Optimization and validation of spectrofluorimetric method for determination of cefadroxile and cefuroxime sodium in pharmaceutical formulations

A simple, accurate, precise and validated spectrofluorimetric method is proposed for the determination of two cephalosporins, namely, cefadroxile (cefa) and cefuroxime sodium (cefu) in pharmaceutical formulations. The method is based on a reaction between cephalosporins with 1,2‐naphthoquinone‐4‐sulfonate in alkaline medium, to form fluorescent derivatives that are extracted with chloroform and subsequently measured at 610 and 605 nm after excitation at 470 and 460 nm for cefa and cefu respectively. The optimum experimental conditions have been studied. Beer's law is obeyed over the concentrations of 20–70 ng/mL and 15–40 ng/mL for cefa and cefu, respectively. The detection limits were 4.46 ng/mL and 3.02 ng/mL with a linear regression correlation coefficient of 0.9984 and 0.998, and recoveries ranging 97.50–109.96% and 95.73–98.89% for cefa and cefu, respectively. The effects of pH, temperature, reaction time, 1,2‐naphthoquinone‐4‐sulfonic concentration and extraction solvent on the determination of cefa and cefu, have been examined. The proposed method can be applied for the determination of cefa and cefu in pharmaceutical formulations in quality control laboratories. Copyright © 2013 John Wiley & Sons, Ltd.     

Calibration of multivariate scatter plots for exploratory analysis of relations within and between sets of variables in genomic research

The scatter plot is a well known and easily applicable graphical tool to explore relationships between two quantitative variables. For the exploration of relations between multiple variables, generalisations of the scatter plot are useful. We present an overview of multivariate scatter plots focussing on the following situations. Firstly, we look at a scatter plot for portraying relations between quantitative variables within one data matrix. Secondly, we discuss a similar plot for the case of qualitative variables. Thirdly, we describe scatter plots for the relationships between two sets of variables where we focus on correlations. Finally, we treat plots of the relationships between multiple response and predictor variables, focussing on the matrix of regression coefficients. We will present both known and new results, where an important original contribution concerns a procedure for the inclusion of scales for the variables in multivariate scatter plots. We provide software for drawing such scales. We illustrate the construction and interpretation of the plots by means of examples on data collected in a genomic research program on taste in tomato.     

The combination of two‐dimensional and three‐dimensional analysis methods contributes to the understanding of glioblastoma spatial heterogeneity

Heterogeneity is regarded as the major factor leading to the poor outcomes of glioblastoma (GBM) patients. However, conventional two‐dimensional (2D) analysis methods, such as immunohistochemistry and immunofluorescence, have limited capacity to reveal GBM spatial heterogeneity. Thus, we sought to develop an effective analysis strategy to increase the understanding of GBM spatial heterogeneity. Here, 2D and three‐dimensional (3D) analysis methods were compared for the examination of cell morphology, cell distribution and large intact structures, and both types of methods were employed to dissect GBM spatial heterogeneity. The results showed that 2D assays showed only cross‐sections of specimens but provided a full view. To visualize intact GBM specimens in 3D without sectioning, the optical tissue clearing methods CUBIC and iDISCO+ were used to clear opaque specimens so that they would become more transparent, after which the specimens were imaged with a two‐photon microscope. The 3D analysis methods showed specimens at a large spatial scale at cell‐level resolution and had overwhelming advantages in comparison to the 2D methods. Furthermore, in 3D, heterogeneity in terms of cell stemness, the microvasculature, and immune cell infiltration within GBM was comprehensively observed and analysed. Overall, we propose that 2D and 3D analysis methods should be combined to provide much greater detail to increase the understanding of GBM spatial heterogeneity.     

A note on pseudo‐observations and left‐truncation

Pseudo‐observations have been introduced as a way to perform regression analysis of a mean value parameter related to a right‐censored time‐to‐event outcome, such as the survival probability or the restricted mean survival time. Since the introduction of the approach there have been several extensions from the original setting. However, the proper definition and performance of pseudo‐observations under left‐truncation has not yet been addressed. Here, we look at two types of pseudo‐observations under right‐censoring and left‐truncation. We explored their performance in a simulation study and applied them to data on diabetes patients with left‐truncation.     

Some remarks about the analysis of active control studies

In an active-controlled trial, the experimental treatment can be declared to be non-inferior to the control if the confidence interval for the difference excludes a fixed pre-specified margin. Recently, some articles have discussed an alternative method where the data from the current study and placebo-controlled studies for the active control are combined together into a single test statistic to test whether a fixed fraction of the effect of the active control is preserved. It has been shown that, conditional on nuisance parameters from the active-controlled study, a fixed margin can be defined that will be operationally equivalent to this latter method. In this article, we will discuss statistical properties associated with these approaches. Specifically, the interim monitoring boundaries and level of evidence will be considered.     

The Use of Two‐Way Linear Mixed Models in Multitreatment Meta‐Analysis

Summary Meta‐analysis summarizes the results of a series of trials. When more than two treatments are included in the trials and when the set of treatments tested differs between trials, the combination of results across trials requires some care. Several methods have been proposed for this purpose, which feature under different labels, such as network meta‐analysis or mixed treatment comparisons. Two types of linear mixed model can be used for meta‐analysis. The one expresses the expected outcome of treatments as a contrast to a baseline treatment. The other uses a classical two‐way linear predictor with main effects for treatment and trial. In this article, we compare both types of model and explore under which conditions they give equivalent results. We illustrate practical advantages of the two‐way model using two published datasets. In particular, it is shown that between‐trial heterogeneity as well as inconsistency between different types of trial is straightforward to account for.     

Geometric morphometrics in entomology: Basics and applications

The recent expansion of a variety of morphometric tools has brought about a revolution in the comparison of morphology in the context of the size and shape in various fields including entomology. First, an overview of the theoretical issues of geometric morphometrics is presented with a caution about the usage of traditional morphometric measurements. Second, focus is then placed on two broad approaches as tools for geometric morphometrics; that is, the landmark‐based and the outline‐based approaches. A brief outline of the two methodologies is provided with some important cautions. The increasing trend of entomological studies in using the procedures of geometric morphometrics is then summarized. Finally, information is provided on useful toolkits such as computer software as well as codes and packages of the R statistical software that could be used in geometric morphometrics.     

Reduced rank hazard regression with fixed and time‐varying effects of the covariates

Modelling survival data from long‐term follow‐up studies presents challenges. The commonly used proportional hazards model should be extended to account for dynamic behaviour of the effects of fixed covariates. This work illustrates the use of reduced rank models in survival data, where some of the covariate effects are allowed to behave dynamically in time and some as fixed. Time‐varying effects of the covariates can be fitted by using interactions of the fixed covariates with flexible transformations of time based on b‐splines. To avoid overfitting, a reduced rank model will restrict the number of parameters, resulting in a more sensible fit to the data. This work presents the basic theory and the algorithm to fit such models. An application to breast cancer data is used for illustration of the suggested methods.     

Identifying subgroups of age and cohort effects in obesity prevalence

The obesity epidemic represents an important public health issue in the United States. Studying obesity trends across age groups over time helps to identify crucial relationships between the disease and medical treatment allowing for the development of effective prevention policies. We aim to define subgroups of age and cohort effects in obesity prevalence over time by considering an optimization approach applied to the age‐period‐cohort (APC) model. We consider a heterogeneous regression problem where the regression coefficients are age dependent and belong to subgroups with unknown grouping information. Using the APC model, we apply the alternating direction method of multipliers (ADMM) algorithm to develop a two‐step algorithm for (1) subgrouping of cohort effects based on similar characteristics and (2) subgrouping age effects over time. The proposed clustering approach is illustrated for the United States population, aged 18–79, during the period 1990–2017.     

A versatile chiral selector for determination of enantiomeric composition of fluorescent and nonfluorescent chiral molecules using steady-state fluorescence spectroscopy

A fluorescent chiral molecular micelle (FCMM), poly (sodium N-undecanoyl-L-phenylalaninate) (poly-L-SUF), was developed as a chiral selector for enantiomeric recognition and determination of enantiomeric composition of four fluorescent and four nonfluorescent chiral molecules by use of steady-state fluorescence spectroscopy. The influence of FCMM concentration, buffer pH and complexation medium on FCMM-analyte host-guest complexation, and the emission spectral properties of the resulting complexes were investigated. The chiral interactions of the analytes,1,1'-binaphthyl-2,2'-diamine, 1-(9-anthryl)-2,2,2-trifluoroethanol, propranolol, naproxen, chloromethyl menthyl ether (CME), citramalic acid, tartaric acid, and limonene (LIM), in the presence of poly-L-SUF were based on diastereomeric complex formation. The figures of merit obtained from the partial-least-squares regression modeling of the calibration samples suggested good prediction ability for the validation of six of the eight chiral analytes. Better host-guest complexation of the more hydrophobic molecules, CME and LIM, were obtained in methanol/water mixtures, resulting in better predictability of the regression models. Prediction ability of the models was evaluated by use of the root-mean-square percent relative error (RMS%RE) and was found to range from 1.77 to 15.80% (buffer), 1.26 to 7.95% (25:75 methanol/water), and 1.21 to 4.28% (75:25 methanol/water).     

Molecular cloning and expression analysis of bovine programmed death‐1

Recent work has shown that PD‐1, an immune inhibitory receptor, is involved in mechanisms for down‐regulating immune responses during tumor progression or chronic viral infection. However, in the case of bovine diseases, there have been no reports on this molecule due to lack of information about bovine PD‐1. In this study, we performed identification and preliminary characterization of the bovine PD‐1 gene in two breeds of cattle. We cloned full cDNA sequences encoding for PD‐1 from both Holstein‐Friesian and Japanese Black breeds, and found that both of the genes encoded a 282‐amino acid protein, which had a signal sequence, transmembrane domain and an immunoreceptor tyrosine‐based inhibitory motif. This bovine PD‐1 showed 72.9% and 65.6% homology to human and mouse PD‐1, respectively, both of which have been well characterized and documented. Quantitative real‐time PCR analysis showed that bovine PD‐1 is expressed predominantly in T‐cells (such as CD4⁺ and CD8⁺ cells) and among PBMCs, and is strongly upregulated on T‐cell stimulation via ConA. A limited number of cattle were tested yet, as expected, the degree of PD‐1 mRNA expression in CD4⁺ and CD8⁺ T‐cells was greater in cattle with bovine leukemia virus‐induced lymphoma than in uninfected cattle. Further studies to characterize the functions of bovine PD‐1 are therefore warranted, in order to elucidate the mechanism of the immunosuppression associated with progression of several diseases and therapy in cattle.     

Optimally weighted Z-test is a powerful method for combining probabilities in meta-analysis

The inverse normal and Fisher's methods are two common approaches for combining P-values. Whitlock demonstrated that a weighted version of the inverse normal method, or 'weighted Z-test', is superior to Fisher's method for combining P-values for one-sided T-tests. The problem with Fisher's method is that it does not take advantage of weighting and loses power to the weighted Z-test when studies are differently sized. This issue was recently revisited by Chen, who observed that Lancaster's variation of Fisher's method had higher power than the weighted Z-test. Nevertheless, the weighted Z-test has comparable power to Lancaster's method when its weights are set to square roots of sample sizes. Power can be further improved when additional information is available. Although there is no single approach that is the best in every situation, the weighted Z-test enjoys certain properties that make it an appealing choice as a combination method for meta-analysis.     

Bulked sample analysis in genetics,genomics and crop improvement

Biological assay has been based on analysis of all individuals collected from sample populations. Bulked sample analysis (BSA), which works with selected and pooled individuals, has been extensively used in gene mapping through bulked segregant analysis with biparental populations, mapping by sequencing with major gene mutants and pooled genomewide association study using extreme variants. Compared to conventional entire population analysis, BSA significantly reduces the scale and cost by simplifying the procedure. The bulks can be built by selection of extremes or representative samples from any populations and all types of segregants and variants that represent wide ranges of phenotypic variation for the target trait. Methods and procedures for sampling, bulking and multiplexing are described. The samples can be analysed using individual markers, microarrays and high‐throughput sequencing at all levels of DNA, RNA and protein. The power of BSA is affected by population size, selection of extreme individuals, sequencing strategies, genetic architecture of the trait and marker density. BSA will facilitate plant breeding through development of diagnostic and constitutive markers, agronomic genomics, marker‐assisted selection and selective phenotyping. Applications of BSA in genetics, genomics and crop improvement are discussed with their future perspectives.     

Analysis of continuous covariables in epidemiological studies: Dose‐response modelling and confounder adjustment

The analysis of continuous covariables with regression models commonly used in epidemiology are reviewed and compared. While some methods have been in use for decades, other more recent methods are not yet common or have not yet been formally described. It is shown that recently developed methods such as fractional polynomials and others are very useful to obtain dose‐response curves or for confounder adjustment. Different methods have their specific merits making it difficult to give general recommendations. The application of some of the methods is demonstrated with real data examples from epidemiological studies. Some suggestions for practical strategies in analysing continuous covariables are given.     

Detecting and adjusting for small-study effects in meta-analysis

Publication bias and related types of small-study effects threaten the validity of systematic reviews. The existence of small-study effects has been demonstrated in empirical studies. Small-study effects are graphically diagnosed by inspection of the funnel plot. Though observed funnel plot asymmetry cannot be easily linked to a specific reason, tests based on funnel plot asymmetry have been proposed. Beyond a vast range of funnel plot tests, there exist several methods for adjusting treatment effect estimates for these biases. In this article, we consider the trim-and-fill method, the Copas selection model, and more recent regression-based approaches. The methods are exemplified using a meta-analysis from the literature and compared in a simulation study, based on binary response data. They are also applied to a large set of meta-analyses. Some fundamental differences between the approaches are discussed. An assumption common to the trim-and-fill method and the Copas selection model is that the small-study effect is caused by selection. The trim-and-fill method corresponds to an unknown implicit model generated by the symmetry assumption, whereas the Copas selection model is a parametric statistical model. However, it requires a sensitivity analysis. Regression-based approaches are easier to implement and not based on a specific selection model. Both simulations and applications suggest that in the presence of strong selection both the trim-and-fill method and the Copas selection model may not fully eliminate bias, while regression-based approaches seem to be a promising alternative.     

A Y‐chromosomal comparison of the Madjars (Kazakhstan) and the Magyars (Hungary)

The Madjars are a previously unstudied population from Kazakhstan who practice a form of local exogamy in which wives are brought in from neighboring tribes, but husbands are not, so the paternal lineages remain genetically isolated within the population. Their name bears a striking resemblance to the Magyars who have inhabited Hungary for over a millennium, but whose previous history is poorly understood. We have now carried out a genetic analysis of the population structure and relationships of the Madjars, and in particular have sought to test whether or not they show a genetic link with the Magyars. We concentrated on paternal lineages because of their isolation within the Madjars and sampled males representing all extant male lineages unrelated for more than eight generations (n = 45) in the Torgay area of Kazakhstan. The Madjars show evidence of extensive genetic drift, with 24/45 carrying the same 12‐STR haplotype within haplogroup G. Genetic distances based on haplogroup frequencies were used to compare the Madjars with 37 other populations and showed that they were closest to the Hungarian population rather than their geographical neighbors. Although this finding could result from chance, it is striking and suggests that there could have been genetic contact between the ancestors of the Madjars and Magyars, and thus that modern Hungarians may trace their ancestry to Central Asia, instead of the Eastern Uralic region as previously thought. Am J Phys Anthropol 2009. © 2009 Wiley‐Liss, Inc.     

Circulating trimethylamine N‐oxide and the risk of cardiovascular diseases: a systematic review and meta‐analysis of 11 prospective cohort studies

Circulating trimethylamine N‐oxide (TMAO), a canonical metabolite from gut flora, has been related to the risk of cardiovascular disorders. However, the association between circulating TMAO and the risk of cardiovascular events has not been quantitatively evaluated. We performed a systematic review and meta‐analysis of all available cohort studies regarding the association between baseline circulating TMAO and subsequent cardiovascular events. Embase and PubMed databases were searched for relevant cohort studies. The overall hazard ratios for the developing of cardiovascular events (CVEs) and mortality were extracted. Heterogeneity among the included studies was evaluated with Cochran's Q Test and I² statistics. A random‐effect model or a fixed‐effect model was applied depending on the heterogeneity. Subgroup analysis and meta‐regression were used to evaluate the source of heterogeneity. Among the 11 eligible studies, three reported both CVE and mortality outcome, one reported only CVEs and the other seven provided mortality data only. Higher circulating TMAO was associated with a 23% higher risk of CVEs (HR = 1.23, 95% CI: 1.07–1.42, I² = 31.4%) and a 55% higher risk of all‐cause mortality (HR = 1.55, 95% CI: 1.19–2.02, I² = 80.8%). Notably, the latter association may be blunted by potential publication bias, although sensitivity analysis by omitting one study at a time did not significantly change the results. Further subgroup analysis and meta‐regression did not support that the location of the study, follow‐up duration, publication year, population characteristics or the samples of TMAO affect the results significantly. Higher circulating TMAO may independently predict the risk of subsequent cardiovascular events and mortality.     

Disease signatures are robust across tissues and experiments

Meta‐analyses combining gene expression microarray experiments offer new insights into the molecular pathophysiology of disease not evident from individual experiments. Although the established technical reproducibility of microarrays serves as a basis for meta‐analysis, pathophysiological reproducibility across experiments is not well established. In this study, we carried out a large‐scale analysis of disease‐associated experiments obtained from NCBI GEO, and evaluated their concordance across a broad range of diseases and tissue types. On evaluating 429 experiments, representing 238 diseases and 122 tissues from 8435 microarrays, we find evidence for a general, pathophysiological concordance between experiments measuring the same disease condition. Furthermore, we find that the molecular signature of disease across tissues is overall more prominent than the signature of tissue expression across diseases. The results offer new insight into the quality of public microarray data using pathophysiological metrics, and support new directions in meta‐analysis that include characterization of the commonalities of disease irrespective of tissue, as well as the creation of multi‐tissue systems models of disease pathology using public data.