A critical evaluation of network and pathway based classifiers for outcome prediction in breast cancer

Recently, several classifiers that combine primary tumor data, like gene expression data, and secondary data sources, such as protein-protein interaction networks, have been proposed for predicting outcome in breast cancer. In these approaches, new composite features are typically constructed by aggregating the expression levels of several genes. The secondary data sources are employed to guide this aggregation. Although many studies claim that these approaches improve classification performance over single gene classifiers, the gain in performance is difficult to assess. This stems mainly from the fact that different breast cancer data sets and validation procedures are employed to assess the performance. Here we address these issues by employing a large cohort of six breast cancer data sets as benchmark set and by performing an unbiased evaluation of the classification accuracies of the different approaches. Contrary to previous claims, we find that composite feature classifiers do not outperform simple single gene classifiers. We investigate the effect of (1) the number of selected features; (2) the specific gene set from which features are selected; (3) the size of the training set and (4) the heterogeneity of the data set on the performance of composite feature and single gene classifiers. Strikingly, we find that randomization of secondary data sources, which destroys all biological information in these sources, does not result in a deterioration in performance of composite feature classifiers. Finally, we show that when a proper correction for gene set size is performed, the stability of single gene sets is similar to the stability of composite feature sets. Based on these results there is currently no reason to prefer prognostic classifiers based on composite features over single gene classifiers for predicting outcome in breast cancer

Evaluation of a website providing information on regional health care services for patients with rheumatoid arthritis: an observational study

Studies on the effectiveness of information provision for patients with arthritis through the Internet are scarce. This study aimed to describe rheumatoid arthritis (RA) patients’ knowledge and information needs before and after launching a website providing information on regional health care services for patients with rheumatic conditions. The intervention consisted of a weekly updated website comprising practical information on regional health care services for patients with arthritis. In addition, patients were offered information leaflets and an information meeting. Before (T1) and 24 months after (T2) the website was launched, a random sample of 400 RA patients filled in a questionnaire regarding knowledge and information need (scores 0–18) about accessibility and contents of 18 regional health care services. Two hundred and fifty-one patients returned the questionnaire (response rate 63%) at T1 and 200 patients (50%) at T2, respectively, with 160 paired observations (112 females (70%), mean age 60.4 years (SD 9.9)). The total score for insufficient knowledge about contents decreased from 9.3 (SD 4.9) to 8.5 (SD 4.8; p = 0.03) and for accessibility from 8.6 (SD 4.7) to 8.4 (SD 4.9; p = 0.59). Total score for information need about contents decreased from 4.2 (SD 4.5) to 1.9 (SD 2.9; p < 0.01) and for accessibility from 3.6 (SD 4.5) to 1.4 (SD 2.4; p < 0.01) (paired t-tests)

Aseptic Meningitis in Children: Analysis of 506 Cases

BACKGROUND: Non-polio human enteroviruses are the leading cause of aseptic meningitis in children. The role of enterovirus PCR for diagnosis and management of aseptic meningitis has not been fully explored. METHODOLOGY/PRINCIPAL FINDINGS: A retrospective study was conducted to determine the epidemiological, clinical, and laboratory characteristics of aseptic meningitis and to evaluate the role of enterovirus PCR for the diagnosis and management of this clinical entity. The medical records of children who had as discharge diagnosis aseptic or viral meningitis were reviewed. A total of 506 children, median age 5 years, were identified. The annual incidence rate was estimated to be 17/100,000 children less than 14 years of age. Most of the cases occurred during summer (38%) and autumn (24%). The dominant clinical symptoms were fever (98%), headache (94%) and vomiting (67%). Neck stiffness was noted in 60%, and irritation in 46% of the patients. The median number of CSF cell count was 201/mm(3) with polymorphonuclear predominance (>50%) in 58.3% of the cases. Enterovirus RNA was detected in CSF in 47 of 96 (48.9%) children tested. Children with positive enterovirus PCR had shorter hospitalization stay as compared to children who had negative PCR or to children who were not tested (P = 0.01). There were no serious complications or deaths. CONCLUSIONS: Enteroviruses accounted for approximately one half of cases of aseptic meningitis. PCR may reduce the length of hospitalization and plays important role in the diagnosis and management of children with aseptic meningitis

An adaptive version of k-medoids to deal with the uncertainty in clustering heterogeneous data using an intermediary fusion approach

This paper introduces Hk-medoids, a modified version of the standard k-medoids algorithm. The modification extends the algorithm for the problem of clustering complex heterogeneous objects that are described by a diversity of data types, e.g. text, images, structured data and time series. We first proposed an intermediary fusion approach to calculate fused similarities between objects, SMF, taking into account the similarities between the component elements of the objects using appropriate similarity measures. The fused approach entails uncertainty for incomplete objects or for objects which have diverging distances according to the different component. Our implementation of Hk-medoids proposed here works with the fused distances and deals with the uncertainty in the fusion process. We experimentally evaluate the potential of our proposed algorithm using five datasets with different combinations of data types that define the objects. Our results show the feasibility of the our algorithm, and also they show a performance enhancement when comparing to the application of the original SMF approach in combination with a standard k-medoids that does not take uncertainty into account. In addition, from a theoretical point of view, our proposed algorithm has lower computation complexity than the popular PAM implementation

Epilepsy Caused by an Abnormal Alternative Splicing with Dosage Effect of the SV2A Gene in a Chicken Model

Photosensitive reflex epilepsy is caused by the combination of an individual's enhanced sensitivity with relevant light stimuli, such as stroboscopic lights or video games. This is the most common reflex epilepsy in humans; it is characterized by the photoparoxysmal response, which is an abnormal electroencephalographic reaction, and seizures triggered by intermittent light stimulation. Here, by using genetic mapping, sequencing and functional analyses, we report that a mutation in the acceptor site of the second intron of SV2A (the gene encoding synaptic vesicle glycoprotein 2A) is causing photosensitive reflex epilepsy in a unique vertebrate model, the Fepi chicken strain, a spontaneous model where the neurological disorder is inherited as an autosomal recessive mutation. This mutation causes an aberrant splicing event and significantly reduces the level of SV2A mRNA in homozygous carriers. Levetiracetam, a second generation antiepileptic drug, is known to bind SV2A, and SV2A knock-out mice develop seizures soon after birth and usually die within three weeks. The Fepi chicken survives to adulthood and responds to levetiracetam, suggesting that the low-level expression of SV2A in these animals is sufficient to allow survival, but does not protect against seizures. Thus, the Fepi chicken model shows that the role of the SV2A pathway in the brain is conserved between birds and mammals, in spite of a large phylogenetic distance. The Fepi model appears particularly useful for further studies of physiopathology of reflex epilepsy, in comparison with induced models of epilepsy in rodents. Consequently, SV2A is a very attractive candidate gene for analysis in the context of both mono- and polygenic generalized epilepsies in humans

The undebated issue of justice: silent discourses in Dutch flood risk management

Flood risk for all types of flooding is projected to increase based on climate change projections and increases in damage potential. These challenges are likely to aggravate issues of justice in flood risk management (henceforth FRM). Based on a discursive-institutionalist perspective, this paper explores justice in Dutch FRM: how do institutions allocate the responsibilities and costs for FRM for different types of flooding? What are the underlying conceptions of justice? What are the future challenges with regard to climate change? The research revealed that a dichotomy is visible in the Dutch approach to FRM: despite an abundance of rules, regulations and resources spent, flood risk or its management, are only marginally discussed in terms of justice. Despite that the current institutional arrangement has material outcomes that treat particular groups of citizens differently, depending on the type of flooding they are prone to, area they live in (unembanked/embanked) or category of user (e.g. household, industry, farmer). The paper argues that the debate on justice will (re)emerge, since the differences in distributional outcomes are likely to become increasingly uneven as a result of increasing flood risk. The Netherlands should be prepared for this debate by generating the relevant facts and figures. An inclusive debate on the distribution of burdens of FRM could contribute to more effective and legitimate FRM

Effects of sample size on robustness and prediction accuracy of a prognostic gene signature

<p>Abstract</p> <p>Background</p> <p>Few overlap between independently developed gene signatures and poor inter-study applicability of gene signatures are two of major concerns raised in the development of microarray-based prognostic gene signatures. One recent study suggested that thousands of samples are needed to generate a robust prognostic gene signature.</p> <p>Results</p> <p>A data set of 1,372 samples was generated by combining eight breast cancer gene expression data sets produced using the same microarray platform and, using the data set, effects of varying samples sizes on a few performances of a prognostic gene signature were investigated. The overlap between independently developed gene signatures was increased linearly with more samples, attaining an average overlap of 16.56% with 600 samples. The concordance between predicted outcomes by different gene signatures also was increased with more samples up to 94.61% with 300 samples. The accuracy of outcome prediction also increased with more samples. Finally, analysis using only Estrogen Receptor-positive (ER+) patients attained higher prediction accuracy than using both patients, suggesting that sub-type specific analysis can lead to the development of better prognostic gene signatures</p> <p>Conclusion</p> <p>Increasing sample sizes generated a gene signature with better stability, better concordance in outcome prediction, and better prediction accuracy. However, the degree of performance improvement by the increased sample size was different between the degree of overlap and the degree of concordance in outcome prediction, suggesting that the sample size required for a study should be determined according to the specific aims of the study.</p

Cost-effectiveness of collaborative care including PST and an antidepressant treatment algorithm for the treatment of major depressive disorder in primary care; a randomised clinical trial

BACKGROUND: Depressive disorder is currently one of the most burdensome disorders worldwide. Evidence-based treatments for depressive disorder are already available, but these are used insufficiently, and with less positive results than possible. Earlier research in the USA has shown good results in the treatment of depressive disorder based on a collaborative care approach with Problem Solving Treatment and an antidepressant treatment algorithm, and research in the UK has also shown good results with Problem Solving Treatment. These treatment strategies may also work very well in the Netherlands too, even though health care systems differ between countries. METHODS/DESIGN: This study is a two-armed randomised clinical trial, with randomization on patient-level. The aim of the trial is to evaluate the treatment of depressive disorder in primary care in the Netherlands by means of an adapted collaborative care framework, including contracting and adherence-improving strategies, combined with Problem Solving Treatment and antidepressant medication according to a treatment algorithm. Forty general practices will be randomised to either the intervention group or the control group. Included will be patients who are diagnosed with moderate to severe depression, based on DSM-IV criteria, and stratified according to comorbid chronic physical illness. Patients in the intervention group will receive treatment based on the collaborative care approach, and patients in the control group will receive care as usual. Baseline measurements and follow up measures (3, 6, 9 and 12 months) are assessed using questionnaires and an interview. The primary outcome measure is severity of depressive symptoms, according to the PHQ9. Secondary outcome measures are remission as measured with the PHQ9 and the IDS-SR, and cost-effectiveness measured with the TiC-P, the EQ-5D and the SF-36. DISCUSSION: In this study, an American model to enhance care for patients with a depressive disorder, the collaborative care model, will be evaluated for effectiveness in the primary care setting. If effective across the Atlantic and across different health care systems, it is also likely to be an effective strategy to implement in the treatment of major depressive disorder in the Netherlands

Built Shallow to Maintain Homeostasis and Persistent Infection: Insight into the Transcriptional Regulatory Network of the Gastric Human Pathogen Helicobacter pylori

Transcriptional regulatory networks (TRNs) transduce environmental signals into coordinated output expression of the genome. Accordingly, they are central for the adaptation of bacteria to their living environments and in host–pathogen interactions. Few attempts have been made to describe a TRN for a human pathogen, because even in model organisms, such as Escherichia coli, the analysis is hindered by the large number of transcription factors involved. In light of the paucity of regulators, the gastric human pathogen Helicobacter pylori represents a very appealing system for understanding how bacterial TRNs are wired up to support infection in the host. Herein, we review and analyze the available molecular and “-omic” data in a coherent ensemble, including protein–DNA and protein–protein interactions relevant for transcriptional control of pathogenic responses. The analysis covers ∼80% of the annotated H. pylori regulators, and provides to our knowledge the first in-depth description of a TRN for an important pathogen. The emerging picture indicates a shallow TRN, made of four main modules (origons) that process the physiological responses needed to colonize the gastric niche. Specific network motifs confer distinct transcriptional response dynamics to the TRN, while long regulatory cascades are absent. Rather than having a plethora of specialized regulators, the TRN of H. pylori appears to transduce separate environmental inputs by using different combinations of a small set of regulators

A Jurisprudential Analysis of Government Intervention and Prenatal Drug Abuse

This article takes a different approach in considering the problem of prenatal drug abuse. After briefly discussing government intervention and constitutional issues, this article will consider the concept of duty and correlative rights. This discussion of duty and correlative rights suggests that the government can take measures to curb prenatal drug use without recognizing fetal rights. The article concludes with a discussion of the utility of criminal legislation as compared to public health legislation that treats drug addiction as a disease requiring treatment. As formulated, the proposal for public health legislation is not based on any concept of fetal rights. Instead, it is based on the recognition of societal interests, as well as the woman’s needs