This study was focused on survival analysis of heart failure patients who were admitted to Institute of Cardiology and Allied hospital Faisalabad-Pakistan during April-December (2015). All the patients were aged 40 years or above, having left ventricular systolic dysfunction, belonging to NYHA class III and IV. Cox regression was used to model mortality considering age, ejection fraction, serum creatinine, serum sodium, anemia, platelets, creatinine phosphokinase, blood pressure, gender, diabetes and smoking status as potentially contributing for mortality. Kaplan Meier plot was used to study the general pattern of survival which showed high intensity of mortality in the initial days and then a gradual increase up to the end of study. Martingale residuals were used to assess functional form of variables. Results were validated computing calibration slope and discrimination ability of model via bootstrapping. For graphical prediction of survival probability, a nomogram was constructed. Age, renal dysfunction, blood pressure, ejection fraction and anemia were found as significant risk factors for mortality among heart failure patients.
Diabetes mellitus is a metabolic disease caused due to the improper secretion of insulin which leads to hyperglycaemia. According to the International Diabetes Federation (IDF), 371 million people are affected by diabetes worldwide, while 7 million people are suffering from this disease in Pakistan. Pyrazolobenzothiazine 5,5-dioxide derivatives have anti-bacterial, anti-inflammatory, antioxidant activity and have the potential to treat diabetes and other diseases. This study was designed to perform in-silico and in-vitro analysis of pyrazolobenzothiazine 5,5-dioxide derivatives against αglucosidase for the treatment of diabetes mellitus. For this purpose, pyrazolobenzothiazine 5,5-dioxide derivatives were synthesized in the laboratory. Molecular docking analysis of pyrazolobenzothiazine 5,5-dioxide derivatives against αglucosidase was achieved through Molecular Operating Environment (MOE) and ranked them based on binding affinity. Compounds with strong binding interaction were selected for invitro anti-diabetic analysis by enzyme inhibition assay against αglucosidase using p-nitrophenyl-α-Dglucopyranoside (PNPG) as a substrate. Furthermore, the dose-response analysis was performed via Microdilution method. Compounds having strong bonding interactions with the active site and high scores were selected for in-vitro analysis. Compounds 1a, 1f, 1 g, 1 h showed strong bonding interaction with the active site of αglucosidase and have docking score − 11.303, −10.189, −10.360, −10.160 respectively. Compound 1e, 1f, 1 g and 1 h showed IC50 at the concentration of 4.7 μM, 8.8 μM, 12.2 μM and 11.2 μM respectively in ezyme inhibition assay. The outcome of this study proved helpful to determine new antidiabetic agents to minimize diabetes complication.
There are many practical situations where the underlying distribution of the quality characteristic either deviates from normality or it is unknown. In such cases, practitioners often make use of the nonparametric control charts. In this paper, a new nonparametric double exponentially weighted moving average control chart on the basis of the signed-rank statistic is proposed for monitoring the process location. Monte Carlo simulations are carried out to obtain the run length characteristics of the proposed chart. The performance comparison of the proposed chart with the existing parametric and nonparametric control charts is made by using various performance metrics of the run length distribution. The comparison showed the superiority of the suggested chart over its existing parametric and nonparametric counterparts. An illustrative example for the practical implementation of the proposed chart is also provided by using the industrial data set.
The article proposes three modified percentile estimators for parameter estimation of the Pareto distribution. These modifications are based on median, geometric mean and expectation of empirical cumulative distribution function of first-order statistic. The proposed modified estimators are compared with traditional percentile estimators through a Monte Carlo simulation for different parameter combinations with varying sample sizes. Performance of different estimators is assessed in terms of total mean square error and total relative deviation. It is determined that modified percentile estimator based on expectation of empirical cumulative distribution function of first-order statistic provides efficient and precise parameter estimates compared to other estimators considered. The simulation results were further confirmed using two real life examples where maximum likelihood and moment estimators were also considered.
In this paper, a generalized class of estimators for the estimation of population median are proposed under simple random sampling without replacement (SRSWOR) through robust measures of the auxiliary variable. Three robust measures, decile mean, Hodges–Lehmann estimator, and trimean of an auxiliary variable, are used. Mathematical properties of the proposed estimators such as bias, mean squared error (MSE), and minimum MSE are derived up to first order of approximation. We considered various real-life datasets and a simulation study to check the potentiality of the proposed estimators over the competitors. Robustness is also examined through a real dataset. Based on the fascinating results, the researchers are encouraged to use the proposed estimators for population median under SRSWOR.
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