BM-MSCs alleviate diabetic nephropathy in male rats by regulating ER stress, oxidative stress, inflammation, and apoptotic pathways

Introduction: Diabetic nephropathy (DN), a chronic kidney disease, is a major cause of end-stage kidney disease worldwide. Mesenchymal stem cells (MSCs) have become a promising option to mitigate several diabetic complications.Methods: In this study, we evaluated the therapeutic potential of bone marrow-derived mesenchymal stem cells (BM-MSCs) in a rat model of STZ-induced DN. After the confirmation of diabetes, rats were treated with BM-MSCs and sacrificed at week 12 after treatment.Results: Our results showed that STZ-induced DN rats had extensive histopathological changes, significant upregulation in mRNA expression of renal apoptotic markers, ER stress markers, inflammatory markers, fibronectin, and intermediate filament proteins, and reduction of positive immunostaining of PCNA and elevated P53 in kidney tissue compared to the control group. BM-MSC therapy significantly improved renal histopathological changes, reduced renal apoptosis, ER stress, inflammation, and intermediate filament proteins, as well as increased positive immunostaining of PCNA and reduced P53 in renal tissue compared to the STZ-induced DN group.Conclusion: In conclusion, our study indicates that BM-MSCs may have therapeutic potential for the treatment of DN and provide important insights into their potential use as a novel therapeutic approach for DN

Type II General Inverse Exponential family of distributions

In this paper, we introduce a new family of distributions based on the T-X transformation , the inverse exponential distribution, the odds function and the Lehmann type II distribution. We investigate its general mathematical properties, including moments , moment generating function, quantile function, entropies and order statistics. A statistical model is constructed from a special case of the family using the Bur III distribution (also known as exponentiated Lomax distribution) as baseline. The estimation of the parameters are performed by the maximum likelihood method and the least square method. Finally, we illustrate its importance by means of two applications to real life data sets

Type II Half Logistic Family of Distributions with Applications

A new family of distributions called the type II half logistic is introduced and studied.  Four new special models are presented. Some mathematical properties of the type II half logistic family are studied. Explicit expressions for the moments, probability weighted, quantile function, mean deviation, order statistics and Rényi entropy are investigated. Parameter estimates of the family are obtained based on maximum likelihood procedure. Two real data sets are employed to show the usefulness of the new family.

Statistical Properties and Estimation of Inverted Topp-Leone Distribution

A new probability distribution, i.e. the inverted Topp-Leone distribution is proposed. Some of its statistical properties such as; quantile function, mode, moments, probability weighted moments, incomplete moments, stress-strength model, moments of residual life function, Rényi entropy, and stochastic ordering are provided. Maximum likelihood estimation method based on complete, Type I, and Type II censored samples is considered. Simulation issues are provided to assess the results of the study. Moreover, the results are applied to a real data set

The Exponentiated Truncated Inverse Weibull-Generated Family of Distributions with Applications

In this paper, we propose a generalization of the so-called truncated inverse Weibull-generated family of distributions by the use of the power transform, adding a new shape parameter. We motivate this generalization by presenting theoretical and practical gains, both consequences of new flexible symmetric/asymmetric properties in a wide sense. Our main mathematical results are about stochastic ordering, uni/multimodality analysis, series expansions of crucial probability functions, probability weighted moments, raw and central moments, order statistics, and the maximum likelihood method. The special member of the family defined with the inverse Weibull distribution as baseline is highlighted. It constitutes a new four-parameter lifetime distribution which brightensby the multitude of different shapes of the corresponding probability density and hazard rate functions. Then, we use it for modelling purposes. In particular, a complete numerical study is performed, showing the efficiency of the corresponding maximum likelihood estimates by simulation work, and fitting three practical data sets, with fair comparison to six notable models of the literature

Bayesian inference using MCMC algorithm of sine truncated Lomax distribution with application

This study makes a significant contribution to the creation of a versatile trigonometric extension of the well-known truncated Lomax distribution. Specifically, we construct a novel one-parameter distribution known as the sine truncated Lomax (STLo) distribution using characteristics from the sine generalized family of distributions. Quantiles, moments, stress–strength reliability, some information measures, residual moments, and reversed residual moments are a few of the crucial elements and characteristics we explored in our research. The flexibility of the STLo distribution in terms of the forms of the hazard rate and probability density functions illustrates how effectively it is able to match many types of data. Maximum likelihood and Bayesian estimation techniques are used to estimate the model parameter. The squared error loss function is employed in the Bayesian approach. To evaluate how various estimates behave, a Monte Carlo simulation study is carried out with the aid of a useful algorithm. Additionally, the STLo distribution has a good fit, making it a viable option when compared to certain other competing models using specific criteria to describe the given dataset

Type II Topp Leone Power Lomax Distribution with Applications

In many areas of applied sciences, the last step of a study often consists in analyzing in depth the collected data. Among all the kinds of data, the lifetime data are well-known to convey a great deal of information whose capture is necessary to identify one or more key phenomena. In this regards, numerous mathematical models have been proposed, including those based on lifetime distributions. In this paper, we introduce a new four-parameter lifetime distribution based on the type II Topp-Leone-G family and the power Lomax distribution. In comparison to the existing distributions, the new one is characterized by very flexible probability functions: increasing, decreasing, J, and reverse J shapes are observed for the probability density and hazard rate functions, giving first signs on the potential of adaptability of the related model. With this idea in mind, the new distribution is studied in detail, from both the theoretical and applied sides. After showing its main mathematical properties, the related model is investigated with estimation of the parameters by the maximum likelihood method. We applied it to two practical datasets, including the well-know aircraft windshield data. We show that the new model performs better than several modern adversary models, motivating its use in an applied setting

Half Logistic Inverse Lomax Distribution with Applications

The last years have revealed the importance of the inverse Lomax distribution in the understanding of lifetime heavy-tailed phenomena. However, the inverse Lomax modeling capabilities have certain limits that researchers aim to overcome. These limits include a certain stiffness in the modulation of the peak and tail properties of the related probability density function. In this paper, a solution is given by using the functionalities of the half logistic family. We introduce a new three-parameter extended inverse Lomax distribution called the half logistic inverse Lomax distribution. We highlight its superiority over the inverse Lomax distribution through various theoretical and practical approaches. The derived properties include the stochastic orders, quantiles, moments, incomplete moments, entropy (Rényi and q) and order statistics. Then, an emphasis is put on the corresponding parametric model. The parameters estimation is performed by six well-established methods. Numerical results are presented to compare the performance of the obtained estimates. Also, a simulation study on the estimation of the Rényi entropy is proposed. Finally, we consider three practical data sets, one containing environmental data, another dealing with engineering data and the last containing insurance data, to show how the practitioner can take advantage of the new half logistic inverse Lomax model

Estimation of different types of entropies for the Kumaraswamy distribution.

The estimation of the entropy of a random system or process is of interest in many scientific applications. The aim of this article is the analysis of the entropy of the famous Kumaraswamy distribution, an aspect which has not been the subject of particular attention previously as surprising as it may seem. With this in mind, six different entropy measures are considered and expressed analytically via the beta function. A numerical study is performed to discuss the behavior of these measures. Subsequently, we investigate their estimation through a semi-parametric approach combining the obtained expressions and the maximum likelihood estimation approach. Maximum likelihood estimates for the considered entropy measures are thus derived. The convergence properties of these estimates are proved through a simulated data, showing their numerical efficiency. Concrete applications to two real data sets are provided

Sine Topp-Leone-G family of distributions: Theory and applications

Recent studies have highlighted the statistical relevance and applicability of trigonometric distributions for the modeling of various phenomena. This paper contributes to the subject by investigating a new trigonometric family of distributions defined from the alliance of the families known as sine-G and Topp-Leone generated (TL-G), inspiring the name of sine TL-G family. The characteristics of this new family are studied through analytical, graphical and numerical approaches. Stochastic ordering and equivalence results, determination of the mode(s), some expansions of distributional functions, expressions of the quantile function and moments and basics on order statistics are discussed. In addition, we emphasize the fact that the sine TL-G family is able to generate original, simple and pliant trigonometric models for statistical purposes, beyond the capacity of the former sine-G models and other top models of the literature. This fact is revealed with the special three-parameter sine TL-G model based on the inverse Lomax model, through an efficient parametric estimation and the adjustment of two data sets of interest