Variance estimation using memory type estimators based on EWMA statistic for time scaled surveys in stratified sampling.

In this article, we have proposed memory-type exponential and non-exponential estimators for population variance based on exponentially weighted moving average (EWMA) statistic in stratified sampling. We drive mathematical expressions for mean square errors of the proposed estimators using Taylor and exponential expansions. Our analysis demonstrates that the proposed memory-type estimators outperform the conventional estimators under stratification, particularly, when the information of previous sample is utilized.

Radiogenomics Map-Based Molecular and Imaging Phenotypical Characterization in Localised Prostate Cancer Using Pre-Biopsy Biparametric MR Imaging.

To create a radiogenomics map and evaluate the correlation between molecular and imaging phenotypes in localized prostate cancer (PCa), using radical prostatectomy histopathology as a reference standard. Radiomic features were extracted from T2-weighted (T2WI) and Apparent Diffusion Coefficient (ADC) images of clinically localized PCa patients (n = 15) across different Gleason score-based risk categories. DNA extraction was performed on formalin-fixed, paraffin-embedded (FFPE) samples.

Predictive accuracy of boosted regression model in estimating risk of venous thromboembolism following minimally invasive radical surgery in pharmacological prophylaxis-naïve men with prostate cancer.

Background: Venous thromboembolism (VTE) is a potentially life-threatening but preventable complication after urological surgery. Physicians are faced with the challenges of weighing the risks and benefits of thromboprophylaxis given scanty evidence for or against and practice variation worldwide.

Objective: The primary objective of the study was to explore the possibility of a risk-stratified approach for thromboembolism prophylaxis following radical prostatectomy.

Biased proportional hazard regression estimator in the existence of collinearity.

This paper proposed a new biased proportional hazard regression (PHR) estimator which is the combination of elastic net proportional hazard regression (ENPHR) and principal components proportional hazard regression (PCPHR) estimator. Comparison of proposed estimator with ENPHR, PCPHR, ridge PHR, lasso PHR, class PHR and maximum likelihood (ML) estimators is done in terms of scalar mean square error (MSE). Simulation study is conducted to examine the performance of each estimator.

Differential Expression of Serum Proinflammatory Cytokine TNF-α and Genetic Determinants of TNF-α, CYP2C19*17, miR-423 Genes and Their Effect on Coronary Artery Disease Predisposition and Progression.

Coronary artery disease (CAD) is the leading cause of death and hospitalization worldwide and represents a problem for public health systems everywhere. In Saudi Arabia, the prevalence of CAD is estimated to be 5.5%.

In Silico Investigation of AKT2 Gene and Protein Abnormalities Reveals Potential Association with Insulin Resistance and Type 2 Diabetes.

Type 2 diabetes (T2D) develops from insulin resistance (IR) and the dysfunction of pancreatic beta cells. The AKT2 protein is very important for the protein signaling pathway, and the non-synonymous SNP (nsSNPs) in AKT2 gene may be associated with T2D. nsSNPs can result in alterations in protein stability, enzymatic activity, or binding specificity.

Comprehensive Molecular Evaluation of HNF-1 Alpha, miR-27a, and miR-146 Gene Variants and Their Link with Predisposition and Progression in Type 2 Diabetes Patients.

Background: Type 2 diabetes (T2D) is a metabolic condition induced by insulin resistance and pancreatic beta cell dysfunction. MicroRNAs (miRNAs) have biological significance because they regulate processes such as the molecular signaling pathways involved in the pathophysiology of diabetes mellitus. The hepatocyte nuclear factor-1 alpha (HNF-1 alpha) is a transcription factor found in hepatocytes and the pancreas.

Radiogenomics Reveals Correlation between Quantitative Texture Radiomic Features of Biparametric MRI and Hypoxia-Related Gene Expression in Men with Localised Prostate Cancer.

Objectives: To perform multiscale correlation analysis between quantitative texture feature phenotypes of pre-biopsy biparametric MRI (bpMRI) and targeted sequence-based RNA expression for hypoxia-related genes.

Materials And Methods: Images from pre-biopsy 3T bpMRI scans in clinically localised PCa patients of various risk categories (n = 15) were used to extract textural features. The genomic landscape of hypoxia-related gene expression was obtained using post-radical prostatectomy tissue for targeted RNA expression profiling using the TempO-sequence method.

Prediction of Clinically Significant Cancer Using Radiomics Features of Pre-Biopsy of Multiparametric MRI in Men Suspected of Prostate Cancer.

Background: Texture features based on the spatial relationship of pixels, known as the gray-level co-occurrence matrix (GLCM), may play an important role in providing the accurate classification of suspected prostate cancer. The purpose of this study was to use quantitative imaging parameters of pre-biopsy multiparametric magnetic resonance imaging (mpMRI) for the prediction of clinically significant prostate cancer.

Methods: This was a prospective study, recruiting 200 men suspected of having prostate cancer.

The Role of Twitter Medium in Business with Regression Analysis and Statistical Modelling.

Marketing refers to the strategies a company undertakes to promote its brands to its potential audience. Advertising provides useful venues for marketing to promote a company's survives/goods to the audience. It has a positive impact on the sale of services or products.

Decision-Making for the Lifetime Performance Index.

The purpose of this research is to develop a maximum likelihood estimator (MLE) for lifetime performance index for the parameter of mixture Rayleigh-Half Normal distribution (RHN) under progressively type-II right-censored samples under the constraint of knowing the lower specification limit (). Additionally, we suggest an asymptotic normal distribution for the MLE for in order to construct a mechanism for evaluating products' lifespan efficiency. We have specified all the steps to carry out the test.

Estimate Stress-Strength Reliability Model Using Rayleigh and Half-Normal Distribution.

In the field of life testing, it is very important to study the reliability of any component under testing. One of the most important subjects is the "stress-strength reliability" term which always refers to the quantity  ( > ) in any statistical literature. It resamples a system with random strength () that is subjected to a random strength () such that a system fails in case the stress exceeds the strength.

Establishing Meaningful Limits of Detection for Ion-Selective Electrodes and Other Nonlinear Sensors.

Although IUPAC has recommended a probabilistic approach to determining limit of detection (LOD) based on false-positive and false-negative rates for more than 20 years, the LOD definition for ion-selective electrodes (ISEs) long predates these recommendations and conflicts substantively with them. Although it is well known that the ISE LOD definition does not follow best practice, it continues to be used due to simplicity and a lack of available methods for estimating LOD for nonlinear sensors. Here, we use ISEs as a model system for estimation of LOD for nonlinear sensors that is consistent with broad IUPAC recommendations and justified using statistical theory.

Semi-Automated Data Analysis for Ion-Selective Electrodes and Arrays Using the R Package ISEtools.

A new software package, , is introduced for use within the popular open-source programming language R that allows Bayesian statistical data analysis techniques to be implemented in a straightforward manner. Incorporating all collected data simultaneously, this Bayesian approach naturally accommodates sensor arrays and provides improved limit of detection estimates, including providing appropriate uncertainty estimates. Utilising >1500 lines of code, provides a set of three core functions-loadISEdata, describeISE, and analyseISE- for analysing ion-selective electrode data using the Nikolskii-Eisenman equation.