Effect of various cardiovascular risk factors on oxidative stress markers in post menopausal women

Background: The objectives was to study the association between oxidative stress and various cardiovascular risk factors individually and also there cumulative effect in post-menopausal women.Methods: 50 postmenopausal women with cardiovascular risk factors like hyperglycemia, hypertension, high Body Mass Index and Hyperlipidaemia were selected and burden of various cardiovascular risk factors in them is noted and also compared with 50 age matched apparently healthy post menopausal controls. Malon-di-aldehyde (MDA), vitaminE and vitamin C were taken to assess oxidative stress status. ANOVA was applied to find the effect of individual risk factor on oxidative stress and Student’s t-test was applied to compare between cases with single risk factors and multiple risk factors.Results: It was found that though all cardiovascular risk factors increase oxidative stress significantly but none of them has significant association in comparison to others (F value 0.37, 0.88 and 0.62 for MDA, vitamin E and C respectively). However, MDA value found in cases with multiple risk factors when compared with that of cases with single risk factor was found to be statistically significant (P <0.001). Similarly, the decrease in vitamin E in cases with multiple risk factors when compared with single risk factor cases was found to be significant. (P <0.01) and vitamin C in women with multiple risk factors was decreased in comparison to women with a single risk factor and was significant (P <0.001).Conclusions: The study shows that all the risk factors are equally responsible for increase in oxidative stress and multiple risk factors increase the oxidative stress significantly in comparison to any single risk factor

Forecasting Stock Market Indices Using Gated Recurrent Unit (GRU) Based Ensemble Models: LSTM-GRU

A time sequence analysis is a particular method for looking at a group of data points gathered over a long period of time. Instead of merely randomly or infrequently, time series analyzers gather information from data points over a predetermined length of time at scheduled times. But this kind of research requires more than just accumulating data over time. Data in time series may be analyzed to illustrate how variables change over time, which makes them different from other types of data. To put it another way, time is a crucial element since it demonstrates how the data changes over the period of the information and the outcomes. It offers a predetermined architecture of data dependencies as well as an extra data source. Time Series forecasting is a crucial field in deep learning because many forecasting issues have a temporal component. A time series is a collection of observations that are made sequentially across time. In this study, we examine distinct machine learning, deep learning and ensemble model algorithms to predict Nike stock price. We are going to use the Nike stock price data from January 2006 to January 2018 and make predictions accordingly. The outcome demonstrates that the hybrid LSTM-GRU model outperformed the other models in terms of performance

Empirical Forecasting Analysis of Bitcoin Prices: A Comparison of Machine learning, Deep learning, and Ensemble learning Models

Bitcoin has drawn a lot of interest recently as a possible high-earning investment. There are significant financial risks associated with its erratic price volatility. Therefore, investors and decision-makers place great significance on being able to precisely foresee and capture shifting patterns in the Bitcoin market. However, empirical studies on the systems that support Bitcoin trading and forecasting are still in their infancy. The suggested method will predict the prices of all key cryptocurrencies with accuracy. A number of factors are going to be taken into account in order to precisely predict the pricing. By leveraging encryption technology, cryptocurrencies may serve as an online accounting framework and a medium of exchange. The main goal of this work is to predict Bitcoin price. To address the drawbacks of traditional forecasting techniques, we use a variety of machine learning, deep learning, and ensemble learning algorithms. We conduct a performance analysis of Auto-Regressive Integrated Moving Averages (ARIMA), Long-Short-Term Memory (LSTM), FB-Prophet, XGBoost, and a pair of hybrid formulations, LSTM-GRU and LSTM-1D_CNN. Utilizing historical Bitcoin data from 2012 to 2020, we compared the models with their Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The hybrid LSTM-GRU model outperforms the rest with a Mean Absolute Error (MAE) of 0.464 and a Root Mean Squared Error (RMSE) of 0.323. The finding has significant ramifications for market analysts and investors in digital currencies

Deep Learning-based Gated Recurrent Unit Approach to Stock Market Forecasting: An Analysis of Intel\u27s Stock Data

The stock price index prediction is a very challenging task that\u27s because the market has a very complicated nonlinear movement system. This fluctuation is influenced by many different factors. Multiple examples demonstrate the suitability of Machine Learning (ML) models like Neural Network algorithms (NN) and Long Short-Term Memory (LSTM) for such time series predictions, as well as how frequently they produce satisfactory outcomes. However, relatively few studies have employed robust feature engineering sequence models to forecast future prices. In this paper, we propose a cutting-edge stock price prediction model based on a Deep Learning (DL) technique. We chose the stock data for Intel, the firm with one of the quickest growths in the past ten years. The experimental results demonstrate that, for predicting this particular stock time series, our suggested model outperforms the current Gated Recurrent Unit (GRU) model. Our prediction approach reduces inaccuracy by taking into account the random nature of data on a big scale

Credit Card Fraud Detection Using Logistic Regression and Synthetic Minority Oversampling Technique (SMOTE) Approach

Financial fraud is a serious threat that is expanding effects on the financial sector. The use of credit cards is growing as digitization and internet transactions advance daily. The most common issues in today\u27s culture are credit card scams. This kind of fraud typically happens when someone uses someone else\u27s credit card details. Credit card fraud detection uses transaction data attributes to identify credit card fraud, which can save significant financial losses and affluence the burden on the police. The detection of credit card fraud has three difficulties: uneven data, an abundance of unseen variables, and the selection of an appropriate threshold to improve the models\u27 reliability. This study employs a modified Logistic Regression (LR) model to detect credit card fraud in order to get over the preceding difficulties. The dataset sampling strategy, variable choice, and detection methods employed all have a significant impact on the effectiveness of fraud detection in credit card transactions. The effectiveness of naive bayes, k-nearest neighbour, and logistic regression on highly skewed credit card fraud data is examined in this research. The accuracy of the logistic regression technique will be closer to 0.98%; with this accuracy, frauds may be easily detected. The fact that LR receives the highest classifier score illustrates how well LR predicts credit card theft

Cryptocurrency fraud detection through classification techniques

Ethereum and its native cryptocurrency, Ether, have played a worthy attention in the development of the blockchain and cryptocurrency space. Its programmability and smart contract capabilities have made it a foundational platform for decentralized applications and innovations across various industries. Because of its anonymous and decentralized structure, the hotheaded expansion of cryptocurrencies in the payment space has created both enormous potential and concerns related to cybercrime, including money laundering, financing terrorism, illegal and dangerous services. As more financial institutions attempt to integrate cryptocurrencies into their networks, there is an increasing need to create a more transparent network that can withstand these kinds of attacks. In this work, we are using different classification techniques, such as logistic regression (LR), random forest (RF), k-nearest neighbors (KNN), adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost) for Ethereum fraud detection. The dataset we are using includes rows of legitimate transactions done using the cryptocurrency Ethereum as well as known fraudulent transactions. The “XGBoost” model, which is noteworthy, detects variations that might attract notice and prevent potential issues in this chore

A rare case of intra-renal paraganglioma in a child masquerading as renal cell carcinoma

Background: Renal cell carcinomas (RCCs) are rare in children, accounting for around 2% of all pediatric renal tumors. Paragangliomas are extra-adrenal locations of phaeochromocytomas. They originate from chromaffin cells arising along the sympathetic paraganglia and are secretory in most cases. Sequential imaging is often required to ascertain the etiology of a renal mass; ultrasound (USG), contrast-enhanced computerized tomography scan (CECT), and magnetic resonance imaging (MRI). Tissue diagnosis is confirmatory. Clinical Description: An 11-year-old girl presented with a right-sided abdominal and flank pain that was dull aching and nonradiating. She had no history of jaundice, hematuria, dysuria, bowel symptoms, sweating, palpitations, or syncope. The vitals were stable, without tachycardia or hypertension. No abnormal findings were found on clinical examination. Initially, the possibility of a renal stone was considered. Management and Outcome: Baseline blood tests were normal. Abdominal USG detected a heterogeneous mass in the right kidney. CECT ascertained that it was very vascular and exhibited contrast enhancement, suggesting a renal tumor. MRI showed that the right renal artery was acting as the feeding vessel to the tumor. RCC was suspected based on imaging. The vascular nature prevented us from performing a Tru-cut biopsy. A right-sided nephrectomy was planned, preceded by angiography and embolization of the right renal artery to reduce vascularity. Intraoperative episodes of hypertension were noted. Gross appearance suggested RCC; however, histopathology revealed evidence of an intrarenal PGL. Conclusions: Diagnosing a nonfunctional PGL in an asymptomatic patient is challenging and may only be possible by intraoperative histopathology

Biliary cystadenoma in a child: A rare entity

Biliary cystadenoma, a rare potentially malignant hepatic cystic lesion, is characterized by multiloculations and septations. It is common in middle-aged females (about 5% of nonparasitic liver cysts); only 12 cases are described in children. We report a rare case of hepatic biliary cystadenoma in a 3-year-old girl, with a gradually increasing lump in the right upper abdomen. Complete excision with a healthy liver margin was done

A self-associating hepatitis B surface antigen-derived peptide that is immunogenic in alum

We previously described an oligomeric synthetic peptide derived from the hepatitis B surface antigen that displayed a limited tendency to form self-associating macromolecular structures in solution. Here it is demonstrated that amino-terminal myristylation of this peptide results in near quantitative aggregation of the oligomeric peptide. The myristylated peptide is highly immunogenic when used in conjunction with alum as adjuvant in both the rabbit and rhesus monkey models. The antibody response generated by peptide also cross-reacted with native antigen and was long-lasting. Collectively the results described in this and previous reports offer an attractive new approach for generating immunogenic peptide mimetics of conformational epitopes that may find application as vaccines

Germination and Graft Compatibilty Study of Wild Solanum spp and Brinjal Root Stocks with Tomato Scions

The experiment was conducted at Central Horticultural Experiment Station, ICAR- IIHR, Aiginia, Bhubaneswar, Odisha, India during kharif seasons of 2018 in collaboration with Odisha University of Agriculture and Technology, Bhubaneswar, to study the seed germination, days taken to reach grafting stage and grafting success (percentage) of rootstock and scion seeds used for grafting. The cultivated and wild species of brinjal and tomato were used as rootstocks with tomato Arka Rakshak F1 hybrid as scion. The experiment was conducted with 13 root stocks following statistical design CRD with three replications in pro trays to find out better root stock for grafting. A significant difference was recorded for the rootstock and scion parameters. Utkal Anushree had taken minimum number of days (6.67 days) for germination and the wild Solanum torvum had taken maximum number of days (16.67 days) to germinate, On the other hand, Arka Rakshak took least number of days (34.33 days) to reach grafting stage while the wild Solanum torvum reached to grafting stage within 56 days. The grafting success percentage ranged from 74.67 to 96 percent. Maximum grafting success (%) was recorded in Solanum torvum whereas minimum grafting success (%) was observed in S. sisymbriifolium wild solanum spp. root stock