ALBERT

Description: Background: There is evidence that physicians’ prescription behavior is negatively affected by the extent of their interactions with pharmaceutical companies. In order to develop and implement policies and interventions for better management of interactions, we need to understand physicians’ perspectives on this issue. Surveys addressing physicians’ interactions with pharmaceutical companies need to use validated tools to ensure the validity of their findings.ObjectiveTo assess the validity of tools used in surveys of physicians about the extent and nature of their interactions with pharmaceutical companies, and about their knowledge, beliefs and attitudes towards such interactions; and to identify those tools that have been formally validated. Methods: We developed a search strategy with the assistance of a medical librarian. We electronically searched MEDLINE and EMBASE databases in September 2015. Teams of two reviewers conducted data selection and data abstraction. They identified eligible studies in one table and then abstracted the relevant data from the studies with validated tools in another table. Tables were piloted and standardized. Results: We identified one validated questionnaire out of the 11 assessing the nature and extent of the interaction, and three validated questionnaires out of the 47 assessing knowledge, beliefs and attitudes of physicians toward the interaction. None of these validated questionnaires were used in more than one survey. Conclusion: The available supporting evidence of the issue of physicians’ interaction with pharmaceutical company is of low quality. There is a need for research to develop and validate tools to survey physicians about their interactions with pharmaceutical companies.

Description: Cyclic nucleotides have been shown to play important signaling roles in many physiological processes in plants including photosynthesis and defence. Despite this, little is known about cyclic nucleotide-depend...

Description: About 20 % of hereditary breast cancers are caused by mutations in BRCA1 and BRCA2 genes. Since BRCA1 and BRCA2 mutations may be spread throughout the gene, genetic testing is usually performed by direct sequenci...

Description: Background: Second generation sequencing has permitted detailed sequence characterisation at the whole genome level of a growing number of non-model organisms, but the data produced have short read-lengths and biased genome coverage leading to fragmented genome assemblies. The PacBio RS long-read sequencing platform offers the promise of increased read length and unbiased genome coverage and thus the potential to produce genome sequence data of a finished quality containing fewer gaps and longer contigs. However, these advantages come at a much greater cost per nucleotide and with a perceived increase in error-rate. In this investigation, we evaluated the performance of the PacBio RS sequencing platform through the sequencing and de novo assembly of the Potentilla micrantha chloroplast genome. Results: Following error-correction, a total of 28,638 PacBio RS reads were recovered with a mean read length of 1,902bp totalling 54,492,250 nucleotides and representing an average depth of coverage of 320x the chloroplast genome. The dataset covered the entire 154,959bp of the chloroplast genome in a single contig (100% coverage) compared to seven contigs (90.59% coverage) recovered from an Illumina data, and revealed no bias in coverage of GC rich regions. Post-assembly the data were largely concordant with the Illumina data generated and allowed 187 ambiguities in the Illumina data to be resolved. The additional read length also permitted small differences in the two inverted repeat regions to be assigned unambiguously. Conclusions: This is the first report to our knowledge of a chloroplast genome assembled de novo using PacBio sequence data. The PacBio RS data generated here were assembled into a single large contig spanning the P. micrantha chloroplast genome, with a higher degree of accuracy than an Illumina dataset generated at a much greater depth of coverage, due to longer read lengths and lower GC bias in the data. The results we present suggest PacBio data will be of immense utility for the development of genome sequence assemblies containing fewer unresolved gaps and ambiguities and a significantly smaller number of contigs than could be produced using short-read sequence data alone.

Description: In many species floral senescence is coordinated by ethylene. Endogenous levels rise, and exogenous application accelerates senescence. Furthermore, floral senescence is often associated with increased reactiv...

Description: Background: Once considered an affliction of people in high-income countries, diabetes mellitus is increasingly seen as a global epidemic. However, for many countries very little is known about the prevalence of diabetes and its complications. This study aims to estimate the prevalence of diabetes, and diabetic retinopathy, in adults in Timor-Leste. Methods: From March 2013 to May 2014, adult patients being assessed for cataract surgery at the Sentru Matan Nasional (National Eye Centre) in Dili, Timor-Leste had a point-of-care HbA1c measurement performed on the DCA Vantage device (Siemens Ltd) under a quality framework. A diagnostic cut-off of 6.5% (48 mmol/mol) HbA1c was used for diagnosis of diabetes. Ocular examination, blood pressure, demographic and general health data were also collected. Diabetic retinopathy assessment was carried out by ophthalmologists. Results: A total of 283 people [mean age 63.6 years (range 20–90 years)] were tested and examined during the study period. Forty-three people (15.2%) were found to have diabetes, with a mean HbA1c of 9.5% (77 mmol/mol). Of these, 27 (62.9%) were newly diagnosed, with a mean HbA1c of 9.7% (83 mmol/mol) and a range of 6.6–14% (49–130 mmol/mol). Nearly half (48.1%) of people newly diagnosed with diabetes had an HbA1c over 10.0% (86 mmol/mol). Of those with known diabetes, only 68.8% were receiving any treatment. Mean HbA1c for treated patients was 9.9% (85 mmol/mol). Diabetic retinopathy was identified in 18.6% of people with diabetes, of whom half had no previous diagnosis of diabetes. Conclusions: This study estimates the prevalence of diabetes at 15% in adults in Timor-Leste, a substantial proportion of whom have evidence of diabetic retinopathy. This is consistent with regional estimates. With the majority of patients undiagnosed, and management of people known to have diabetes largely inadequate, point-of-care testing is a valuable tool to assist with diabetes case detection and management. Whilst only a preliminary estimate, our data provides important impetus for further investigation of the prevalence and impact of diabetes in Timor-Leste. It provides guidance that further investment is required in expanding testing, as well as in prevention and treatment.

Description: Background PAM, a nearest shrunken centroid method (NSC), is a popular classification method for high-dimensional data. ALP and AHP are NSC algorithms that were proposed to improve upon PAM. The NSC methods base their classification rules on shrunken centroids; in practice the amount of shrinkage is estimated minimizing the overall cross-validated (CV) error rate.Results We show that when data are class-imbalanced the three NSC classifiers are biased towards the majority class. The bias is larger when the number of variables or class-imbalance is larger and/or the differences between classes are smaller. To diminish the class-imbalance problem of the NSC classifiers we propose to estimate the amount of shrinkage by maximizing the CV geometric mean of the class-specific predictive accuracies (g-means).Conclusions The results obtained on simulated and real high-dimensional class-imbalanced data show that our approach outperforms the currently used strategy based on the minimization of the overall error rate when NSC classifiers are biased towards the majority class. The number of variables included in the NSC classifiers when using our approach is much smaller than with the original approach. This result is supported by experiments on simulated and real high-dimensional class-imbalanced data.

Description: Background: Batch effect is one type of variability that is not of primary interest but ubiquitous in sizable genomic experiments. To minimize the impact of batch effects, an ideal experiment design should ensure the even distribution of biological groups and confounding factors across batches. However, due to the practical complications, the availability of the final collection of samples in genomics study might be unbalanced and incomplete, which, without appropriate attention in sample-to-batch allocation, could lead to drastic batch effects. Therefore, it is necessary to develop effective and handy tool to assign collected samples across batches in an appropriate way in order to minimize batch effects. Results: We describe OSAT (Optimal Sample Assignment Tool), a bioconductor package designed for automated sample-to-batch allocations in genomics experiments. Conclusions: OSAT is developed to facilitate the allocation of collected samples to different batches in genomics study. Through optimizing the even distribution of samples in groups of biological interest into different batches, it can reduce the confounding or correlation between batches and the biological variables of interest. It can also optimize the homogeneous distribution of confounding factors across batches. It can handle challenging instances where incomplete and unbalanced sample collections are involved as well as ideally balanced designs.

Description: Background: In clinical research prediction models are used to accurately predict the outcome of the patients based on some of their characteristics. For high-dimensional prediction models (the number of variables greatly exceeds the number of samples) the choice of an appropriate classifier is crucial as it was observed that no single classification algorithm performs optimally for all types of data. Boosting was proposed as a method that combines the classification results obtained using base classifiers, where the sample weights are sequentially adjusted based on the performance in previous iterations. Generally boosting outperforms any individual classifier, but studies with high-dimensional data showed that the most standard boosting algorithm, AdaBoost.M1, cannot significantly improve the performance of its base classier. Recently other boosting algorithms were proposed (Gradient boosting, Stochastic Gradient boosting, LogitBoost); they were shown to perform better than AdaBoost.M1 but their performance was not evaluated for high-dimensional data. Results: In this paper we use simulation studies and real gene-expression data sets to evaluate the performance of boosting algorithms when data are high-dimensional. Our results confirm that AdaBoost.M1 can perform poorly in this setting, often failing to improve the performance of its base classifier. We provide the explanation for this and propose a modification, AdaBoost.M1.ICV, which uses cross-validated estimates of the prediction errors and outperforms the original algorithm when data are high-dimensional. The use of AdaBoost.M1.ICV is advisable when the base classifier overfits the training data: the number of variables is large, the number of samples is small, and/or the difference between the classes is large. To a lesser extent also Gradient boosting suffers from similar problems. Contrary to the findings for the low-dimensional data, shrinkage does not improve the performance of Gradient boosting when data are high-dimensional, however it is beneficial for Stochastic Gradient boosting, which outperformed the other boosting algorithms in our analyses. LogitBoost suffers from overfitting and generally performs poorly. Conclusions: The results show that boosting can substantially improve the performance of its base classifier also when data are high-dimensional. However, not all boosting algorithms perform equally well. LogitBoost, AdaBoost.M1 and Gradient boosting seem less useful for this type of data. Overall, Stochastic Gradient boosting with shrinkage and AdaBoost.M1.ICV seem to be the preferable choices for high-dimensional class-prediction.

Description: Background: Prediction models are used in clinical research to develop rules that can be used to accurately predict the outcome of the patients based on some of their characteristics. They represent a valuable tool in the decision making process of clinicians and health policy makers, as they enable them to estimate the probability that patients have or will develop a disease, will respond to a treatment, or that their disease will recur. The interest devoted to prediction models in the biomedical community has been growing in the last few years. Often the data used to develop the prediction models are class-imbalanced as only few patients experience the event (and therefore belong to minority class). Results: Prediction models developed using class-imbalanced data tend to achieve sub-optimal predictive accuracy in the minority class. This problem can be diminished by using sampling techniques aimed at balancing the class distribution. These techniques include under- and oversampling, where a fraction of the majority class samples are retained in the analysis or new samples from the minority class are generated. The correct assessment of how the prediction model is likely to perform on independent data is of crucial importance; in the absence of an independent data set, cross-validation is normally used. While the importance of correct cross-validation is well documented in the biomedical literature, the challenges posed by the joint use of sampling techniques and cross-validation have not been addressed. Conclusions: We show that care must be taken to ensure that cross-validation is performed correctly on sampled data, and that the risk of overestimating the predictive accuracy is greater when oversampling techniques are used. Examples based on the re-analysis of real datasets and simulation studies are provided. We identify some results from the biomedical literature where the incorrect cross-validation was performed, where we expect that the performance of oversampling techniques was heavily overestimated.