关键词： Quantitative CT   Machine learning   COVID-19  

摘要： Rationale and objectives This study aims to evaluate the effectiveness of integrating clinical data and quantitative CT parameters with machine learning techniques in forecasting the short-term outcomes of severe COVID-19 in elderly patients. Materials and methods In this retrospective study, we analyzed the clinical profiles and chest quantitative CT parameters of 239 elderly patients with severe COVID-19 admitted for treatment. The cohort included 61 deceased patients (death group) and 178 who recovered and were discharged (survival group). The participants were randomly assigned into a training group (n = 167) and a validation group (n = 72). Quantitative CT parameters were measured using the 3D-Slicer software. Univariate and multivariate logistic regression analyses identified independent risk factors for mortality. Predictive models were developed employing four machine learning algorithms: Logistic Regression (LR), Random Forest (RF), Decision Tree (DT), and Support Vector Machine (SVM). Results Both univariate and multivariate logistic regression analyses revealed age, hypersensitive C-reactive protein (hs-CRP), and solid organ volume percentage (SOV%) as independent predictors of mortality. The Area Under the Curve (AUC) values for the LR, RF, DT, and SVM models in the training group were 0.795, 0.726, 0.854, and 0.589, respectively; for the validation group, they were 0.817, 0.634, 0.869, and 0.754, respectively. The DT algorithm outperformed other models in both the training and validation groups, emerging as the most effective predictive model in this study. Conclusion The combination of clinical data and quantitative CT parameters with machine learning approaches is highly valuable in predicting the short-term prognosis of severe COVID-19 in the elderly. Among the various models tested, the Decision Tree algorithm-based model proved to be the most accurate and reliable in this context.

关键词： COVID-19   Diabetes mellitus   Pandemic   Bibliometric analysis   CiteSpace   Visualization analysis  

摘要： Background & objective The extensive spread of Coronavirus disease 2019 (COVID-19) worldwide has caused a dramatic negative impact on many individuals' health. This study aims to systematically and comprehensively analyze the current status and possible future directions of diabetes mellitus (DM) and COVID-19 research. Methods We obtained publications about COVID-19 and DM from the Web of Science Core Collection (WoSCC) using the search terms "COVID-19″ and similar terms combined with "DM" and similar terms, with a date range of January 2020 to May 2024. And we used CiteSpace V 6.3.R2 to perform the bibliometric visualization analysis. Results The search enrolled 6266 publications. The USA is a country with the most publications; Harvard University was the most productive institution in this field. The highest-ranked journal was the PLOS ONE, and the most cited journal was Lancet. The 20 most cited journals have all been cited 28754 times, accounting for 28 % of the total cites; the range of those journals was 790–3197. Publications on COVID-19 and DM research exhibited a distinct trajectory, shifting from an initial emphasis on understanding the impact of diabetes on COVID-19 infection and its associated pathophysiological mechanisms to a focus on analyzing the differential responses of diverse patient populations. Subsequently, research has progressed to examine the effects of medications and vaccines, as well as the long-term consequences of COVID-19 in diabetic individuals. Throughout this research endeavor, the exploration of diverse therapeutic interventions, their efficacy, and ultimate outcomes have consistently remained a paramount focus. And " metabolic syndrome," " long COVID," and " gestational diabetes" are still likely to be the hotspots and frontiers of research in the future. Conclusions This bibliometric analysis related to DM in COVID-19 illuminates the current research situation and developmental trends, supporting researchers in the exploration o

关键词： COVID-19   Diabetes mellitus   Hypoalbuminemia   ICU admission   Predictors of severity  

摘要： Introduction To elucidate factors associated with severe disease in patients with coronavirus disease 2019 (COVID-19) pneumonia complicated with diabetes mellitus. Methods Among patients with COVID-19 pneumonia hospitalized from January to September 2021, 100 patients with diabetes were included in the study. Patients in the intensive care unit admission or in-hospital death category were defined as the severe group (26 patients), and the other patients as the moderate group (74 patients). Comparisons were made between the two groups. Medical histories were extracted from the medical records, and statistical analysis was performed. Results Patients were 62.1 ± 13.0 years of age, 70 % male, 48 % aged 65 years and more, and HbA1c was 7.72 ± 1.51 %. The severe group had significantly higher blood glucose level, CRP, WBC, and LDH on admission. Serum albumin level was significantly lower in the severe group and was significantly negatively correlated with number of days on oxygen administration ( r  = 0.447) and maximal oxygen concentration ( r  = 0.561). Multiple logistic regression analysis with severity of illness as the objective variable showed that high CRP and low albumin levels on admission, and older age were independently associated. Conclusions Low albumin levels on admission may be an indicator of severe disease in patients with diabetes.

关键词： COVID-19   Fake news   Community analysis   Twitter   Disseminator   Misinformation   Social network analysis   Deep learning   Machine learning  

摘要： The alarming growth of misinformation on social media has become a global concern as it influences public opinions and compromises social, political, and public health development. The proliferation of deceptive information has resulted in widespread confusion, societal disturbances, and significant consequences for matters pertaining to health. Throughout the COVID-19 pandemic, there was a substantial surge in the dissemination of inaccurate or deceptive information via social media platforms, particularly X (formerly known as Twitter), resulting in the phenomenon commonly referred to as an “Infodemic”. This review paper examines a grand selection of 600 articles published in the past five years and focuses on conducting a thorough analysis of 87 studies that investigate the detection of fake news connected to COVID-19 on Twitter. In addition, this research explores the algorithmic techniques and methodologies used to investigate the individuals responsible for disseminating this type of fake news. A summary of common datasets, along with their fundamental qualities, for detecting fake news has been included as well. For the purpose of identifying fake news, the behavioral pattern of the misinformation spreaders, and their community analysis, we have performed an in-depth examination of the most recent literature that the researchers have worked with and recommended. Our key findings can be summarized in a few points: (a) around 80% of fake news detection-related papers have utilized Deep Neural Networks-based techniques for better performance achievement, although the proposed models suffer from overfitting, vanishing gradients, and higher prediction time problems, (b) around 60% of the disseminator related analysis papers focus on identifying dominant spreaders and their communities utilizing graph modeling although there is not much work done in this domain, and finally, (c) we conclude by pointing out a wide range of research gaps, for example, the need of a la

关键词： COVID-19 pandemic   Students   Mental health   Resilience   Psychological adaptation   Focus group   Qualitative content analysis  

摘要： Purpose University students have been one of the most affected groups worldwide during the COVID-19 pandemic, having experienced increased stress, anxiety, and depression. Resilience is reported as the most predictive factor in decreasing the impact of the threats of the COVID-19 pandemic. However, university students’ resilience during the COVID-19 pandemic has not received much attention; this is a research gap that this study aimed to address. Methods Seventeen 2nd- and 4th-year South Korean students who had experienced at least one year of university life during the COVID-19 pandemic participated in this study between June and July 2021. This study involved three focus-group interviews that were recorded and transcribed verbatim, and data were analyzed using qualitative content analysis. Results Eight conceptual categories emerged from the three main themes distilled from participants’ responses: 1) factors impeding resilience: experience of loss, experience of a dangerous situation, expansion of uncertainty, and accumulated stress; 2) strategies to strengthen resilience: change and innovation, loss and confrontation, and transcendence; and 3) outcomes from strengthening resilience: adapting to the changing world (“the new normal”). Conclusion s: This study found several factors that impede resilience and strategies to further support resilience. Universities and relevant community organizations can use these findings to develop educational programs and interventions to strengthen resilience in university students, helping them to better cope with future challenges and overcome adversity.

关键词： Ozone therapy   Major autohemotherapy   post -acute sequelae of COVID-19   Pulmonary function   Inflammation   Cellular immunity   OXIDATIVE STRESS   FOLLOW-UP   THERAPY   HEART   DYSFUNCTION   STATEMENT   FATIGUE   PASC  

摘要： This prospective, randomized, controlled clinical trial assessed the therapeutic effects of major ozone autohemotherapy (O3-MAH) in patients with post-acute sequelae of COVID-19 (PASC). Seventy-three eligible participants were randomly assigned to an O3-MAH plus conventional therapy group (n = 35) or a conventional therapy alone group (n = 38). Symptom score, pulmonary function, 6-minute walk distance (6MWD), and hematological, biochemical, and immunological parameters were evaluated before and after the interventions. Both groups demonstrated improvements in various parameters post-intervention, but efficacy was greater in the O3-MAH group than the conventional treatment group; with intervention effectiveness defined as a >= 50 % reduction in symptom score, 25 of 35 patients (71 %) responded to O3-MAH, while 17/38 patients (45 %) responded to conventional treatment alone (P = 0.0325). Significant improvements in symptom scores (P = 0.0478), tidal volume (P = 0.0374), predicted 6MWD (P = 0.0032), and coagulation and inflammatory indicators were noted in the O3-MAH group compared with the conventional treatment group. O3-MAH was more likely to be effective in patients with elevated CRP levels. Furthermore, O3-MAH markedly improved cellular immunity, and this improvement became more pronounced with extended treatment duration. In summary, combining O3-MAH with conventional treatment was more effective than conventional therapy alone in improving symptoms, pulmonary function, inflammation, coagulation, and cellular immunity in patients with PASC. Further research is now warranted to validate these findings and individualize the regimen.

关键词： COVID-19   SARS-CoV-2   Amubarvimab   Romlusevimab   Antibody therapy   Intensive care Units  

摘要： Amubarvimab-romlusevimab is a commonly recommended antiviral treatment in China for adult patients with mild or moderate SARS-CoV-2 infections, especially for patients with a high risk factor for progression to severe COVID-19. However, its exact efficacy in patients with severe Covid-19 is not yet *** is a single-center retrospective cohort study, in which we collected the general data, laboratory tests, radiological characteristics, viral conversion status, and prognosis of the disease from patients with COVID-19 hospitalized, from December 2022 to March 2023 in the Department of Critical Care Medicine. The amubarvimab-romlusevimab therapy can reduce the 28-day mortality (29.79 % vs 51.35 %, p = 0.02), and ICU mortality (29.79 % vs 55.41 %, p = 0.006) of severe COVID-19.A 1:1 PSM (Propensity Score Matching) was performed to reduce bias, in order to ensure the two groups were balanced and comparable. In the matched population (n = 47), there were no statistically significant differences between the mAbs (monoclonal antibody)group and the Non-antiviral group in 28-day, and thromboembolic events in COVID-19 patients. The 40-day survival analysis shows that mAbs therapy can improve patient prognosis (HR = 0.45, 95%CI = 0.26–0.76, p = 0.008). However, no significant intergroup difference in the 40-day cumulative viral conversion rate. In a univariate Cox regression analysis, The Amubarvimab - romlusevimab therapy(HR:0.464; CI:[0.252–0.853]; p:0.013) is a protective factor and CRP, PCT, PLT, Lactate, PT, PT-INR, and pt% level at admission were risk factors for clinical prognosis. After including the above covariates, Multifactorial COX regression shows that the Amubarvimab - romlusevimab therapy(HR:0.392; CI:[0.211–0.729]; p:0.003), CRP, Lactate and PT-INR at admission are independent factors for mortality of severe COVID-19. Based on the current data, we conclude that amubarvimab-romlusevimab therapy is beneficial for patients with severe COVID-19.

关键词： Kavya-Manoharan   Progressive type-II censoring   Statistical inference   Bayes prediction   COVID-19 datasets   62F15   62N01   62N05  

摘要： This research proposes the Kavya-Manoharan Unit Exponentiated Half Logistic (KM-UEHL) distribution as a novel tool for epidemiological modeling of COVID-19 data. Specifically designed to analyze data constrained to the unit interval, the KM-UEHL distribution builds upon the unit exponentiated half logistic model, making it suitable for various data from COVID-19. The paper emphasizes the KM-UEHL distribution's adaptability by examining its density and hazard rate functions. Its effectiveness is demonstrated in handling the diverse nature of COVID-19 data through these functions. Key characteristics like moments, quantile functions, stress-strength reliability, and entropy measures are also comprehensively investigated. Furthermore, the KM-UEHL distribution is employed for forecasting future COVID-19 data under a progressive Type-II censoring scheme, which acknowledges the time-dependent nature of data collection during outbreaks. The paper presents various methods for constructing prediction intervals for future-order statistics, including maximum likelihood estimation, Bayesian inference (both point and interval estimates), and upper-order statistics approaches. The Metropolis-Hastings and Gibbs sampling procedures are combined to create the Markov chain Monte Carlo simulations because it is mathematically difficult to acquire closed-form solutions for the posterior density function in the Bayesian framework. The theoretical developments are validated with numerical simulations, and the practical applicability of the KM-UEHL distribution is showcased using real-world COVID-19 datasets.

关键词： Economic uncertainty   operating cycle   COVID-19   responses to crises   >   EARNINGS   GROWTH  

摘要： This paper investigates the impact of COVID-19-related disruptions on firms' operating cycles. Demand shocks and supply chain disruptions can have unexpected outcomes on operating cycles, especially on receivables and inventories. We find that the COVID-19 pandemic caused a significant increase in firms' operating cycle lengths. Further, we separate the operating cycle into its components and find that firms experienced an increase in both receivables and inventories; this implies longer times to collect receivables and slower-moving inventories. Furthermore, we find that firms with more resources - larger firms and higher cash flows - experienced smaller increases in operating cycles. Moreover, while firms experienced a sharp drop in profitability after COVID-19, an increase in operating cycle exacerbated the drop in profitability even more. We contribute to the literature by showing how the aftermath of COVID-19 impacted firms' operating cycles.

关键词： RESOURCE  

摘要： COVID-19 casualties vary among different ancestral groups due to a variety of factors. Here, we present a protocol for analyzing publicly available genomewide association studies (GWASs) to search for ancestry-specific genetic factors related to severe COVID-19. We describe steps for downloading and comparing two COVID-19 GWASs, calculating expression quantitative trait loci, and single- cell gene expression analysis. We demonstrate this approach using GWASs from Host Genetics Initiative; however, it is applicable to other databases such as the UK Biobank. For complete details on the use and execution of this protocol, please refer to Cheng et al.1 1