A transition copula model for analyzing multivariate longitudinal data with missing responses.

In multivariate longitudinal studies, several outcomes are repeatedly measured for each subject over time. The data structure of these studies creates two types of associations which should take into account by the model: association of outcomes at a given time point and association among repeated measurements over time for a specific outcome. In our approach, because of some advantageous arisen from features like flexibility of marginal distributions, a copula-based approach is used for joint modeling of multivariate outcomes at each time points, also a transition model is used for considering the association of longitudinal measurements over time. For the problem of incomplete data, missingness mechanism is assumed to be ignorable. Some simulation results are reported in different scenarios using the Gaussian, t and several commonly used copulas of the family of Archimedean copulas. Akaike information criterion (AIC) is used to select the best copula function. The proposed approach is also used for analyzing a real obesity data set.

A Bayesian shared parameter model for joint modeling of longitudinal continuous and binary outcomes.

Joint modeling of associated mixed biomarkers in longitudinal studies leads to a better clinical decision by improving the efficiency of parameter estimates. In many clinical studies, the observed time for two biomarkers may not be equivalent and one of the longitudinal responses may have recorded in a longer time than the other one. In addition, the response variables may have different missing patterns. In this paper, we propose a new joint model of associated continuous and binary responses by accounting different missing patterns for two longitudinal outcomes. A conditional model for joint modeling of the two responses is used and two shared random effects models are considered for intermittent missingness of two responses. A Bayesian approach using Markov Chain Monte Carlo (MCMC) is adopted for parameter estimation and model implementation. The validation and performance of the proposed model are investigated using some simulation studies. The proposed model is also applied for analyzing a real data set of bariatric surgery.

Approximate Bayesian inference for joint linear and partially linear modeling of longitudinal zero-inflated count and time to event data.

Joint modeling of zero-inflated count and time-to-event data is usually performed by applying the shared random effect model. This kind of joint modeling can be considered as a latent Gaussian model. In this paper, the approach of integrated nested Laplace approximation (INLA) is used to perform approximate Bayesian approach for the joint modeling. We propose a zero-inflated hurdle model under Poisson or negative binomial distributional assumption as sub-model for count data. Also, a Weibull model is used as survival time sub-model. In addition to the usual joint linear model, a joint partially linear model is also considered to take into account the non-linear effect of time on the longitudinal count response. The performance of the method is investigated using some simulation studies and its achievement is compared with the usual approach via the Bayesian paradigm of Monte Carlo Markov Chain (MCMC). Also, we apply the proposed method to analyze two real data sets. The first one is the data about a longitudinal study of pregnancy and the second one is a data set obtained of a HIV study.

A two-stage approach for joint modeling of longitudinal measurements and competing risks data.

Joint modeling of longitudinal measurements and time-to-event data is used in many practical studies of medical sciences. Most of the time, particularly in clinical studies and health inquiry, there are more than one event and they compete for failing an individual. In this situation, assessing the competing risk failure time is important. In most cases, implementation of joint modeling involves complex calculations. Therefore, we propose a two-stage method for joint modeling of longitudinal measurements and competing risks (JMLC) data based on the full likelihood approach via the conditional EM (CEM) algorithm. In the first stage, a linear mixed effect model is used to estimate the parameters of the longitudinal sub-model. In the second stage, we consider a cause-specific sub-model to construct competing risks data and describe an approximation for the joint log-likelihood that uses the estimated parameters of the first stage. We express the results of a simulation study and perform this method on the "standard and new anti-epileptic drugs" trial to check the effect of drug assaying on the treatment effects of lamotrigine and carbamazepine through treatment failure.

A sensitive signal-on electrochemiluminescence sensor based on a nanocomposite of polypyrrole-Gd2O3 for the determination of L-cysteine in biological fluids.

A sensitive solid-state electrochemiluminescence (ECL) electrode for L-cysteine was developed based on depositing layers of a hybrid nanocomposite of polypyrrole-dodecyl benzene sulfate-sodium perchlorate-sodium carbonate-gadolinium (PPy-Gd2O3) on a platinum substrate. The presence of the Gd2O3 nanoparticle layer improved the ECL signal, and under optimum conditions, a linear relationship was observed between the signal and the logarithm of L-cysteine concentration from 1.0 × 10-13 to 1.0 × 10-6 M (R2 = 0.9937). At the emission wavelength around 425 nm , at which the analytical signal was measured, the electrode showed an RSD of less than 4% and a low detection limit of 4.2 × 10-14 M. The results proved to be reproducible and stable, and the electrode was applicable in the determination of L-cysteine in biological fluids with recoveries from 94.0-107%. Graphical abstract The results of this research indicated that the weak ECL of luminol is greatly improved by traces of L-cysteine on a solid-state platinum electrode coated with polypyrrole and gadolinium oxide nanoparticles (NPs) and have hence been effectively used in the analysis of L-cysteine in plasma and saliva.

Comparative Study of Various Types of Metal-Free N and S Co-Doped Porous Graphene for High Performance Oxygen Reduction Reaction in Alkaline Solution.

Heteroatom doping into carbon structures is an effective approach to enhance the electrochemical performance of carbon materials. In the work presented here, the electrocatalysts including: nitrogen and co-doped nitrogen and sulfur on porous graphene (PG) were synthesized by different precursors. The physico-chemical properties of the prepared samples were determined using X-ray Diffraction (XRD), Raman spectroscopy, Fourier transform infrared spectroscopy (FTIR), N2 sorption-desorption, Transmission electron microscopy (TEM), Field Emission Scanning Electron Microscopy (FESEM) and X-ray photoelectron spectroscopy (XPS). The prepared samples were further applied for oxygen reduction reaction (ORR) and the effects of pyrolysis temperature, precursor type and dose, on the prepared samples structure and their electrochemical performances were investigated. The results revealed that synergistic effect of nitrogen and sulfur co-doped on the graphene structure leads to improvement in catalytic activity and current. Furthermore, S and N co-doped graphene prepared using sulfur trioxide pyridine complex exhibited excellent methanol tolerance and long-term stability.

Molecular detection of Trypanosoma evansi based on ITS1 rDNA gene in Camelus dromedarius in Sistan Region, Iran.

Trypanosomiasis is a disease caused by a flagellate protozoon called Trypanosoma and can be mechanically transmitted by vectors to humans and animals. Various species of Trypanosoma are found in livestock and poultry, which include Trypanosoma evansi, T. brucei, T. vivax and T. congolense. The camel is the most sensitive livestock for T. evansi, so the exact identification of infection is very important for epidemiological studies and the design of control programs. The present study was conducted with the aim of molecular detection of camel trypanosomiasis in the Sistan region in 2015. Previous studies have shown that internal transcribed spacer one (ITS1) of the ribosomal DNA is a reliable genetic marker for carrying out systematic molecular studies of trypanosomes. In order to investigate infections of camels with T. evansi, a total of 113 blood samples were collected randomly and the presence of parasites in each sample was evaluated using the microscopic method and polymerase chain reaction (PCR) test. Genomic DNA was extracted and the ITS-1 was amplified by PCR. In comparison to the nucleotide sequence obtained with the sequences recorded in GenBank, it was determined that there is a 99% homology with the recorded sequence of T. evansi. The obtained sequence was registered in Gen Bank with kx900449 code. The T. evansi sequences from different countries such as India, Taiwan, Thailand, the Philippines, China and Argentina and etc., were extracted from the Gene bank and aligned using the ClustalW2 sequence alignment tool and MEGA software. In this study the prevalence of T. evansi infection using the molecular method was 6.19% and no positive samples were found by microscopic observation.

Molecular detection of Trypanosoma evansi based on ITS1 rDNA gene in Camelus dromedarius in Sistan Region, Iran

@#Trypanosomiasis is a disease caused by a flagellate protozoon called Trypanosoma and can be mechanically transmitted by vectors to humans and animals. Various species of Trypanosoma are found in livestock and poultry, which include Trypanosoma evansi, T. brucei, T. vivax and T. congolense. The camel is the most sensitive livestock for T. evansi, so the exact identification of infection is very important for epidemiological studies and the design of control programs. The present study was conducted with the aim of molecular detection of camel trypanosomiasis in the Sistan region in 2015. Previous studies have shown that internal transcribed spacer one (ITS1) of the ribosomal DNA is a reliable genetic marker for carrying out systematic molecular studies of trypanosomes. In order to investigate infections of camels with T. evansi, a total of 113 blood samples were collected randomly and the presence of parasites in each sample was evaluated using the microscopic method and polymerase chain reaction (PCR) test. Genomic DNA was extracted and the ITS-1 was amplified by PCR. In comparison to the nucleotide sequence obtained with the sequences recorded in GenBank, it was determined that there is a 99% homology with the recorded sequence of T. evansi. The obtained sequence was registered in Gen Bank with kx900449 code. The T. evansi sequences from different countries such as India, Taiwan, Thailand, the Philippines, China and Argentina and etc., were extracted from the Gene bank and aligned using the ClustalW2 sequence alignment tool and MEGA software. In this study the prevalence of T. evansi infection using the molecular method was 6.19% and no positive samples were found by microscopic observation.

3D-QSAR and docking studies on adenosine A2A receptor antagonists by the CoMFA method.

Parkinson's disease affects millions of people around the world. Recently, adenosine A2A receptor antagonists have been identified as a drug target for the treatment of Parkinson's disease. Consequently, there is an immediate need to develop new classes of A2A receptor antagonists. In the present analysis, three-dimensional quantitative structure-activity relationship (3D-QSAR) studies were performed on a series of pyrimidines, using comparative molecular field analysis (CoMFA). The best prediction was obtained with a CoMFA standard model (q(2) = 0.475, r(2) = 0.977) and a CoMFA region focusing model (q(2) = 0.637, r(2) = 0.976) combined with steric and electrostatic fields. The structural insights derived from the contour maps helped to better interpret the structure-activity relationships. Also, to understand the structure-activity correlation of A2A receptor antagonists, we have carried out molecular docking analysis. Based on the results obtained from the present 3D-QSAR and docking studies, we have identified some key features for increasing the activity of compounds, which have been used to design new A2A receptor antagonists. The newly designed molecules showed high activity with the obtained models.

Selective recognition of Ni2+ ion based on fluorescence enhancement chemosensor.

A new enhancing fluorescent chemosensor was introduced for selective and sensitive determination of nickel ions based on 2-(1-H-benzo[d]imidazol-2yl)-N-phenyl hydrazine carbothioamide (L). L has an intrinsic fluorescent emission which enhances in presence of nickel ions in CH3CN/H2O (70:30, v/v) solution. The fluorescence enhancement of L is attributed to a 1:1 complex formation between L and Ni2+ ion which has been used for selective detection of Ni2+ ion. At the optimum conditions, the fluorescence intensity of L at 352 nm enhances linearly by the concentration of nickel ion from 1.6×10(-5) to 1.6×10(-7) M and detection limit of 7.9×10(-8) M. The new fluorescent probe exhibited high selectivity to Ni2+ ion over the other common mono, di-and trivalent cations.

A Copula Approach to Joint Modeling of Longitudinal Measurements and Survival Times Using Monte Carlo Expectation-Maximization with Application to AIDS Studies.

Joint modeling of longitudinal measurements and time to event data is often performed by fitting a shared parameter model. Another method for joint modeling that may be used is a marginal model. As a marginal model, we use a Gaussian model for joint modeling of longitudinal measurements and time to event data. We consider a regression model for longitudinal data modeling and a Weibull proportional hazard model for event time data modeling. A Gaussian copula is used to consider the association between these two models. A Monte Carlo expectation-maximization approach is used for parameter estimation. Some simulation studies are conducted in order to illustrate the proposed method. Also, the proposed method is used for analyzing a clinical trial dataset.

Turn-on fluorescent chemosensor for determination of lutetium ion.

A turn-on fluorescent chemosensor is introduced for the detection of Lu(3+) ion using N-[3-methyl]-2-[pyridine-2-amido] phenyl] pyridine-2-carboxamide (L) molecule. Fluorescent emission intensity of L enhances after binding to Lu(3+) ions in ethanol-water solution (1:9, v/v). The observed enhancement is the result of a strong covalent binding between Lu(3+) ion and L (the binding constant value is 2.0×10(6) mol(-1) L). The proposed optical chemosensor can be applied for the analysis of Lu(3+) ion in a linear range of 3.3×10(-7) to 1.0×10(-5) mol L(-1). The limit of detection was obtained 8.6×10(-7) mol L(-1). The probe exhibits high selectivity toward Lu(3+) ion in comparison with common metal ions. The proposed fluorescent chemosensor was successfully used in the determination of Lu(3+) ion in some water samples.

Bayesian joint modeling of longitudinal measurements and time-to-event data using robust distributions.

Distributional assumptions of most of the existing methods for joint modeling of longitudinal measurements and time-to-event data cannot allow incorporation of outlier robustness. In this article, we develop and implement a joint modeling of longitudinal and time-to-event data using some powerful distributions for robust analyzing that are known as normal/independent distributions. These distributions include univariate and multivariate versions of the Student's t, the slash, and the contaminated normal distributions. The proposed model implements a linear mixed effects model under a normal/independent distribution assumption for both random effects and residuals of the longitudinal process. For the time-to-event process a parametric proportional hazard model with a Weibull baseline hazard is used. Also, a Bayesian approach using the Markov-chain Monte Carlo method is adopted for parameter estimation. Some simulation studies are performed to investigate the performance of the proposed method under presence and absence of outliers. Also, the proposed methods are applied for analyzing a real AIDS clinical trial, with the aim of comparing the efficiency and safety of two antiretroviral drugs, where CD4 count measurements are gathered as longitudinal outcomes. In these data, time to death or dropout is considered as the interesting time-to-event outcome variable. Different model structures are developed for analyzing these data sets, where model selection is performed by the deviance information criterion (DIC), expected Akaike information criterion (EAIC), and expected Bayesian information criterion (EBIC).

DNA methylation detection by a novel fluorimetric nanobiosensor for early cancer diagnosis.

A very sensitive and convenient fluorescence nanobiosensor for rapid detection of DNA methylation based on Fe3O4/Au core/shell nanoparticles has been developed. Specific site of CpG islands of adenomatous polyposis coli (APC), a well studied tumor suppressor gene, was used as the detection target DNA sequence. The characteristics of nanoparticles were determined by scanning electron microscopy (SEM), transmission electron microscopy (TEM), energy dispersive spectroscopy (EDS), UV-visible spectroscopy and X-ray diffraction (XRD) spectroscopy. Fe@Au nanoparticles functionalized by bounding of single stranded DNA (ssDNA) probe through sulfhydryl group at the 5' phosphate end. Then unmethylated and methylated complementary target ssDNA were hybridized with the immobilized ssDNA probe. Dipyridamole, a pharmaceutical agent used for the first time as a fluorescence probe which significantly interacted with hybridized unmethylated and methylated DNA. Upon the addition of the target unmethylated and methylated ssDNA, the fluorescence intensity increased in linear range by concentration of unmethylated ssDNA from 1.6 × 10(-15) to 6.6 × 10(-13)M with detection limit of 1.2 × 10(-16)M and on the other hand, fluorescence intensity declined linearly with concentration of 3.2 × 10(-15)-8.0 × 10(-13)M methylated DNA and detection limit was 3.1 × 10(-16)M. We have also shown that nanobiosensor could distinguish ratio of methylation in series of partially methylated DNA targets with identical sequences. A density functional theory (DFT) calculation was also performed to investigate the interaction between Dipyridamole with unmethylated and methylated cytosine. Finally real sample analysis suggested that nanobiosensor could have practical application for methylation detection in human plasma sample.

Selective recognition of Pr3+ based on fluorescence enhancement sensor.

(E)-2-(1-(4-hydroxy-2-oxo-2H-chromen-3-yl)ethylidene)hydrazinecarbothioamide (L) has been used to detect trace amounts of praseodymium ion in acetonitrile-water solution (MeCN/H2O) by fluorescence spectroscopy. The fluorescent probe undergoes fluorescent emission intensity enhancement upon binding to Pr(3+) ions in MeCN/H2O (9/1:v/v) solution. The fluorescence enhancement of L is attributed to a 1:1 complex formation between L and Pr(3+), which has been utilized as the basis for selective detection of Pr(3+). The sensor can be applied to the quantification of praseodymium ion with a linear range of 1.6×10(-7) to 1.0×10(-5) M. The limit of detection was 8.3×10(-8) M. The sensor exhibits high selectivity toward praseodymium ions in comparison with common metal ions. The proposed fluorescent sensor was successfully used for determination of Pr(3+) in water samples.

A novel fluoride-selective electrode based on metalloporphyrin grafted-grapheneoxide.

In this work, the unique properties of graphene oxide were combined with the anion selectivity of metalloporphyrin to fabricate a novel fluoride-selective sensor. The electrode made of 27% PVC, 54% NPOE, 4% NaTPB and 15% NbTPP-GO was found to show the most favorable behavior. The sensor shows a Nernstian response (58.3 mV decade(-1)) in the concentration window of 5.0×10(-1)-5.0×10(-7) mol L(-1)with detection limit of 8.0×10(-87) mol L(-1). The response of the sensor was found to be stable in the pH range of 3.0-7.0 and the metalloporphyrin grafted-GO based F(-) sensors displayed very good selectivity with respect to a number of anions. The proposed sensor displays a long life time (more than 12 weeks) with a short response time of about 20 s.

A highly selective fluorescent probe for pyrophosphate detection in aqueous solutions.

A novel and simple fluorescence enhancement method is introduced for selective pyrophosphate (PPi) sensing in an aqueous solution. The method is based on a 1:1 metal complex formation between tris(8-hydroxyquinoline-5-sulphonate) thulium(III) [Tm(QS)(3)] and PPi ion. The linear response covers a concentration range of 1.6 × 10(-7) -1.0 × 10(-5) mol/L PPi and the detection limit is 2.3 × 10(-8) mol/L. The association constant of Tm(QS)(3) -PPi complex was calculated as 2.6 × 10(5) mol/L. Tm(QS)(3) shows a selective and sensitive fluorescence enhancement toward PPi ion in comparison with I(3)(-), NO(3)(-), CN(-), CO(3)(2-), Br(-), Cl(-), F(-), H(2) PO(4) (-) and SO(4)(2-), which is attributed to higher stability of the inorganic complex between pyrophosphate ion and Tm(QS)(3).

Modified ionic liquid cold-induced aggregation dispersive liquid-liquid microextraction combined with spectrofluorimetry for trace determination of ofloxacin in pharmaceutical and biological samples.

BACKGROUND AND THE PURPOSE OF THE STUDY: Ofloxacin is a quinolone synthetic antibiotic, which acts against resistant mutants of bacteria by inhibiting DNA gyrase. This antibacterial agent is widely used in the treatment of respiratory tract, urinary tract and tissue-based infections, which are caused by Gram-positive and Gram-negative bacteria. In this work, an efficient modified ionic liquid cold-induced aggregation dispersive liquid-liquid microextraction (M-IL-CIA-DLLME) was combined with spectrofluorimetry for trace determination of ofloxacin in real samples. METHODS: In this microextraction method, hydrophobic 1-hexyl-3-methylimidazolium hexafluorophosphate ([Hmim] [PF(6)]) ionic liquid (IL) as a microextraction solvent was dispersed into a heated sample solution containing sodium hexafluorophosphate (NaPF(6)) (as a common ion) and the analyte of interest. Afterwards, the resultant solution was cooled in an ice-water bath and a cloudy condition was formed due to a considerable decrease of IL solubility. After centrifuging, the enriched phase was introduced to the spectrofluorimeter for the determination of ofloxacin. RESULTS AND MAJOR CONCLUSION: In this technique, the performance of the microextraction method was not influenced by variations in the ionic strength of the sample solution (up to 30% w/v). Furthermore, [Hmim][PF(6)] IL was chosen as a green microextraction phase and an alternative to traditional toxic organic solvents. Different parameters affecting the analytical performance were studied and optimized. At optimum conditions, a relatively broad linear dynamic range of 0.15-125 µg l(-1) and a limit of detection (LOD) of 0.029 µg l(-1) were obtained. The relative standard deviation (R.S.D.) obtained for the determination of five replicates of the 10 ml solution containing 50 µg l(-1) ofloxacin was 2.7%. Finally, the combined methodology was successfully applied to ofloxacin determination in actual pharmaceutical formulations and biological samples.

Electrochemical Analysis of Some Toxic Metals by Ion-Selective Electrodes.

An overview of potentiometric sensors that are capable of detecting toxic heavy metal ions in environmental samples is presented and discussed. Notwithstanding the tremendous work performed so far, it is obvious that still several limitations do exist in terms of selectivity, limits of detection, dynamic ranges, applicability to specific problems, and reversibility. A survey on important advances in potentiometric sensors with regard to high selectivity, lower detection limit, fast response time, and on-line environmental analysis is presented in this review article. [Supplemental materials are available for this article. Go to the publisher's online edition of Critical Reviews in Analytical Chemistry to view the free supplemental file.].

An index of local sensitivity to non-ignorability for multivariate longitudinal mixed data with potential non-random dropout.

Multivariate longitudinal data with mixed continuous and discrete responses with the possibility of non-ignorable missingness are often common in follow-up medical studies and their analysis needs to be developed. Standard methods of analysis based on the strong and the unverifiable assumption of missing at random (MAR) mechanism could be highly misleading. A way out of this problem is to start with methods that simultaneously allow modelling non-ignorable mechanism, which includes somehow troubling computations that are often time consuming, then we can use a sensitivity analysis, in which one estimates models under a range of assumptions about non-ignorability parameters to study the impact of these parameters on key inferences. A general index of sensitivity to non-ignorability (ISNI) to measure sensitivity of key inferences in a neighborhood of MAR model without fitting a complicated not MAR (NMAR) model for univariate generalized linear models and for models used for univariate longitudinal normal and non-Gaussian data with potentially NMAR dropout are well presented in the literature. In this paper we extend ISNI methodology to analyze multivariate longitudinal mixed data subject to non-ignorable dropout in which the non-ignorable dropout model could be dependent on the mixed responses. The approach is illustrated by analyzing a longitudinal data set in which the general substantive goal of the study is to better understand the relations between parental assessment of child's antisocial behavior and child's reading recognition skill.