Prediction of carcinogenic human papillomavirus types in cervical cancer from multiparametric magnetic resonance images with machine learning-based radiomics models

PURPOSEThis study aimed to evaluate the potential of machine learning-based models for predicting carcinogenic human papillomavirus (HPV) oncogene types using radiomics features from magnetic resonance imaging (MRI).METHODSPre-treatment MRI images of patients with cervical cancer were collected retrospectively. An HPV DNA oncogene analysis was performed based on cervical biopsy specimens. Radiomics features were extracted from contrast-enhanced T1-weighted images (CE-T1) and T2-weighted images (T2WI). A third feature subset was created as a combined group by concatenating the CE-T1 and T2WI subsets. Feature selection was performed using Pearson’s correlation coefficient and wrapper- based sequential-feature selection. Two models were built with each feature subset, using support vector machine (SVM) and logistic regression (LR) classifiers. The models were validated using a five-fold cross-validation technique and compared using Wilcoxon’s signed rank and Friedman’s tests.RESULTSForty-one patients were enrolled in the study (26 were positive for carcinogenic HPV oncogenes, and 15 were negative). A total of 851 features were extracted from each imaging sequence. After feature selection, 5, 17, and 20 features remained in the CE-T1, T2WI, and combined groups, respectively. The SVM models showed 83%, 95%, and 95% accuracy scores, and the LR models revealed 83%, 81%, and 92.5% accuracy scores in the CE-T1, T2WI, and combined groups, respectively. The SVM algorithm performed better than the LR algorithm in the T2WI feature subset (P = 0.005), and the feature sets in the T2WI and the combined group performed better than CE-T1 in the SVM model (P = 0.033 and 0.006, respectively). The combined group feature subset performed better than T2WI in the LR model (P = 0.023).CONCLUSIONMachine learning-based radiomics models based on pre-treatment MRI can detect carcinogenic HPV status with discriminative accuracy

HUMAN SIDE OF E-COMMERCE IN THE SUB-SAHARAN AFRICAN COUNTRIES

E-commerce is a new way of shopping through using internet since 1990s. This latest from of shopping is based on progress in information technologies. In the 1990s, companies just as Amazon and eBay started to change the shopping behavior of people. In this research, e-commerce index of 44 Sub-Saharan African countries are calculated by using World Development Indicators which are issued by the World Bank Group. The E-commerce index is consisting of access to electricity, cost of business start-up procedures, fixed broadband subscriptions, internet users, mobile cellular subscriptions and secure Internet servers. It is assumed that this combined index shows the readiness of countries for international e-commerce and they are compared with Human Development Index (HDI) to identify a correlation between them. The result shows that there is a moderate positive correlation between e-commerce index and HDI

Using Markov Chains in Prediction of Stock Price Movements: A Study on Automotive Industry

Stock price prediction is on the agenda of most researchers based on the uncertainty in its nature. In past two decades, the literature on the development of prediction models for stock prices has extended dramatically. These studies mostly focused on specific industries such as banking and finance, petroleum, manufacturing, and automotive. In line with prior studies, the aim of this study is also to investigate the efficiency of Markov Chains Model, which is one of the most commonly applied models, in predicting the stock price movements for the firms operating in automotive industry and to reveal the possible contribution it can make to the decision making process of investors. Automotive industry is not only a major and industrial force worldwide, but also is a locomotive power that serves to many other industries. Thus, this study considers the firms operating in automotive industry and daily closing stock prices of all 13 automotive companies are collected for the calendar year of 2015. By defining three possible states (decrease, increase, and no change), individual state transition probability matrixes are formed for each company. Then, using the probabilities provided with these matrixes, different investment strategies are evaluated for the first five working days of 2016. According to the results of analysis, it is concluded that applying Markov Chains generates a positive income or at least minimizes the loss

Antineutrophil Cytoplasmic Antibody-Associated Vasculitis and COVID-19: The Clinical Course and Prognosis of 15 Patients From a Tertiary Care Center.

The aim of this study was to evaluate incidence rates, prognoses, and disease-related factors associated with poor outcomes in patients with antineutrophil cytoplasmic antibody-associated vasculitis (AAV) who had coronavirus disease (COVID-19). METHODS: Patients with AAV were questioned for a history of COVID-19 in the outpatient setting. Cumulative clinical findings and treatment history were obtained from the patients' medical records. The clinical, laboratory, and imaging findings of inpatients with COVID-19 were recorded. The data of patients who developed symptomatic COVID-19 and/or died of the disease were used for comparison. RESULTS: Eighty-nine patients (47.2% female; mean age, 56 ± 12.5 years) were included. The diagnosis was granulomatosis with polyangiitis in 56 patients (62.9%) and microscopic polyangiitis in 33 (37.1%). Sixty-one (68.2%) and 21 patients (23.6%) had renal and peripheral nerve involvement, respectively. Ten patients had a history of diffuse alveolar hemorrhage. Fifteen patients (16.9%) had COVID-19, including 9 (60%) with severe pneumonia. Twelve patients (85.7%) were hospitalized, 6 (42.9%) were admitted to the intensive care unit, and 5 (35.7%) died. All deceased patients had hypogammaglobulinemia (IgG levels <700 mg/dL) during hospital admission. Symptomatic COVID-19 was associated with higher disease activity, glucocorticoid and rituximab treatments, and glomerular filtration rate <30 mL/min. A history of peripheral nerve involvement, higher organ damage scores, and hypogammaglobulinemia was associated with mortality. CONCLUSIONS: The prognosis was poor in our patients with AAV who had COVID-19, especially those with severe multisystem involvement. Hypogammaglobulinemia was associated with mortality. Serum IgG level monitoring in patients with AAV would be beneficial during the COVID-19 pandemic