An approximation of a multivariative stochastic model for the analysis of longitudinal data

We propose to approximate a model for multivariate repeated measures that incorporated random effects, correlated stochastic process and measurements error. The model is generalization for univariate longitudinal data given by Taylor et al. (1994). The stochastic process used in this paper is the multivariate Integrated Omstein-Uhlenbeck (IOU) process. We consider a Bayesian approach which is motivated by the complexity of the model, thus, we propose to approximate the IOU stochastic process in a simple way that will mitigate the necessity to invert the stochastic process covariance matrix separately for each group at each iteration of the Markov Chain Monte Carlo (MCMC) sampler. The proposed method is illustrated by application to the melanoma data set

DNA-Based Applications in Nanobiotechnology

Biological molecules such as deoxyribonucleic acid (DNA) have shown great potential in fabrication and construction of nanostructures and devices. The very properties that make DNA so effective as genetic material also make it a very suitable molecule for programmed self-assembly. The use of DNA to assemble metals or semiconducting particles has been extended to construct metallic nanowires and functionalized nanotubes. This paper highlights some important aspects of conjugating the unique physical properties of dots or wires with the remarkable recognition capabilities of DNA which could lead to miniaturizing biological electronics and optical devices, including biosensors and probes. Attempts to use DNA-based nanocarriers for gene delivery are discussed. In addition, the ecological advantages and risks of nanotechnology including DNA-based nanobiotechnology are evaluated

A stochastic joint model for longitudinal and survival data with cure patients

Many medical investigations generate both repeatedly-measured (longitudinal) biomarker and survival data. One of complex issues arises when investigating the association between longitudinal and time-to-event data when there are cured patients in the population, which leads to a plateau in the survival function S(t) after sufficient follow-up. Thus, usual Cox proportional hazard model Cox (1972) is not applicable since the proportional hazard assumption is violated. An alternative is to consider survival models incorporating a cure fraction. In this paper we present a new class of joint model for univariate longitudinal and survival data in presence of cure fraction. For the longitudinal model, a stochastic Integrated Ornstein-Uhlenbeck process will be presented. For the survival model a semiparametric survival function will be considered which accommodate both zero and non-zero cure fractions of the dynamic disease progression. Moreover, we consider a Bayesian approach which is motivated by the complexity of the model. Posterior and prior specification needs to accommodate parameter constraints due to the nonnegativity of the survival function. A simulation study is presented to evaluate the performance of this joint model

Cure fraction, modelling and estimating in a population-based cancer survival analysis

In population-based cancer studies, cure is said to occur when the mortality (hazard)rate in the diseased group of individuals returns to the same level as that expected in the general population. The optimal method for monitoring the progress of patient care across the full spectrum of provider settings is through the population-based study of cancer patient survival, which is only possible using data collected by population-based cancer registries. The probability of cure, statistical cure, is defined for a cohort of cancer patients as the percent of patients whose annual death rate equals the death rate of general cancer-free population. Recently models have been introduced, so called cure fraction models, that estimates the cure fraction as well as the survival time distribution for those uncured. The colorectal cancer survival data from the Surveillance, Epidemiology and End Results (SEER) program, USA, is used. The aim is to evaluate the cure fraction models and compare these methods to other methods used to monitor time trends in cancer patient survival, and to highlight some problems using these models

Bayesian approach for joint longitudinal and time-to-event data with survival fraction

Many medical investigations generate both repeatedly-measured(longitudinal) biomarker and survival data. One of complex issue arises when investigating the association between longitudinal and time-to-event data when there are cured patients in the population, which leads to a plateau in the survival function S(t) after sufficient follow-up. Thus, usual Cox proportional hazard model [11] is not applicable since the proportional hazard assumption is violated. An alternative is to consider survival models incorporating a cure fraction. In this paper, we present a new class of joint model for univariate longitudinal and survival data in presence of cure fraction. For the longitudinal model, a stochastic Integrated Ornstein-Uhlenbeck process will present, and for the survival model a semiparametric survival function will be considered which accommodate both zero and non-zero cure fractions of the dynamic disease progression. Moreover, we consider a Bayesian approach which is motivated by the complexity of the model. Posterior and prior specification needs to accommodate parameter constraints due to the non-negativity of the survival function. A simulation study is presented to evaluate the performance of the proposed joint model

A Semiparametric Joint Model for Longitudinal and Time-to- Event Univariate Data in Presence of Cure Fraction

Many medical investigations generate both repeatedly-measured (longitudinal)biomarker and survival data. One of complex issue arises when investigating the association between longitudinal and time-to-event data when there are cured patients in the population, which leads to a plateau in the survival function S(t) after sufficient follow-up. Thus, usual Cox proportional hazard model Cox (1972) is not applicable since the proportional hazard assumption is violated. An alternative is to consider survival models incorporating a cure fraction. In this paper we present a new class of joint model for univariate longitudinal and survival data in presence of cure fraction. For the longitudinal model, a stochastic Integrated Ornstein-Uhlenbeck process will present, and for the survival model a semiparametric survival function will be considered which accommodate both zero and non-zero cure fractions of the dynamic disease progression. Moreover, we consider a Bayesian approach which is motivated by the complexity of the model. Posterior and prior specification needs to accommodate parameter constraints due to the nonnegativity of the survival function. A simulation study is presented to evaluate the performance of this joint model

Isolation and fatty acid profile of selected microalgae strains from the Red Sea for biofuel production

The isolation of lipid-rich autochthonous strains of microalgae is a crucial stage for the development of a microalgae-based biofuel production plant, as these microalgae already have the necessary adaptations to withstand competition, predation and the temperatures observed at each production site. This is particularly important in extreme climates such as in Saudi Arabia. Resorting to fluorescence activated cell sorting (FACS) we screened for and isolated several microalgal strains from samples collected from the Red Sea. Relying on the fluorescence of BODIPY 505/515 (4,4-difluoro-1,3,5,7-tetramethyl-4-bora-3a,4a-diazasindacene) and growth performance, four promising candidates were identified and the total lipid content and fatty acid profile was assessed for biofuels production. Selected isolates were classified as chlorophytes, belonging to three different genera: Picochlorum, Nannochloris and Desmochloris. The lipid contents were assessed microscopically by means of BODIPY 505/515-associated fluorescence to detect intracellular lipid bodies, which revealed several lipid drops in all selected strains. This result was confirmed by lipid gravimetric determination, which demonstrated that all strains under study presented inner cell lipid contents ranging from 20% to 25% of the biomass dry weight. Furthermore, the fatty acid methyl esters profile of all strains seems ideal for biodiesel production due to a low degree of polyunsaturated fatty acid methyl esters and high amount of palmitic and oleic acids.NPST Program of King Saud University [11-ENE1719-02]info:eu-repo/semantics/publishedVersio

Nanomaterials as Analytical Tools for Genosensors

Nanomaterials are being increasingly used for the development of electrochemical DNA biosensors, due to the unique electrocatalytic properties found in nanoscale materials. They offer excellent prospects for interfacing biological recognition events with electronic signal transduction and for designing a new generation of bioelectronic devices exhibiting novel functions. In particular, nanomaterials such as noble metal nanoparticles (Au, Pt), carbon nanotubes (CNTs), magnetic nanoparticles, quantum dots and metal oxide nanoparticles have been actively investigated for their applications in DNA biosensors, which have become a new interdisciplinary frontier between biological detection and material science. In this article, we address some of the main advances in this field over the past few years, discussing the issues and challenges with the aim of stimulating a broader interest in developing nanomaterial-based biosensors and improving their applications in disease diagnosis and food safety examination

Development of Polymeric Nanoparticles of Garcinia mangostana Xanthones in Eudragit RL100/RS100 for Anti-Colon Cancer Drug Delivery

Xanthones are a group of oxygenated heterocyclic compounds with anticancer properties, but poor aqueous solubility and low oral bioavailability hinder their therapeutic application. This study sought to prepare a xanthones extract (81

Antiangiogenic properties of Koetjapic acid, a natural triterpene isolated from Sandoricum koetjaoe Merr

Background: Angiogenesis, the formation of new blood vessels, has become an important target in cancer therapy. Angiogenesis plays an important role in tumor growth and metastasis. Koetjapic acid (KA) is a seco-A-ring oleanene triterpene isolated from S. koetjape. The solvent extract of this plant species was shown previously to have strong antiangiogenic activity; however the active ingredient(s) that conferred the biological activity and the mode of action was not established. Given the high concentration of KA in S. koetjape, an attempt has been made in this study to investigate the antiangiogenic properties of KA.Results: Treatment with 10-50 μg/ml KA resulted in dose dependent inhibition of new blood vessels growth in ex vivo rat aortic ring assay. KA was found to be non-cytotoxic against HUVECs with IC40.97 ± 0.37 μg/ml. KA inhibited major angiogenesis process steps, endothelial cell migration and differentiation as well as VEGF expression.Conclusions: The non-cytotoxic compound, KA, may be a potent antiangiogenic agent; its activity may be attributed to inhibition of endothelial cells migration and differentiation as well VEGF suppression