Effects of specific allergen immunotherapy on biological markers and clinical parameters in asthmatic children: a controlled-real life study

Background

Allergen-specific immunotherapy (AIT) is the only treatment able to change the natural course of allergic diseases. We aimed at investigating the clinical efficacy of SLITOR (Serbian registered vaccine for sublingual allergen specific immunotherapy).

Methods

7–18 years old children with allergic asthma and rhinitis were enrolled and addressed to the active (AIT plus pharmacological treatment) or control (standard pharmacological treatment only) group. Clinical and medications scores, lung function and exhaled FeNO were measured at baseline and at every follow-up.

Results

There was a significant improvement in both nasal and asthma symptom scores as well as in medication score in SLIT group. SLIT showed an important influence on lung function and airway inflammation.

Conclusions

Our data showed that SLITOR was effective not only in terms of patient reported outcomes but an improvement of pulmonary function and decrease of lower airway inflammation were also observed.

     

Identification of a urine metabolite constellation characteristic for kidney allograft rejection

Introduction

Allograft rejection is still an important complication after kidney transplantation. Currently, monitoring of these patients mostly relies on the measurement of serum creatinine and clinical evaluation. The gold standard for diagnosing allograft rejection, i.e. performing a renal biopsy is invasive and expensive. So far no adequate biomarkers are available for routine use.

Objectives

We aimed to develop a urine metabolite constellation that is characteristic for acute renal allograft rejection.

Methods

NMR-Spectroscopy was applied to a training cohort of transplant recipients with and without acute rejection.

Results

We obtained a metabolite constellation of four metabolites that shows promising performance to detect renal allograft rejection in the cohorts used (AUC of 0.72 and 0.74, respectively).

Conclusion

A metabolite constellation was defined with the potential for further development of an in-vitro diagnostic test that can support physicians in their clinical assessment of a kidney transplant patient.

     

archiDART: an R package for the automated computation of plant root architectural traits

Background and aims

In order to analyse root system architectures (RSAs) from captured images, a variety of manual (e.g. Data Analysis of Root Tracings, DART), semi-automated and fully automated software packages have been developed. These tools offer complementary approaches to study RSAs and the use of the Root System Markup Language (RSML) to store RSA data makes the comparison of measurements obtained with different (semi-) automated root imaging platforms easier. The throughput of the data analysis process using exported RSA data, however, should benefit greatly from batch analysis in a generic data analysis environment (R software).

Methods

We developed an R package (archiDART) with five functions. It computes global RSA traits, root growth rates, root growth directions and trajectories, and lateral root distribution from DART-generated and/or RSML files. It also has specific plotting functions designed to visualise the dynamics of root system growth.

Results

The results demonstrated the ability of the package’s functions to compute relevant traits for three contrasted RSAs (Brachypodium distachyon [L.] P. Beauv., Hevea brasiliensis Müll. Arg. and Solanum lycopersicum L.).

Conclusions

This work extends the DART software package and other image analysis tools supporting the RSML format, enabling users to easily calculate a number of RSA traits in a generic data analysis environment.

     

A group matrix representation relevant to scales of measurement of clinical disease states via stratified vectors

Background

Previously, we applied basic group theory and related concepts to scales of measurement of clinical disease states and clinical findings (including laboratory data). To gain a more concrete comprehension, we here apply the concept of matrix representation, which was not explicitly exploited in our previous work.

Methods

Starting with a set of orthonormal vectors, called the basis, an operator R_j (an N-tuple patient disease state at the j-th session) was expressed as a set of stratified vectors representing plural operations on individual components, so as to satisfy the group matrix representation.

Results

The stratified vectors containing individual unit operations were combined into one-dimensional square matrices [R_j]s. The [R_j]s meet the matrix representation of a group (ring) as a K-algebra. Using the same-sized matrix of stratified vectors, we can also express changes in the plural set of [R_j]s. The method is demonstrated on simple examples.

Conclusions

Despite the incompleteness of our model, the group matrix representation of stratified vectors offers a formal mathematical approach to clinical medicine, aligning it with other branches of natural science.

     

A lost opportunity for science: journals promote data sharing in metabolomics but do not enforce it

Introduction

Data sharing is being increasingly required by journals and has been heralded as a solution to the ‘replication crisis’.

Objectives

(i) Review data sharing policies of journals publishing the most metabolomics papers associated with open data and (ii) compare these journals’ policies to those that publish the most metabolomics papers.

Methods

A PubMed search was used to identify metabolomics papers. Metabolomics data repositories were manually searched for linked publications.

Results

Journals that support data sharing are not necessarily those with the most papers associated to open metabolomics data.

Conclusion

Further efforts are required to improve data sharing in metabolomics.

     

Integrated analysis of microRNA-target interactions with clinical outcomes for cancers

Background

Clinical statement alone is not enough to predict the progression of disease. Instead, the gene expression profiles have been widely used to forecast clinical outcomes. Many genes related to survival have been identified, and recently miRNA expression signatures predicting patient survival have been also investigated for several cancers. However, miRNAs and their target genes associated with clinical outcomes have remained largely unexplored.

Methods

Here, we demonstrate a survival analysis based on the regulatory relationships of miRNAs and their target genes. The patient survivals for the two major cancers, ovarian cancer and glioblastoma multiforme (GBM), are investigated through the integrated analysis of miRNA-mRNA interaction pairs.

Results

We found that there is a larger survival difference between two patient groups with an inversely correlated expression profile of miRNA and mRNA. It supports the idea that signatures of miRNAs and their targets related to cancer progression can be detected via this approach.

Conclusions

This integrated analysis can help to discover coordinated expression signatures of miRNAs and their target mRNAs that can be employed for therapeutics in human cancers.

     

Male breast cancer: thirteen years experience of a single center

Background

This retrospective study analysed the epidemiological, clinical, and therapeutic profiles of breast cancer in males.

Methods

We report our experience at the Hospital of the University of Baskent, where 20 cases of male breast cancer were observed and treated between 1995–2008.

Results

Median age at presentation was 66,7 ± 10,9 years. Average follow-up was 63 ± 18,5 months. The main presenting symptom was a mass in 65% of cases (13 patients). Ýnvasive ductal carcinoma was the most frequent pathologic type (70% of cases).

Conclusion

Male breast cancer patients have an incidence of prostate cancer higher than would be predicted in the general population. Cause of men have a higher rate of ER positivity the responses with hormonal agents are good.

     

Identifying functional relationships within sets of co-expressed genes by combining upstream regulatory motif analysis and gene expression information

Background

Existing clustering approaches for microarray data do not adequately differentiate between subsets of co-expressed genes. We devised a novel approach that integrates expression and sequence data in order to generate functionally coherent and biologically meaningful subclusters of genes. Specifically, the approach clusters co-expressed genes on the basis of similar content and distributions of predicted statistically significant sequence motifs in their upstream regions.

Results

We applied our method to several sets of co-expressed genes and were able to define subsets with enrichment in particular biological processes and specific upstream regulatory motifs.

Conclusions

These results show the potential of our technique for functional prediction and regulatory motif identification from microarray data.

     

Effect of a web-based audit and feedback intervention with outreach visits on the clinical performance of multidisciplinary teams: a cluster-randomized trial in cardiac rehabilitation

Background

The objective of this study was to assess the effect of a web-based audit and feedback (A&F) intervention with outreach visits to support decision-making by multidisciplinary teams.

Methods

We performed a multicentre cluster-randomized trial within the field of comprehensive cardiac rehabilitation (CR) in the Netherlands. Our participants were multidisciplinary teams in Dutch CR centres who were enrolled in the study between July 2012 and December 2013 and received the intervention for at least 1 year. The intervention included web-based A&F with feedback on clinical performance, facilities for goal setting and action planning, and educational outreach visits. Teams were randomized either to receive feedback that was limited to psychosocial rehabilitation (study group A) or to physical rehabilitation (study group B). The main outcome measure was the difference in performance between study groups in 11 care processes and six patient outcomes, measured at patient level. Secondary outcomes included effects on guideline concordance for the four main CR therapies.

Results

Data from 18 centres (14,847 patients) were analysed, of which 12 centres (9353 patients) were assigned to group A and six (5494 patients) to group B. During the intervention, a total of 233 quality improvement goals was identified by participating teams, of which 49 (21%) were achieved during the study period. Except for a modest improvement in data completeness (4.5% improvement per year; 95% CI 0.65 to 8.36), we found no effect of our intervention on any of our primary or secondary outcome measures.

Conclusions

Within a multidisciplinary setting, our web-based A&F intervention engaged teams to define local performance improvement goals but failed to support them in actually completing the improvement actions that were needed to achieve those goals. Future research should focus on improving the actionability of feedback on clinical performance and on addressing the socio-technical perspective of the implementation process.

Trial registration

NTR3251

     

Internal mammary artery injury during percutaneous coronary intervention: a case report

Background

Percutaneous coronary intervention (PCI) is widely used to treat coronary artery disease (CAD). However, complications of PCI are inevitable. Internal mammary artery (IMA) injury is an infrequent but potentially lethal complication of PCI.

Case presentation

A 78-year-old man was diagnosed with multivessel lesions by coronary angiography. The IMA was injured during PCI, then cured by early identification and active rescue.

Conclusions

This is the first reported case, to our knowledge, of injury to the IMA during PCI. We we report this case to discuss how to treat this injury effectively and avoid this complication during clinical therapy.

     

Preclinical and clinical studies on cancer-associated cachexia

Background

Cancer cachexia is the wasting condition that is often seen in advanced stage cancer patients. This wasting is largely attributable to a systemic and progressive loss of skeletal muscle mass that greatly hinders performance of normal daily activities, resulting in reduced quality of life. Moreover, it negatively influences the prognosis of cancer patients. A general consensus in the field is that the loss of muscle mass is due both to an increase in protein degradation and a decrease in protein synthesis. Recent studies using preclinical models for studying cachexia have been useful in identifying the contribution of inflammatory cytokines (e.g. tumor necrosis factor-α and Interleukin-6), and myostatin receptors (e.g. the type IIB activin receptor) to cachexia development, and have led to several clinical trials. However, many questions remain about the molecular mechanisms thought to play a role in the development of cachexia.

Methods

We conducted a literature search using search engines, such as PubMed and Google Scholar to identify publications within the cancer cachexia field.

Results

We summarized our current knowledge of: 1) the driving mechanisms of cancer cachexia, 2) the preclinical models available for studying the condition, and 3) the findings of recent clinical trials.

Conclusion

Cancer cachexia is a complex and variable condition that currently has no standard effective therapeutic treatment. Further studies are desperately needed to better understand this condition and develop effective combination treatments for patients.

     

Challenges in implementing model-based phase I designs in a grant-funded clinical trials unit

Background

For a clinical trials unit to run its first model-based, phase I trial, the statistician, chief investigator, and trial manager must all acquire a new set of skills. These trials also require a different approach to funding and data collection.

Challenges and discussion

From the statisticians’ viewpoint, we highlight what is needed to move from running rule-based, early-phase trials to running a model-based phase I study as we experienced it in our trials unit located in the United Kingdom. Our example is CHARIOT, a dose-finding trial using the time-to-event continual reassessment method. It consists of three stages and aims to discover the maximum tolerated dose of the combination of radiotherapy, chemotherapy, and the ataxia telangiectasia mutated Rad3-related inhibitor M6620 (previously known as VX-970) in patients with oesophageal cancer. We present the challenges we faced in designing this trial and how we overcame them as a way of demystifying the conduct of a model-based trial in a grant-funded clinical trials unit.

Conclusions

Although we appreciate that undertaking model-based trials requires additional time and effort, they are feasible to implement and, once suitable tools such as guiding publications and document templates become available, the design and set-up process will be easier and more efficient.

     

Predicting clinical outcomes in neuroblastoma with genomic data integration

Background

Neuroblastoma is a heterogeneous disease with diverse clinical outcomes. Current risk group models require improvement as patients within the same risk group can still show variable prognosis. Recently collected genome-wide datasets provide opportunities to infer neuroblastoma subtypes in a more unified way. Within this context, data integration is critical as different molecular characteristics can contain complementary signals. To this end, we utilized the genomic datasets available for the SEQC cohort patients to develop supervised and unsupervised models that can predict disease prognosis.

Results

Our supervised model trained on the SEQC cohort can accurately predict overall survival and event-free survival profiles of patients in two independent cohorts. We also performed extensive experiments to assess the prediction accuracy of high risk patients and patients without MYCN amplification. Our results from this part suggest that clinical endpoints can be predicted accurately across multiple cohorts. To explore the data in an unsupervised manner, we used an integrative clustering strategy named multi-view kernel k-means (MVKKM) that can effectively integrate multiple high-dimensional datasets with varying weights. We observed that integrating different gene expression datasets results in a better patient stratification compared to using these datasets individually. Also, our identified subgroups provide a better Cox regression model fit compared to the existing risk group definitions.

Conclusion

Altogether, our results indicate that integration of multiple genomic characterizations enables the discovery of subtypes that improve over existing definitions of risk groups. Effective prediction of survival times will have a direct impact on choosing the right therapies for patients.

Reviewers

This article was reviewed by Susmita Datta, Wenzhong Xiao and Ziv Shkedy.

     

Untargeted metabolomics suffers from incomplete raw data processing

Introduction

Untargeted metabolomics is a powerful tool for biological discoveries. To analyze the complex raw data, significant advances in computational approaches have been made, yet it is not clear how exhaustive and reliable the data analysis results are.

Objectives

Assessment of the quality of raw data processing in untargeted metabolomics.

Methods

Five published untargeted metabolomics studies, were reanalyzed.

Results

Omissions of at least 50 relevant compounds from the original results as well as examples of representative mistakes were reported for each study.

Conclusion

Incomplete raw data processing shows unexplored potential of current and legacy data.

     

Inferring drug-disease associations from integration of chemical,genomic and phenotype data using network propagation

Background

During the last few years, the knowledge of drug, disease phenotype and protein has been rapidly accumulated and more and more scientists have been drawn the attention to inferring drug-disease associations by computational method. Development of an integrated approach for systematic discovering drug-disease associations by those informational data is an important issue.

Methods

We combine three different networks of drug, genomic and disease phenotype and assign the weights to the edges from available experimental data and knowledge. Given a specific disease, we use our network propagation approach to infer the drug-disease associations.

Results

We apply prostate cancer and colorectal cancer as our test data. We use the manually curated drug-disease associations from comparative toxicogenomics database to be our benchmark. The ranked results show that our proposed method obtains higher specificity and sensitivity and clearly outperforms previous methods. Our result also show that our method with off-targets information gets higher performance than that with only primary drug targets in both test data.

Conclusions

We clearly demonstrate the feasibility and benefits of using network-based analyses of chemical, genomic and phenotype data to reveal drug-disease associations. The potential associations inferred by our method provide new perspectives for toxicogenomics and drug reposition evaluation.

     

MIDAS-G: a computational platform for investigating fragmentation rules of tandem mass spectrometry in metabolomics

Introduction

Tandem mass spectrometry (MS/MS) has been widely used for identifying metabolites in many areas. However, computationally identifying metabolites from MS/MS data is challenging due to the unknown of fragmentation rules, which determine the precedence of chemical bond dissociation. Although this problem has been tackled by different ways, the lack of computational tools to flexibly represent adjacent structures of chemical bonds is still a long-term bottleneck for studying fragmentation rules.

Objectives

This study aimed to develop computational methods for investigating fragmentation rules by analyzing annotated MS/MS data.

Methods

We implemented a computational platform, MIDAS-G, for investigating fragmentation rules. MIDAS-G processes a metabolite as a simple graph and uses graph grammars to recognize specific chemical bonds and their adjacent structures. We can apply MIDAS-G to investigate fragmentation rules by adjusting bond weights in the scoring model of the metabolite identification tool and comparing metabolite identification performances.

Results

We used MIDAS-G to investigate four bond types on real annotated MS/MS data in experiments. The experimental results matched data collected from wet labs and literature. The effectiveness of MIDAS-G was confirmed.

Conclusion

We developed a computational platform for investigating fragmentation rules of tandem mass spectrometry. This platform is freely available for download.

     

Production,purification, characterization,immobilization, and application of Serrapeptase: a review

Background

Serrapeptase is a proteolytic enzyme with many favorable biological properties like anti-inflammatory, analgesic, anti-bacterial, fibrinolytic properties and hence, is widely used in clinical practice for the treatment of many diseases. Although Serrapeptase is widely used, there are very few published papers and the information available about the enzyme is very meagre. Hence this review article compiles all the information about this important enzyme Serrapeptase.

Methods

A literature search against various databases and search engines like PubMed, SpringerLink, Scopus etc. was performed.

Results

We gathered and highlight all the published information regarding the molecular aspects, properties, sources, production, purification, detection, optimizing yield, immobilization, clinical studies, pharmacology, interaction studies, formulation, dosage and safety of the enzyme Serrapeptase.

Conclusion

Serrapeptase is used in many clinical studies against various diseases for its anti-inflammatory, fibrinolytic and analgesic effects. There is insufficient data regarding the safety of the enzyme as a health supplement. Data about the antiatherosclerotic activity, safety, tolerability, efficacy and mechanism of action of the Serrapeptase are still required.

     

<Emphasis Type="Italic">massPix</Emphasis>: an R package for annotation and interpretation of mass spectrometry imaging data for lipidomics

Introduction

Mass spectrometry imaging (MSI) experiments result in complex multi-dimensional datasets, which require specialist data analysis tools.

Objectives

We have developed massPix—an R package for analysing and interpreting data from MSI of lipids in tissue.

Methods

massPix produces single ion images, performs multivariate statistics and provides putative lipid annotations based on accurate mass matching against generated lipid libraries.

Results

Classification of tissue regions with high spectral similarly can be carried out by principal components analysis (PCA) or k-means clustering.

Conclusion

massPix is an open-source tool for the analysis and statistical interpretation of MSI data, and is particularly useful for lipidomics applications.

     

KniMet: a pipeline for the processing of chromatography–mass spectrometry metabolomics data

Introduction

Data processing is one of the biggest problems in metabolomics, given the high number of samples analyzed and the need of multiple software packages for each step of the processing workflow.

Objectives

Merge in the same platform the steps required for metabolomics data processing.

Methods

KniMet is a workflow for the processing of mass spectrometry-metabolomics data based on the KNIME Analytics platform.

Results

The approach includes key steps to follow in metabolomics data processing: feature filtering, missing value imputation, normalization, batch correction and annotation.

Conclusion

KniMet provides the user with a local, modular and customizable workflow for the processing of both GC–MS and LC–MS open profiling data.