FDA approved five-membered ring fused pyrimidine-based derivatives and their biological properties

Pyrimidines-based drugs are one of the most important drugs for novel and recurring viruses, including the coronavirus. This chapter deals with 41 FDA-approved five-membered ring fused pyrimidine-based drugs, their synthetic strategies, and pharmacological activities. © 2023 Elsevier Ltd. All rights reserved.

Evaluation of Dietary Habits in Relation to Covid-19 Mortality Rate Using Machine Learning Techniques

Coronavirus attacks have affected countless countries. The death rates between most countries are increasing day by day, and we have attempted to propose many considerations about the principal problems that cause dangerous infections across the globe. In this work, the dietary patterns of 170 countries are considered to identify correlations between diet practices and death rates, confirmed and recovered cases caused by COVID-19. We have used data from food intake by countries and data associated with the spread of COVID-19 and other health issues that help get new insights into the importance of nutrition and eating habits to combat the spreading of infectious diseases. We have built a machine learning model (regressor) such as ridge regressor, support vector regression, random forest, and XGBoost regressor to predict the mortality rate based on food intake information and Obesity. Two approaches were considered: One with all food-related features taken as parameters and a simpler one, which reduced the dimensionality by using only two features: Animal products and vegetal products. Both have issues (mainly of spread and non-linearity), but we could use different models and metrics. Next, we have built a model to predict obesity rates based on eating habits in each country. The proposed model was far more effective, and the general inclination of the information was taken and anticipated. We have also used data visualization approaches to get better insights into the data considered. © 2022, Success Culture Press. All rights reserved.

Mirrors study: A prospective cohort study assessing the feasibility of robotic interval debulking surgery for advanced-stage ovarian cancer

Introduction/Background MIRRORS (Minimally Invasive Robotic surgery, Role in optimal debulking Ovarian cancer, Recovery & Survival) is the largest prospective cohort study of robotic interval debulking surgery (IDS) in women with advanced-stage epithelial ovarian cancer (EOC) to date. MIRRORS has investigated the feasibility of obtaining consent from women, the acceptability and success of robotic IDS and its impact on short-term surgical outcomes and quality of life. Methodology Eligibility Women with FIGO IIIc-IVb EOC undergoing neoadjuvant chemotherapy and suitable for IDS. Exclusions: pelvic mass >8cm, extensive HPB and/or extensive bowel involvement. Surgery commenced with an initial laparoscopic assessment, for all women recruited, followed by a decision to proceed immediately to robotic or open IDS. Result(s) 23/24 eligible women recruited. Following initial diagnostic laparoscopy, 20 women proceeded directly to robotic IDS, 3 women received open IDS. All patients were debulked with maximal surgical effort to R<1, 39% to R=0. No robotic cases were converted to open. Median EBL for robotic IDS: 50ml, open: 2026ml, median operating time 05:58 robotic vs 05:38 open, length of stay (LOS) 1.5 days robotic vs 6 days open. Bowel resection with stapled anastomosis 15% (3/20), diaphragmatic stripping 60% (12/20), fullthickness diaphragmatic resection 5% (1/20), pelvic peritoneal stripping 70% (14/20). Conclusion MIRRORS has shown significantly enhanced recovery with short LOS, reduced blood loss and reduced HDU/ITU demands, enabling faster re-commencement of chemotherapy in women with FIGO IIIc-IVb EOC. This proved to be greatly beneficial during the COVID-19 pandemic. In experienced hands robotic IDS proved feasible in cases with a pelvic mass up to 8cm. Robotic surgery is not suitable for peritoneal disease covering the anterior abdominal wall close to port sites but does facilitate pelvic and diaphragmatic stripping and arguably provides better visualisation of these peritoneal surfaces in women with high BMI. The planned multicentre MIRRORS-RCT will assess whether robotic IDS offers improved quality of life and recovery with non-inferior progression-free and overall survival. We present the evolution of our surgical technique with illustrative surgical videos and qualitative patient feedback, supported by the objective surgical outcomes for this trial.

Prediction of suitable candidates for covid-19 vaccination

In the current times, COVID-19 has taken a handful of people’s lives. So, vaccination is crucial for everyone to avoid the spread of the disease. How-ever, not every vaccine will be perfect or will get success for everyone. In the pre-sent work, we have analyzed the data from the Vaccine Adverse Event Reporting System and understood that the vaccines given to the people might or might not work considering certain demographic factors like age, gender, and multiple other variables like the state of living, etc. This variable is considered because it explains the unmentioned variables like their food habits and living conditions. The target group for this work will be the healthcare workers, government bodies & medical research organizations. We analyze the data using machine learning techniques & algorithms and predict the working of COVID-19 vaccines on specific age groups developed by significant vaccine manufacturers, i.e., PFIZER \BIONTECH and MODERNA. Data visualization and analysis interpret the vaccine impact based on the above-said variables. It becomes clear that people belonging to a specific demographic factor can have an option to choose the vaccine accordingly based on the previous history of a particular manufacturer’s vaccine getting succeeded for that demographic factor. The various machine learning algorithms we have used are Logistic Regression, Adaboost, Decision Tree, and Random Forest. We have considered the DIED variable as the target variable as this results in a high life threat. On performance measure, perspective Adaboost is showing appreciable values. The prediction of the type of vaccine to be adminis-tered could be derived using this machine learning algorithm. The accuracy we achieved based on the experiment are as follows: Decision Tree Classifier with 97.3%, Logistic Regression with 97.31%, Random Forest with 97.8%, AdaBoost with 98.1%. © 2022, Tech Science Press. All rights reserved.

Coronago website integrated with chatbot for covid-19 tracking

The first cases of a typical pneumonia of unidentified ailment were reported on December 30, 2019, from Wuhan, China. After many researches, severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) is found as the main reason of the ailment and the problem has been named as COVID-19. The rapid spread of this virus resulted in the worldwide pandemic state. This global pandemic has made a devastating impact on several domains like education, business and others. There are many problems that the people are facing in this situation. The medical department staff are facing problem in providing medical assistance to the people in need, providing awareness among the people has become difficult, there are many people who need financial help and the list goes on. As of now, there are some websites and mobile applications to help the people to fight these problems. Here in this work, we are proposing a website incorporated with a healthcare chatbot for assistance & tracking the COVID-19 situation. © 2021 CEUR-WS. All rights reserved.