Serum uric acid: an independent risk factor for cardiovascular disease in Pakistani Punjabi patients.

Background: It is well-known that serum uric acid (SUA) can increase the risk of hypertension, diabetes, obesity and dyslipidemia. However, its independent association with the risk of cardiovascular diseases (CVD) is controversial particularly in different populations. Hence, this study was aimed to assess an independent association of SUA with CVD risk in a Punjabi Pakistani cohort.

Innovative thermal management in the presence of ferromagnetic hybrid nanoparticles.

In the present work, a simple intelligence-based computation of artificial neural networks with the Levenberg-Marquardt backpropagation algorithm is developed to analyze the new ferromagnetic hybrid nanofluid flow model in the presence of a magnetic dipole within the context of flow over a stretching sheet. A combination of cobalt and iron (III) oxide (Co-FeO) is strategically selected as ferromagnetic hybrid nanoparticles within the base fluid, water. The initial representation of the developed ferromagnetic hybrid nanofluid flow model, which is a system of highly nonlinear partial differential equations, is transformed into a system of nonlinear ordinary differential equations using appropriate similarity transformations.

Effect of boundary slips and magnetohydrodynamics on peristaltic mechanism of Jeffrey nanofluid along with microorganisms through a porous medium.

The development on entropy generation in fluid flows has applications in many medical equipment such as cryogenic devices and therapeutic heat apparatus. This study looks at how porous medium, multi-slips, and entropy formation affect the pumping of Jeffrey nanofluid flow through an asymmetric channel containing motile microorganims. A lubrication theory is used to neglect the fluctuation effects in the flow.

Neuro-computing solution for Lorenz differential equations through artificial neural networks integrated with PSO-NNA hybrid meta-heuristic algorithms: a comparative study.

In this article, examine the performance of a physics informed neural networks (PINN) intelligent approach for predicting the solution of non-linear Lorenz differential equations. The main focus resides in the realm of leveraging unsupervised machine learning for the prediction of the Lorenz differential equation associated particle swarm optimization (PSO) hybridization with the neural networks algorithm (NNA) as ANN-PSO-NNA. In particular embark on a comprehensive comparative analysis employing the Lorenz differential equation for proposed approach as test case.

Machine learning intelligent based hydromagnetic thermal transport under Soret and Dufour effects in convergent/divergent channels: a hybrid evolutionary numerical algorithm.

In this research, we analyze the complex dynamics of hydro-magnetic flow and heat transport under Sorent and Dofour effects within wedge-shaped converging and diverging channels emphasizing its critical role in conventional system design, high-performance thermal equipment. We utilized artificial neural networks (ANNs) to investigation the dynamics of the problem. Our study centers on unraveling the intricacies of energy transport and entropy production arising from the pressure-driven flow of a non-Newtonian fluid within both convergent and divergent channel.

Tetra-ARMS-PCR assay development for genotyping of rs699 T/C polymorphism, its comparison with PCR-RFLP and application in a case-control association study of cardiovascular disease patients.

Genetic variations in the gene play a significant role in controlling the plasma concentration of angiotensinogen (precursor protein of bioactive octapeptide angiotensin II) and the efficacy of antihypertensive drugs. In the current study, Tetra-Amplification Refractory Mutation System-Polymerase Chain Reaction (T-ARMS-PCR) was developed for genotyping of rs699 T/C polymorphism and validated through Sanger DNA sequencing. Its efficiency was also tested using 474 human DNA samples [control, n = 181; cardiovascular disease (CVD) patients, n = 293].

Association of ACE I/D Polymorphism with Risk of Diabetes in Cardiovascular Disease Patients.

Angiotensin converting enzyme (ACE), as part of renin angiotensin aldosterone system, is involved in blood pressure regulation and control several physiological functions. Insertion/Deletion (I/D) polymorphism of has pronounced effects on development of metabolic diseases like diabetes, cardiovascular diseases (CVDs) and hypertension. However, association of I/D polymorphism with risk of diabetes in CVD patients is not known.