Comparison of thromboembolic risk scores for evaluating in-hospital events of COVID-19 patients.

Aim: To compare the effectiveness of thromboembolic risk scores in determining in-hospital events of COVID-19 patients. Methods: This retrospective study included a total of 410 consecutive COVID-19 patients. Scores including CHA2DS2-VASc-HS (congestive heart failure, hypertension, age, diabetes mellitus, stroke/transient ischemic attack, vascular disease, sex, hyperlipidemia, smoking); modified R2CHA2DS2-VASc (CHA2DS2-VASc plus renal function), m-ATRIA (modified Anticoagulation and Risk Factors in Atrial Fibrillation score), ATRIA-HSV (ATRIA plus hyperlipidemia, smoking and vascular disease) and modified ATRIA-HSV were calculated. Participants were divided by in-hospital mortality status into two groups: alive and deceased. Results: Ninety-two (22.4%) patients died. Patients in the deceased group were older, predominantly male and had comorbid conditions. CHA2DS2-VASc-HS (adjusted odds ratio [aOR]: 1.31; p = 0.011), m-R2CHA2DS2-VASc (aOR: 1.33; p = 0.007), m-ATRIA (aOR: 1.18; p = 0.026), ATRIA-HSV (aOR: 1.18; p = 0.013) and m-ATRIA-HSV (aOR: 1.24; p = 0.001) scores were all associated with in-hospital mortality. m-R2CHA2DS2-VASc and modified ATRIA-HSV had the best discriminatory performance. Conclusion: We showed that m-R2CHA2DS2-VASc and m-ATRIA-HSV scores were better than the rest in predicting mortality among COVID-19 patients.

COVID-19 continues to be a pandemic that threatens human health all over the world. The main aim of our study was to examine the relationship between risk scores routinely used to determine the probability of clot formation in various cardiovascular diseases and in-hospital deaths of COVID-19 patients. The study comprised 410 adult patients hospitalized with a confirmed diagnosis of COVID-19. The clinical and laboratory data were obtained from the hospital registry system. All risk scores in the study were significantly greater in people who died from COVID-19 than in those who survived. Moreover, scoring systems that include kidney function outperformed the rest in determining in-hospital death. As a result, we discovered that specific risk scores used to indicate a person's likelihood of developing clot formation at a routine cardiology clinic are connected to in-hospital deaths among hospitalized COVID-19 patients.

A comparative study for COVID-19 cases forecasting with loss function as AIC and MSE in RNN family and ARIMA

Forecasting COVID-19 incidents is a trending research study in today's world. Since Machine learning models have been occupied in forecasting recently, this study focus on comparing statical and machine learning models such as ARIMA, RNN, LSTM, Seq2Seq, and Stacked LSTM. The performances were evaluated using two loss functions, namely, AIC and RMSE. The results showed that RNN performs with the lowest RMSE with-49.5% compared with the ARIMA. Seq2Seq scored the highest correlation of determination (R2) with 0.92. © 2022 IEEE.

Predicting Covid-19 Trends Using Machine Learning based on Linear and Multiple Linear Regression Model

The COVID-19 pandemic has adversely affected the health and economy of almost all the countries in the world including India. Almost thousands of people are getting affected by this daily. In this paper, analysis of the daily statistics of people who got affected and this proposed work is going to predict the future trend of the active cases in Odisha and India. Machine Learning based forecasting algorithms have proved their significance in generating predictive outcomes which are used to make decisions on actions that are going to happen in the future. ML algorithms have been using for a long time to do this kind of task. This proposed work is going to do analysis and prediction on the dataset which was created by COVID India organization. Linear and Multiple Linear Regression models are used to predict the future trend of active cases and also the number of active cases in fore coming days and to visualize the trend of future active cases. Here, the performance of Linear and Multiple Linear regression models are compared by using the R2score. Linear and Multiple Linear regression got 0.99 and 1.0 as R2scores respectively which shows that these are the strongest prediction models that are used to predict the future active cases of COVID - 19. Both these models acquired remarkable accuracy in COVID - 19 prediction. A strong correlation factor shows that there is a very strong relationship between a dependent variable (Active cases) and independent variables (positive, deceases, recovered cases). © 2022 University of Stockholm. All rights reserved.

Forecasting the stock price of vaccine manufacturers in China Using Machine Learning and Econometrics Model

Due to the sudden outbreak of COVID-19, there is a high volatility in stock price of vaccine manufacturers in China (Between December 15, 2020 and December 13, 2021, average monthly volatility of these companies is 986). The aim of this paper is to compare the price prediction result of four algorithms: Multivariable Regression Model (MLR), Auto Regressive Integrated Moving Average Model (ARIMA), Back Propagation Neural Network Model (BP-NN), Random Forest Regression (RF), and decide which one has a better performance. Data from December 2020 to December 2021 is collected from Wind and the 8 stocks is selected in leading companies in vaccine industry. It turns out that BP-NN Model gives the best result in predicting stock price of vaccine manufacturers, measured using commonly used indicator, i.e., root-mean-square error (RMSE) and R Square (R2). So next time in the similar situation, BP-NN can be seen as a powerful tool to help us make decision. This paper would help investors build an optimal strategy in selecting stocks in terms of pharmaceutical industry. © 2022 SPIE.

Systematic Analysis and Functional Characterization of R2R3-MYB Genes in Scutellaria baicalensis Georgi.

R2R3-MYB transcription factors participate in multiple critical biological processes, particularly as relates to the regulation of secondary metabolites. The dried root of Scutellaria baicalensis Georgi is a traditional Chinese medicine and possesses various bioactive attributes including anti-inflammation, anti-HIV, and anti-COVID-19 properties due to its flavonoids. In the current study, a total of 95 R2R3-MYB genes were identified in S. baicalensis and classified into 34 subgroups, as supported by similar exon-intron structures and conserved motifs. Among them, 93 R2R3-SbMYBs were mapped onto nine chromosomes. Collinear analysis revealed that segmental duplications were primarily responsible for driving the evolution and expansion of the R2R3-SbMYB gene family. Synteny analyses showed that the ortholog numbers of the R2R3-MYB genes between S. baicalensis and other dicotyledons had a higher proportion compared to that which is found from the monocotyledons. RNA-seq data indicated that the expression patterns of R2R3-SbMYBs in different tissues were different. Quantitative reverse transcriptase-PCR (qRT-PCR) analysis showed that 36 R2R3-SbMYBs from different subgroups exhibited specific expression profiles under various conditions, including hormone stimuli treatments (methyl jasmonate and abscisic acid) and abiotic stresses (drought and cold shock treatments). Further investigation revealed that SbMYB18/32/46/60/70/74 localized in the nucleus, and SbMYB18/32/60/70 possessed transcriptional activation activity, implying their potential roles in the regulatory mechanisms of various biological processes. This study provides a comprehensive understanding of the R2R3-SbMYBs gene family and lays the foundation for further investigation of their biological function.

Development and validation of a paper spray mass spectrometry method for the rapid quantitation of remdesivir and its active metabolite, GS-441524, in human plasma.

Introduction: Remdesivir (GS-5734) is a nucleoside analog prodrug with antiviral activity against several single-stranded RNA viruses, including the novel severe respiratory distress syndrome virus 2 (SARS-CoV-2). It is currently the only FDA-approved antiviral agent for the treatment of individuals with COVID-19 caused by SARS-CoV-2. However, remdesivir pharmacokinetics/pharmacodynamics (PK/PD) and toxicity data in humans are extremely limited. It is imperative that precise analytical methods for the quantification of remdesivir and its active metabolite, GS-441524, are developed for use in further studies. We report, herein, the first validated anti-viral paper spray-mass spectrometry (PS-MS/MS) assay for the quantification of remdesivir and GS-441524 in human plasma. We seek to highlight the utility of PS-MS/MS technology and automation advancements for its potential future use in clinical research and the clinical laboratory setting. Methods: Calibration curves for remdesivir and GS-441524 were created utilizing seven plasma-based calibrants of varying concentrations and two isotopic internal standards of set concentrations. Four plasma-based quality controls were prepared in a similar fashion to the calibrants and utilized for validation. No sample preparation was needed. Briefly, plasma samples were spotted on a paper substrate contained within pre-manufactured plastic cassette plates, and the spots were dried for 1 h. The samples were then analyzed directly for 1.2 min utilizing PS-MS/MS. All experiments were performed on a Thermo Scientific Altis triple quadrupole mass spectrometer utilizing automated technology. Results: The calibration ranges were 20 - 5000 and 100 - 25000 ng/mL for remdesivir and GS-441524, respectively. The calibration curves for the two antiviral agents showed excellent linearity (average R2 = 0.99-1.00). The inter- and intra-day precision (%CV) across validation runs at four QC levels for both analytes was less than 11.2% and accuracy (%bias) was within ± 15%. Plasma calibrant stability was assessed and degradation for the 4 °C and room temperature samples were seen beginning at Day 7. The plasma calibrants were stable at -20 °C. No interference, matrix effects, or carryover was discovered during the validation process. Conclusions: PS-MS/MS represents a useful methodology for rapidly quantifying remdesivir and GS-441524, which may be useful for clinical PK/PD, therapeutic drug monitoring (TDM), and toxicity assessment, particularly during the current COVID-19 pandemic and future viral outbreaks.

Predictions and visualization for confirmed, recovered and deaths COVID-19 cases in Iraq

The 2019-2020 coronavirus pandemic is an emerging infectious disease that has been referred to as the "COVID-19", which results from the coronavirus "SARS-CoV-2" that started in Wuhan, China, in Dec. 2019 and then spread worldwide. In this paper, an attempt for compiling and analyzing the information of the epidemiological outbreaks on "COVID-19" based upon datasets on "2019-nCoV" has been presented. An empirical data analysis with the visualizations was conducted for understanding the numbers of the variety of the cases that have been reported (i.e confirmed, deaths, and recoveries) in and outside of Iraq and carried out a dynamic map visualization of the " COVID-19" expansion in a global manner through the date wise and in Iraq. We an investigation has been carried out as well, which characterized the pandemic effects Iraq and the entire world, with the use of machine learning. A k-nearest neighbor (kNN) model and a linear regression (LR) model have been proposed. This paper included the precise analysis of the confirmed cases, as well as the recovered cases, deaths, predicting the pandemic viral attacks and how far it is expanding in Iraq and the world, the LR model got the highest results, reaching 100 percent. © 2022 Institute of Advanced Engineering and Science. All rights reserved.

Prediction of the COVID-19 pandemic with Machine Learning Models

The latest destructive outbreak, Corona virus (2019), is rapidly sweeping the globe. Not only are economies deteriorating, but countries' entire strengths and confidence are as well. Machine learning forecasting strategies have demonstrated their importance to anticipate in outcomes of the perioperative period to improve the future decision-making actions. The machine learning algorithms have long been used in several applications which require the detection of adverse factors for a threat. Forecasting techniques are essential for producing accurate results. This study shows the ability to predict the number of cases affected by COVID-19 as potential risk to mankind. In this analysis, four-prediction algorithms have been used which are linear regression (LR), Exponential Smoothing (ES), least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM). Each of these models has three different kinds of predictions, such as the newly infected patients, death cases and the recovery cases in the next ten days. These approaches are better used to forecast the covid-19 pandemic, as shown by the findings of analysis. The ES, that is effective in forecasting new corona cases, death cases and recovery cases.

MODELING AND POTENTIAL PROGNOSIS OF THE NUMBER OF CASES COVID-19 PANDEMIC WITH LINEAR, NONLINEAR REGRESSION MODELS AND ARTIFICIAL NEURAL NETWORK MODELS

Coronavirus disease 2019 (COVID-2019) has been identified as a global threat, and many experiments are being performed using various mathematical models to forecast this epidemic's possible evolution. Many of the biggest wealth Economies are stressed because this disease is highly contagious and transmissible. Because of the rise in number of cases and their resulting burden on the government and health care practitioners, some predictive methods for predicting the number of cases in the future will be needed. We evaluated the performance of the linear, non-linear regression and artificial neural network models to forecast the cases reported daily COVID-19 in India 60 days ahead, and the impact of preventive measures such as social isolation, wearing mask and lockdown on COVID-19 spread. Predicting different parameters (number of positive cases, number of cases reported, number of deaths).

Hospital-Acquired SARS-CoV-2 infection among patients admitted to a university hospital.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a public health emergency, as it is a highly contagious disease, health services had to adapt to the high demand for hospitalizations in order to contain hospital outbreaks. We aimed to identify the impact of nosocomial transmission of severe acute respiratory coronavirus virus 2 among inpatients at a university hospital in São Paulo, Brazil. Among 455 inpatients diagnosed with coronavirus disease 2019 in March-May, 2020, nosocomial infection was implicated in 42 (9.2%), of whom 23 (54.7%) died. becoming routine, especially when community transmission occur with high levels of incidence. It was possible to observe with this study that the nosocomial transmission by SARS-CoV-2 was present even with these measures instituted, and some of the damages caused by these infections are intangible.

Building resilience for healthcare professionals working in an Italian red zone during the COVID-19 outbreak: A pilot study.

The COVID-19 pandemic has placed considerable strain on healthcare workers showing high rates of stress and psychological health problems. Interventions are urgently needed to help healthcare workers perform under conditions of great risk and uncertainty. In particular, healthcare leadership is known to be critical to supporting healthcare workers to deal with an uncertain and distressing healthcare environment. This pilot study evaluated the impact of the R2 resilience program tailored for healthcare leaders working in a highly affected COVID-19 area in Italy. Through two group cohorts, 21 healthcare leaders completed the intervention, with 17 participants providing pre- and post-intervention assessment data. Sixty-two staff members who benefitted from their coordinators' resilience-focused leadership were also included in the study. Findings show that participation in R2 was associated with reduction in levels of perceived stress and burnout symptoms, and increases in rugged qualities, self-efficacy and in social-ecological resilience. Significant changes in rugged qualities, self-efficacy and perceived stress were also detected in staff members. High rates of participants' program satisfaction have been detected. R2 is a promising intervention for healthcare professionals working in emergency settings designed to enhance the rugged qualities and resources required to deal with heightened exposure to stress.

Machine Learning Models for covid-19 future forecasting.

Computational methods for machine learning (ML) have shown their meaning for the projection of potential results for informed decisions. Machine learning algorithms have been applied for a long time in many applications requiring the detection of adverse risk factors. This study shows the ability to predict the number of individuals who are affected by the COVID-19[1] as a potential threat to human beings by ML modelling. In this analysis, the risk factors of COVID-19 were exponential smoothing (ES). The Lower Absolute Reductor and Selection Operator, (LASSo), Vector Assistance (SVM), four normal potential forecasts, such as Linear Regression (LR)). [2] Each of these machine-learning models has three distinct kinds of predictions: the number of newly infected COVID 19 people, mortality rates and the recovered COVID-19 estimates in the next 10 days. These approaches are better used in the latest COVID-19 situation, as shown by the findings of the analysis. The LR, that is effective in predicting new cases of corona, death numbers and recovery.