The rise of Endobronchial Ultrasound (EBUS) and fall of Mediastinoscopy

Introduction: For over 50 years mediastinoscopy was considered the gold-standard to sample the mediastinum. The introduction of EBUS-TBNA (endobronchial ultrasound - transbronchial needle aspiration) revolutionised the diagnosis and staging of lung cancer and the diagnosis of metastases and investigation of isolated mediastinal and hilar lymphadenopathy. Objective(s): We aim to examine the relation between the number of EBUS and mediastinoscopy procedures performed in a large regional Cancer Alliance. Methodology A retrospective observational study of the number of EBUS and Mediastinoscopy procedures performed in the Greater Manchester Cancer Alliance between 2011-2020. Result(s): The number of EBUS procedures performed increased annually, from 362 in 2013 to a peak of 1,660 in 2019. There was a decline in in 2020 owing to the COVID-19 pandemic but this has recovered again in 2021 (n=1565). The number of mediastinoscopies was inversely proportional and declined on an annual basis with 179 performed in 2011, reducing to 21 in 2020 Conclusion(s): EBUS is now a widely available and highly adopted procedure for sampling the mediastinum. The number of procedures performed has increased on an annual basis and led to a reduction in the need for undertaking mediastinoscopies. The less invasive nature of EBUS has likely lowered the threshold for sampling the mediastinum to investigate benign conditions such as tuberculosis and sarcoidosis.

Evaluation of the prediction of CoVID-19 recovered and unrecovered cases using symptoms and patient's meta data based on support vector machine, neural network, CHAID and QUEST Models.

OBJECTIVE: This paper aims to develop four prediction models for recovered and unrecovered cases using descriptive data of patients and symptoms of CoVID-19 patients. The developed prediction models aim to extract the important variables in predicting recovered cases by using the binary values for recovered cases. MATERIALS AND METHODS: The data were collected from different countries all over the world. The input of the prediction model contains 28 symptoms and four variables of the patient's information. Symptoms of COVID-19 include a high fever, low fever, sore throat, cough, and so on, where patient metadata includes Province, county, sex, and age. The dataset contains 1254 patients with 664 recovered cases. To develop prediction models, four models are used including neural network, support vector machine, CHAID, and QUEST models. To develop prediction models, the dataset is divided into train and test datasets with splitting ratios equal to 70%, and 30%, respectively. RESULTS: The results showed that the neural network model is the most effective model in developing COVID-19 prediction with the highest performance metrics using train and test datasets. The results found that recovered cases are associated with the place of the patients mainly, province of the patient. Besides the results showed that high fever is not strongly associated with recovered cases, where cough and low fever are strongly associated with recovered cases. In addition, the country, sex, and age of the patients have higher importance than other patient's symptoms in COVID-19 development. CONCLUSIONS: The results revealed that the prediction models of the recovered COVID-19 cases can be effectively predicted using patient characteristics and symptoms, besides the neural network model is the most effective model to create a COVID -19 prediction model. Finally, the research provides empirical evidence that recovered cases of COVID-19 are closely related to patients' provinces.

Impact of COVID-19 pandemic virus on G8 countries’ financial indices based on artificial neural network

Purpose: The COVID-19 pandemic virus has affected the largest economies around the world, especially Group 8 and Group 20. The increasing numbers of confirmed and deceased cases of the COVID-19 pandemic worldwide are causing instability in stock indices every day. These changes resulted in the G8 suffering major losses due to the spread of the pandemic. This paper aims to study the impact of COVID-19 events using country lockdown announcement on the most important stock indices in G8 by using seven lockdown variables. To find the impact of the COVID-19 virus on G8, a correlation analysis and an artificial neural network model are adopted. Design/methodology/approach: In this study, a Pearson correlation is used to study the strength of lockdown variables on international indices, where neural network is used to build a prediction model that can estimate the movement of stock markets independently. The neural network used two performance metrics including R2 and mean square error (MSE). Findings: The results of stock indices prediction showed that R2 values of all G8 are between 0.979 and 0.990, where MSE values are between 54 and 604. The results showed that the COVID-19 events had a strong negative impact on stock movement, with the lowest point on the March of all G8 indices. Besides, the US lockdown and interest rate changes are the most affected by the G8 stock trading, followed by Germany, France and the UK. Originality/value: The study has used artificial intelligent neural network to study the impact of US lockdown, decrease the interest rate in the USA and the announce of lockdown in different G8 countries. © 2021, Emerald Publishing Limited.

CoVID-19 symptoms analysis of deceased and recovered cases using Chi-square test.

This paper aims to show the relationship between COVID-19 symptoms and patients' status including recovered and deceased cases. The study uses different CoVID-19 patients' information from different countries, the dataset contains 13174 patients with 730 as recovered and 34 cases as deceased. The Chi-square test is adopted with asymptotic significance level to show the strength of each symptom on recovered and deceased cases independently. The study found that the recovered cases are associated with different symptoms based on the patient history, where the deceased cases showed that high fever is not responsible for increasing the number of deceased cases. In addition, the use of symptoms will not give evidence of the patients' status, and therefore gender, age, reason of infection and patients' province are more dominant in determining the status of patients.

Is visiting Qom spread CoVID-19 epidemic in the Middle East?

The CoVID-19 epidemic started in Wuhan, China and spread to 217 other countries around the world through direct contact with patients, goods transfer, animal transport, and touching unclean surfaces. In the Middle East, the first confirmed case in both Iran and UAE originated from China. A series of infections since those confirmed cases started in the Middle East originated from Qom, Iran, and other Shi'ite holy places. Thereafter, CoVID-19 has been transmitted to other countries in the Middle East. This report aims to trace all of the confirmed cases in the Middle East until March 6, 2020 and their further spread. This report proves that further transmission of CoVID-19 to the Middle East was because of human mobility, besides engaging in different Jewish and Shi'ite religious rites. This report suggests avoiding several religious rites, closing the borders of infected countries, and supporting the infected countries to prevent further transmission.

Are Italy and Iran really suffering from COVID-19 epidemic? A controversial study.

The number of global COVID-19 infected cases is increased rapidly to exceed 370 thousand. COVID-19 is transmitted between humans through direct contact and touching dirty surfaces. This paper aims to find the similarity between DNA sequences of COVID-19 in different countries, and to compare these sequences with three different diseases [HIV, Hand-Foot-Mouth disease (HFMD), and Cryptococcus]. The study used pairwise distance, maximum likelihood tree, and similarity between amino acid to find the results. The results showed that different three main types of viruses namely, COVID-19 are found. The virus in both Italy and Iran is not similar to COVID-19 in China and USA. While, two viruses were spread in Wuhan (before and after December 26, 2019). Besides Cryptococcus and HFMD are found as dominant diseases with Group 1 and Group 3, respectively. Authors claim that the current virus in Italy and Iran that killed thousands of people is not COVID-19 based on the available data.

The correlation between the spread of COVID-19 infections and weather variables in 30 Chinese provinces and the impact of Chinese government mitigation plans.

On February 1, 2020, China announced a novel coronavirus CoVID-19 outbreak to the public. CoVID-19 was classified as an epidemic by the World Health Organization (WHO). Although the disease was discovered and concentrated in Hubei Province, China, it was exported to all of the other Chinese provinces and spread globally. As of this writing, all plans have failed to contain the novel coronavirus disease, and it has continued to spread to the rest of the world. This study aimed to explore and interpret the effect of environmental and metrological variables on the spread of coronavirus disease in 30 provinces in China, as well as to investigate the impact of new China regulations and plans to mitigate further spread of infections. This article forecasts the size of the disease spreading based on time series forecasting. The growing size of CoVID-19 in China for the next 210 days is estimated by predicting the expected confirmed and recovered cases. The results revealed that weather conditions largely influence the spread of coronavirus in most of the Chinese provinces. This study has determined that increasing temperature and short-wave radiation would positively increase the number of confirmed cases, mortality rate, and recovered cases. The findings of this study agree with the results of our previous study.

A classifier prediction model to predict the status of Coronavirus COVID-19 patients in South Korea.

OBJECTIVE: Coronavirus COVID-19 further transmitted to several countries globally. The status of the infected cases can be determined basing on the treatment process along with several other factors. This research aims to build a classifier prediction model to predict the status of recovered and death coronavirus CovID-19 patients in South Korea. MATERIALS AND METHODS: Artificial neural network principle is used to classify the collected data between February 20, 2020 and March 9, 2020. The proposed classifier used different seven variables, namely, country, infection reason, sex, group, confirmation date, birth year, and region. The most effective variables on recovered and fatal cases are analyzed based on the neural network model. RESULTS: The results found that the proposed predictive classifier efficiently predicted recovered and death cases. Besides, it is found that discovering the infection reason would increase the probability to recover the patient. This indicates that the virus might be controllable based on infection reasons. In addition, the earlier discovery of the disease affords better control and a higher probability of being recovered. CONCLUSIONS: Our recommendation is to use this model to predict the status of the patients globally.