Country through a User-Informed Natural Ventilation Demonstrator.

The COVID-19 pandemic has generated a renewed interest in indoor air quality to limit viral spread. In the case of educational spaces, due to the high concentration of people and the fact that most of the existing buildings do not have any mechanical ventilation system, the different administrations have established natural ventilation protocols to guarantee an air quality that reduces risk of contagion by the SARS-CoV-2 virus after the return to the classrooms. Many of the initial protocols established a ventilation pattern that opted for continuous or intermittent ventilation to varying degrees of intensity. This study, carried out on a university campus in Spain, analyses the performance of natural ventilation activated through the information provided by monitoring and visualisation of real-time data. In order to carry out this analysis, a experiment was set up where a preliminary study of ventilation without providing information to the users was carried out, which was then compared with the result of providing live feedback to the occupants of two classrooms and an administration office in different periods of 2020, 2021 and 2022. In the administration office, a CO2-concentration-based method was applied retrospectively to assess the risk of airborne infection. This experience has served as a basis to establish a route for user-informed improvement of air quality in educational spaces in general through low-cost systems that allow a rational use of natural ventilation while helping maintain an adequate compromise between IAQ, comfort and energy consumption, without having to resort to mechanical ventilation systems.

Dynamics of Morbidity and Etiological Structure of Acute Respiratory Infections on the Eve and in the First Year of COVID-19 in the Irkutsk Region

Relevance. At the beginning of the pandemic, there was regional variability in incidence rates not only for COVID-19, but also for other acute respiratory infections of the upper and lower respiratory tract (ARIs). The first cases of COVID-19 in the Irkutsk region were detected two months later than the first cases in Russia, and at the beginning of the epidemic, the disease slowly spread among the local population. A retrospective analysis of ARIs monitoring is necessary to improve epidemiological surveillance. The aim of the work is to identify annual and seasonal changes in the incidence of ARIs and the circulation of respiratory viruses in the Irkutsk region before and during the first months of the epidemic of a new coronavirus infection. Materials and methods. A retrospective descriptive study was conducted based on the results of regional monitoring for 2017–2020. The analysis included records of acute infections of the upper (J06) and lower (J20-J22) respiratory tract, influenza (J10, J11), community-acquired pneumonia (J12-J16, J18) and COVID-19 (U07.1, U07.2), as well as the results of a study of 5.5 thousand samples from patients with ARIs. Respiratory viruses (n = 10) were detected using reagent kits for polymerase chain reaction (PCR) «AmpliSense® Influenza virus A/B-FL», «AmpliSense®ORVI-screen-Fl» (Russia). The distribution of incidence rates and PCR results was analyzed by years, epidemic seasons, calendar weeks, and age groups. Results. In 2020, the incidence of COVID-19 was 3180 per 100,000 population, or 7% of all cases of ARIs of the upper and lower respiratory tract (∑ARIs). In 2020, according to epidemiological service, the number of cases of acute respiratory infections decreased by 25.7%, but the number of cases of community-acquired pneumonia (CAP) increased by 83.2% (p < 0.001) compared to 2019. The incidence of CAP reached 1400 per 100,000 population. The proportion of children in the structure of CAP incidence decreased from 39.4% to 12.6% (p < 0.001), and the proportion of adults increased from 60.6% to 87.3% (p < 0.001). These changes were most pronounced during the «second wave» of COVID-19 at the end of 2020. The share of CAP in ∑ARIs on average for 2017-2020 was 2.62% (CI 2.56–2.68).This indicator was exceeded at the end of 2020 during the COVID-19 epidemic – 8.08 (CI 8.07–8.09), and also at the beginning of 2019 during the influenza epidemic – 2.83% (CI 2.81–2.85). In 2019–2020 an increase in the frequency of positive tests for human coronaviruses (hCovs) up to 2.1–2.3% was registered, compared with 0.7–0.9% in 2017–2018 (p<0.05). Rhinoviruses continued to circulate actively during the COVID-19 epidemic. A significant correspondence between the frequency of detection of viruses and the share of CAP in ∑ARIs was established only for influenza viruses (χ2 = 26.2, p < 0.01). For other viruses, the relationship is not statistically significant (p > 0.05). Conclusions. The start of the COVID-19 epidemic was accompanied by significant changes in the incidence of ARIs, although in 2020 the number of registered cases did not exceed 10% of ∑ARIs. The increase in the proportion of CAP in ∑ARIs and the change in age risk groups deserve special attention. The epidemiological analysis does not confirm the hypothesis of a possible spread of SARS-Cov2 in the Irkutsk region before 2020. © 2022, Numikom. All rights reserved.

Identification of the first COVID-19 infections in the US using a retrospective analysis (REMEDID).

Accurate detection of early COVID-19 cases is crucial to reduce infections and deaths, however, it remains a challenge. Here, we used the results from a seroprevalence study in 50 US states to apply our Retrospective Methodology to Estimate Daily Infections from Deaths (REMEDID) with the aim of analyzing the initial spread of SARS-CoV-2 infections across the US. Our analysis revealed that the virus likely entered the country through California on December 28, 2019, which corresponds to 16 days prior to the officially recognized entry date established by the Centers of Disease Control and Prevention. Furthermore, the REMEDID algorithm provides evidence that SARS-CoV-2 entered, on average, a month earlier than previously reflected in official data for each US state. Collectively, our mathematical modeling provides more accurate estimates of the initial COVID-19 cases in the US, and has the ability to be extrapolated to other countries and used to retrospectively track the progress of the pandemic. The use of approaches such as REMEDID are highly recommended to better understand the early stages of an outbreak, which will enable health authorities to improve mitigation and preventive measures in the future.

Retrospective analysis of controversial topics on COVID-19 in Japan

For efficient policy-making, a thorough recognition of controversial topics is crucial because the cost of unmitigated controversies would be extremely high for society. However, identifying controversial topics is costly. In this paper, we proposed a framework to search for controversial topics comprehensively. We then conducted a retrospective analysis of the controversial topics of COVID-19 with data obtained via Twitter in Japan as a case study of the framework. The results show that the proposed framework can effectively detect controversial topics that reflect current reality. Controversial topics tend to be about the government, medical matters, economy, and education;moreover, the controversy score had a low correlation with the traditional indicators-scale and sentiment of the topics-which suggests that the controversy score is a potentially important indicator to be obtained. We also discussed the difference between highly controversial topics and less controversial ones despite their large scale and sentiment. © 2021 ACM.

Safety and clinical outcomes of remdesivir in hospitalised COVID-19 patients: a retrospective analysis of active surveillance database.

BACKGROUND: Real-world data on safety and clinical outcomes of remdesivir in COVID-19 management is scant. We present findings of data analysis conducted for assessing the safety and clinical outcomes of remdesivir treatment for COVID-19 in India. METHODS: This retrospective analysis used data from an active surveillance programme database of hospitalised patients with COVID-19 who were receiving remdesivir. RESULTS: Of the 2329 patients included, 67.40% were men. Diabetes (29.69%) and hypertension (20.33%) were the most common comorbidities. At remdesivir initiation, 2272 (97.55%) patients were receiving oxygen therapy. Remdesivir was administered for 5 days in 65.38% of patients. Antibiotics (64.90%) and steroids (47.90%) were the most common concomitant medications. Remdesivir was overall well tolerated, and total 119 adverse events were reported; most common were nausea and vomiting in 45.40% and increased liver enzymes in 14.28% patients. 84% of patients were cured/improved, 6.77% died and 9.16% showed no improvement in their clinical status at data collection. Subgroup analyses showed that the mortality rate was significantly lower in patients < 60 years old than in those > 60 years old. Amongst patients on oxygen therapy, the cure/improvement rate was significantly higher in those receiving standard low-flow oxygen than in those receiving mechanical ventilation, non-invasive ventilation, or high-flow oxygen. Factors that were associated with higher mortality were age > 60 years, cardiac disease, diabetes high flow oxygen, non-invasive ventilation and mechanical ventilation. CONCLUSION: Our analysis showed that remdesivir is well tolerated and has an acceptable safety profile. The clinical outcome of cure/improvement was 84%, with a higher improvement in patients < 60 years old and on standard low-flow oxygen.

During the COVID-19 Epidemic: Recommendations for the Admission and Treatment of Patients With Ovarian Cancer.

Background: The coronavirus disease 2019 (COVID-19) had become a health care event endangering humans globally. It takes up a large number of healthcare resources. We studied the impact of COVID-19 on patients with ovarian cancer by comprehensively analyzing their admissions before and after the epidemic, and made reasonable suggestions to improve their current situation. Methods: We randomly divided the enrolled patients into three groups, PreCOVID-19 Group (PCG) (2019.8.20-2020.1.20), COVID-19 Group (CG) (2020.1.21-2020.6.14), and Secondary Outbreak COVID-19 Group (SOCG) (2020.6.15-2020.10.10). One-way ANOVA and chi-square test were used for analysis. Results: The number of patients from other provinces decreased significantly (p < 0.05). The total hospital stay during the epidemic was substantially more extended (p < 0.05). Before the epidemic, our department performed more open surgery while during the epidemic outbreak, we tended to choose laparoscopy (p < 0.01). We took a longer surgery time (P < 0.05). Patients had significantly less post-operative fever during the epidemic (p < 0.001). Conclusion: During the COVID-19 epidemic, no patient was infected with COVID-19, and no patient experienced severe post-operative complications. We recommend maintaining the admissions of patients with ovarian cancer during the epidemic following the rules: 1. The outpatients must complete a nucleic acid test and chest CT in the outpatient clinic; 2. Maintain full daily disinfection of the ward and insist that health care workers disinfect their hands after contact with patients; 3. Increase the use of minimally invasive procedures, including laparoscopy and robotics; 4. Disinfect the ward twice a day with UV light and sodium hypochlorite disinfectant; 5. Patients need to undergo at least three nucleic acid tests before entering the operating room.

Model-based ensembles: Lessons learned from retrospective analysis of COVID-19 infection forecasts across 10 countries.

Mathematical models of different types and data intensities are highly used by researchers, epidemiologists, and national authorities to explore the inherently unpredictable progression of COVID-19, including the effects of different non-pharmaceutical interventions. Regardless of model complexity, forecasts of future COVID-19 infections, deaths and hospitalization are associated with large uncertainties, and critically depend on the quality of the training data, and in particular how well the recorded national or regional numbers of infections, deaths and recoveries reflect the the actual situation. In turn, this depends on, e.g., local test and abatement strategies, treatment capacities and available technologies. Other influencing factors including temperature and humidity, which are suggested by several authors to affect the spread of COVID-19 in some countries, are generally only considered by the most complex models and further serve to inflate the uncertainty. Here we use comparative and retrospective analyses to illuminate the aggregated effect of these systematic biases on ensemble-based model forecasts. We compare the actual progression of active infections across ten of the most affected countries in the world until late November 2020 with "re-forecasts" produced by two of the most commonly used model types: (i) a compartment-type, susceptible-infected-removed (SIR) model; and (ii) a statistical (Holt-Winters) time series model. We specifically examine the sensitivity of the model parameters, estimated systematically from different subsets of the data and thereby different time windows, to illustrate the associated implications for short- to medium-term forecasting and for probabilistic projections based on (single) model ensembles as inspired by, e.g., weather forecasting and climate research. Our findings portray considerable variations in forecasting skill in between the ten countries and demonstrate that individual model predictions are highly sensitive to parameter assumptions. Significant skill is generally only confirmed for short-term forecasts (up to a few weeks) with some variation across locations and periods.

Recognition of Long-COVID-19 Patients in a Canadian Tertiary Hospital Setting: A Retrospective Analysis of Their Clinical and Laboratory Characteristics.

A proportion of patients with COVID-19 have symptoms past the acute disease phase, which may affect quality of life. It is important for clinicians to be aware of this "long-COVID-19" syndrome to better diagnose, treat, and prevent it. We reviewed clinical and laboratory characteristics of a COVID-19 cohort in a Toronto, Ontario tertiary care center. Demographic, clinical, and laboratory data were collected, and patients were classified as "long-COVID-19" or "non-long-COVID-19" using consensus criteria. Of 397 patients who tested positive for COVID-19, 223 met inclusion criteria, and 62 (27%) had long-COVID-19. These patients had a similar age distribution compared to non-long-COVID-19 patients overall but were younger in the admitted long COVID-19 group. The long-COVID-19 group had more inpatients compared to the non-long-COVID-19 group (39% vs. 25%) and more frequent supplemental oxygen or mechanical ventilation use. However, long-COVID-19 patients did not differ by duration of mechanical ventilation, length of stay, comorbidities, or values of common laboratory tests ordered. The most frequent symptoms associated with long-COVID-19 were fatigue and weakness, as reported most commonly by the infectious disease, respirology and cardiology disciplines. In conclusion, by retrospective chart review, 27% of COVID-19 patients presenting to a tertiary care center in Toronto, Canada, were found to meet criteria for long-COVID-19. Past medical history and routine laboratory testing at presentation did not predict for long-COVID-19 development.

Catching the second wave: clinical characteristics and nosocomial infection rates in major trauma and orthopaedic patients during the COVID-19 pandemic.

AIMS: The new COVID-19 variant was reported by the authorities of the UK to the World Health Organization (WHO) on 14 December 2020. We aim to describe the clinical characteristics and nosocomial infection rates in major trauma and orthopaedic patients comparing the first and second wave of COVID-19 infection. METHODS: A retrospective analysis of a prospectively collected trauma database was reviewed at a level 1 major trauma centre from 1 December 2020 to 18 February 2021 looking at demographics, clinical characteristics, and nosocomial infections and compared to our previously published first wave data (26 January 2020 to 14 April 2020). RESULTS: From 1 December 2020 to 18 February 2021, 522 major trauma patients were identified with a mean age of 54.6 years, and 53.4% (n = 279) were male. Common admissions were falls (318; 60.9%) and road traffic accidents (RTAs; 71 (13.6%); 262 of these patients (50.2%) had surgery. In all, 75 patients (14.4%) tested positive for COVID-19, of which 51 (68%) were nosocomial. Surgery on COVID-19 patients increased to 46 (61.3%) in the second wave compared to 13 (33.3%) in the first wave (p = 0.005). ICU admissions of patients with COVID-19 infection increased from two (5.1%) to 16 (20.5%), respectively (p = 0.024). Second wave mortality was 6.1% (n = 32) compared to first wave of 4.7% (n = 31). Cardiovascular (CV) disease (35.9%; n = 14); p = 0.027) and dementia (17.9%; n = 7); p = 0.030) were less in second wave than the first. Overall, 13 patients (25.5%) were Black, Asian and Minority ethnic (BAME), and five (9.8%) had a BMI > 30 kg/m2. The mean time from admission to diagnosis of COVID-19 was 13.9 days (3 to 44). Overall, 12/75 (16%) of all COVID-19 patients died. CONCLUSION: During the second wave, COVID-19 infected three-times more patients. There were double the number of operative cases, and quadruple the cases of ICU admissions. The patients were younger with less dementia and CV disease with lower mortality. Concomitant COVID-19 and the necessity of major trauma surgery showed 13% mortality in the second wave compared with 15.4% in the first wave. In contrast to the literature, we showed a high percentage of nosocomial infection, normal BMI, and limited BAME infections. Cite this article: Bone Jt Open 2021;2(8):661-670.

Elective Surgical Experience During COVID Pandemic at a Tertiary Cancer Care Centre in India: A Retrospective Analysis.

To analyse and identify possible outcomes of elective cancer surgeries performed at a tertiary cancer centre during COVID19 pandemic. This is a retrospective study including patients that underwent surgery at HCG Manavata cancer centre, Nashik, Maharashtra, India, from 15 March 2020 to 15 June 2020. Among the 458 patients that underwent elective surgeries, 54% were male and 46% were female, with a median age of 50.57 years. The most common sites of cancer distribution were head and neck (24.67%), colorectal (11.57%), gynaecological (11.35%), and breast (10.26%). Of the included patients, 92% were of American Society of Anaesthesiologists (ASA) II with comorbidities such as hypertension, and 64% underwent major surgeries with a mortality rate of 1.52% (n = 7). Average duration of surgery and hospital stay was observed to be 168.43 min and 4.4 days, respectively. Post-operatively, 7 patients were tested COVID positive and their recovery was uneventful. Despite the difficulty that set in because of COVID19 pandemic, it was proven from our study that elective cancer care surgeries can be successfully performed by following all the set guidelines.

The clinical characteristics and underlying causes of liver damage in patients with COVID-19 infection: Retrospective analysis.

OBJECTIVES: 2019-nCoV has become a global threat to human health. The primary objective of this study was to examine the hepatic damage in 2019-nCoV infected patients and the associated underlying causes. METHODS: In this retrospective study, a total of 68 laboratory-confirmed and 20 suspected COVID-19 cases from 23rd January 2020 to 15th February 2020 were included. The study was conducted in The First People's Hospital of Jingzhou, Hubei. SPSS version 23.0. was used for Statistical analysis using the Student's t-test or Chi-square test. Data was analyzed for the clinical characteristics and underlying causes of liver damage. The outcomes were followed up until March 29, 2020. RESULTS: Out of the 68 COVID-19 confirmed cases, 51 had an abnormal liver function, of which 15 had an abnormal liver function at the time of hospital admission. The relationship between the liver function and clinical prognosis of patients showed that the abnormal liver function was positively correlated with the severity of the infection (100% vs.70.2%, p=0.036). The proportion of patients with an elevated level of ALT and a depleted level of Albumin (ALB) were significantly lower in the COVID-19 suspected group than the confirmed group (5% vs. 50.9%, p=0.000; 10% vs. 35.8%, p=0.030, respectively). Besides, the utilization rate of lopinavir/ritonavir, azithromycin, and methylprednisolone in COVID-19 suspected patients were significantly lower than the confirmed patients (25% vs. 62.3%, p=0.004; 35% vs. 62.3%, p=0.037; 25% vs. 64.2%, p=0.003, respectively). CONCLUSIONS: Liver function anomalies are one of the common symptoms associated with the COVID-19 infection, where virus-replication in the liver cells, virus-induced inflammatory response, and administration of clinical medication could be the plausible reason.

Fine-Scale Space-Time Cluster Detection of COVID-19 in Mainland China Using Retrospective Analysis.

Exploring spatio-temporal patterns of disease incidence can help to identify areas of significantly elevated or decreased risk, providing potential etiologic clues. The study uses the retrospective analysis of space-time scan statistic to detect the clusters of COVID-19 in mainland China with a different maximum clustering radius at the family-level based on case dates of onset. The results show that the detected clusters vary with the clustering radius. Forty-three space-time clusters were detected with a maximum clustering radius of 100 km and 88 clusters with a maximum clustering radius of 10 km from 2 December 2019 to 20 June 2020. Using a smaller clustering radius may identify finer clusters. Hubei has the most clusters regardless of scale. In addition, most of the clusters were generated in February. That indicates China's COVID-19 epidemic prevention and control strategy is effective, and they have successfully prevented the virus from spreading from Hubei to other provinces over time. Well-developed provinces or cities, which have larger populations and developed transportation networks, are more likely to generate space-time clusters. The analysis based on the data of cases from onset may detect the start times of clusters seven days earlier than similar research based on diagnosis dates. Our analysis of space-time clustering based on the data of cases on the family-level can be reproduced in other countries that are still seriously affected by the epidemic such as the USA, India, and Brazil, thus providing them with more precise signals of clustering.

Clinical characteristics and related risk factors of disease severity in 101 COVID-19 patients hospitalized in Wuhan, China.

Coronavirus disease 2019 (COVID-19) broke out in December 2019. Due its high morbility and mortality, it is necessary to summarize the clinical characteristics of COVID-19 patients to provide more theoretical basis for future treatment. In the current study, we conducted a retrospective analysis of the clinical characteristics of COVID-19 patients and explored the risk factors for the severity of illness. A total of 101 COVID-19 patients hospitalized in Leishenshan Hospital (Wuhan, China) was classified into three sub-types: moderate (n = 47), severe (n = 36), and critical (n = 18); their clinical data were collected from the Electronic Medical Record. We showed that among the 101 COVID-19 patients, the median age was 62 years (IQR 51-74); 50 (49.5%) patients were accompanied by hypertension, while 25 (24.8%) and 22 (21.8%) patients suffered from diabetes and heart diseases, respectively, with complications. All patients were from Wuhan who had a definite history of exposure to the epidemic area. Multivariate logistic regression analysis revealed that older age, diabetes, chronic liver disease, percentage of neutrophils (N%) > 75%, CRP > 4 mg/L, D-dimer > 0.55 mg/L, IL-2R > 710 U/mL, IL-8 > 62 pg/mL, and IL-10 > 9.1 pg/mL were independent variables associated with severe COVID-19. In conclusion, we have identified the independent risk factors for the severity of COVID-19 pneumonia, including older age, diabetes, chronic liver disease, higher levels of N%, CRP, D-dimer, IL-2R, IL-8, and IL-10, providing evidence for more accurate risk prediction.