SWIFT-Review: a text-mining workbench for systematic review.

Syst Rev. 2016;5(1):87

Authors: Howard BE, Phillips J, Miller K, Tandon A, Mav D, Shah MR, Holmgren S, Pelch KE, Walker V, Rooney AA, Macleod M, Shah RR, Thayer K

Abstract
BACKGROUND: There is growing interest in using machine learning approaches to priority rank studies and reduce human burden in screening literature when conducting systematic reviews. In addition, identifying addressable questions during the problem formulation phase of systematic review can be challenging, especially for topics having a large literature base. Here, we assess the performance of the SWIFT-Review priority ranking algorithm for identifying studies relevant to a given research question. We also explore the use of SWIFT-Review during problem formulation to identify, categorize, and visualize research areas that are data rich/data poor within a large literature corpus.
METHODS: Twenty case studies, including 15 public data sets, representing a range of complexity and size, were used to assess the priority ranking performance of SWIFT-Review. For each study, seed sets of manually annotated included and excluded titles and abstracts were used for machine training. The remaining references were then ranked for relevance using an algorithm that considers term frequency and latent Dirichlet allocation (LDA) topic modeling. This ranking was evaluated with respect to (1) the number of studies screened in order to identify 95 % of known relevant studies and (2) the "Work Saved over Sampling" (WSS) performance metric. To assess SWIFT-Review for use in problem formulation, PubMed literature search results for 171 chemicals implicated as EDCs were uploaded into SWIFT-Review (264,588 studies) and categorized based on evidence stream and health outcome. Patterns of search results were surveyed and visualized using a variety of interactive graphics.
RESULTS: Compared with the reported performance of other tools using the same datasets, the SWIFT-Review ranking procedure obtained the highest scores on 11 out of 15 of the public datasets. Overall, these results suggest that using machine learning to triage documents for screening has the potential to save, on average, more than 50 % of the screening effort ordinarily required when using un-ordered document lists. In addition, the tagging and annotation capabilities of SWIFT-Review can be useful during the activities of scoping and problem formulation.
CONCLUSIONS: Text-mining and machine learning software such as SWIFT-Review can be valuable tools to reduce the human screening burden and assist in problem formulation.

PMID: 27216467 [PubMed - in process]

miRiaD: A Text Mining Tool for Detecting Associations of microRNAs with Diseases.

J Biomed Semantics. 2016;7(1):9

Authors: Gupta S, Ross KE, Tudor CO, Wu CH, Schmidt CJ, Vijay-Shanker K

Abstract
BACKGROUND: MicroRNAs are increasingly being appreciated as critical players in human diseases, and questions concerning the role of microRNAs arise in many areas of biomedical research. There are several manually curated databases of microRNA-disease associations gathered from the biomedical literature; however, it is difficult for curators of these databases to keep up with the explosion of publications in the microRNA-disease field. Moreover, automated literature mining tools that assist manual curation of microRNA-disease associations currently capture only one microRNA property (expression) in the context of one disease (cancer). Thus, there is a clear need to develop more sophisticated automated literature mining tools that capture a variety of microRNA properties and relations in the context of multiple diseases to provide researchers with fast access to the most recent published information and to streamline and accelerate manual curation.
METHODS: We have developed miRiaD (microRNAs in association with Disease), a text-mining tool that automatically extracts associations between microRNAs and diseases from the literature. These associations are often not directly linked, and the intermediate relations are often highly informative for the biomedical researcher. Thus, miRiaD extracts the miR-disease pairs together with an explanation for their association. We also developed a procedure that assigns scores to sentences, marking their informativeness, based on the microRNA-disease relation observed within the sentence.
RESULTS: miRiaD was applied to the entire Medline corpus, identifying 8301 PMIDs with miR-disease associations. These abstracts and the miR-disease associations are available for browsing at http://biotm.cis.udel.edu/miRiaD . We evaluated the recall and precision of miRiaD with respect to information of high interest to public microRNA-disease database curators (expression and target gene associations), obtaining a recall of 88.46-90.78. When we expanded the evaluation to include sentences with a wide range of microRNA-disease information that may be of interest to biomedical researchers, miRiaD also performed very well with a F-score of 89.4. The informativeness ranking of sentences was evaluated in terms of nDCG (0.977) and correlation metrics (0.678-0.727) when compared to an annotator's ranked list.
CONCLUSIONS: miRiaD, a high performance system that can capture a wide variety of microRNA-disease related information, extends beyond the scope of existing microRNA-disease resources. It can be incorporated into manual curation pipelines and serve as a resource for biomedical researchers interested in the role of microRNAs in disease. In our ongoing work we are developing an improved miRiaD web interface that will facilitate complex queries about microRNA-disease relationships, such as "In what diseases does microRNA regulation of apoptosis play a role?" or "Is there overlap in the sets of genes targeted by microRNAs in different types of dementia?"."

PMID: 27216254 [PubMed - in process]

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Effective Management of Advanced Angiosarcoma by the Synergistic Combination of Propranolol and Vinblastine-based Metronomic Chemotherapy: A Bench to Bedside Study.

EBioMedicine. 2016 Apr;6:87-95

Authors: Pasquier E, André N, Street J, Chougule A, Rekhi B, Ghosh J, Philip DS, Meurer M, MacKenzie KL, Kavallaris M, Banavali SD

Abstract
BACKGROUND: Angiosarcomas are rare malignant tumors of vascular origin that represent a genuine therapeutic challenge. Recently, the combination of metronomic chemotherapy and drug repositioning has been proposed as an attractive alternative for cancer patients living in developing countries.
METHODS: In vitro experiments with transformed endothelial cells were used to identify synergistic interactions between anti-hypertensive drug propranolol and chemotherapeutics. This led to the design of a pilot treatment protocol combining oral propranolol and metronomic chemotherapy. Seven consecutive patients with advanced/metastatic/recurrent angiosarcoma were treated with this combination for up to 12months, followed by propranolol-containing maintenance therapy.
FINDINGS: Gene expression analysis showed expression of ADRB1 and ADRB2 adrenergic receptor genes in transformed endothelial cells and in angiosarcoma tumors. Propranolol strongly synergized with the microtubule-targeting agent vinblastine in vitro, but only displayed additivity or slight antagonism with paclitaxel and doxorubicin. A combination treatment using bi-daily propranolol (40mg) and weekly metronomic vinblastine (6mg/m(2)) and methotrexate (35mg/m(2)) was designed and used in 7 patients with advanced angiosarcoma. Treatment was well tolerated and resulted in 100% response rate, including 1 complete response and 3 very good partial responses, based on RECIST criteria. Median progression-free and overall survival was 11months (range 5-24) and 16months (range 10-30), respectively.
INTERPRETATION: Our results provide a strong rationale for the combination of β-blockers and vinblastine-based metronomic chemotherapy for the treatment of advanced angiosarcoma. Furthermore, our study highlights the potential of drug repositioning in combination with metronomic chemotherapy in low- and middle-income country setting.
FUNDING: This study was funded by institutional and philanthropic grants.

PMID: 27211551 [PubMed - in process]

Drug repositioning in sarcomas and other rare tumors.

EBioMedicine. 2016 Apr;6:4-5

Authors: Lee AT, Huang PH, Pollack SM, Jones RL

PMID: 27211532 [PubMed - in process]

Implementing Pharmacogenomics at Your Institution: Establishment and Overcoming Implementation Challenges.

Clin Transl Sci. 2016 May 23;

Authors: Arwood MJ, Chumnumwat S, Cavallari LH, Nutescu EA, Duarte JD

Abstract
With advancements in pharmacogenomics research and genotyping technology, implementation of pharmacogenomics into clinical practice is now feasible. The aim of this publication is to serve as a tutorial for institutions interested in developing pharmacogenomics services. Topics covered include resources needed, clinical decision support establishment, choosing a genotyping platform, and challenges faced with pharmacogenomics service implementation. This tutorial provides practical advice, drawing upon experience of two established clinical pharmacogenomics services. This article is protected by copyright. All rights reserved.

PMID: 27214750 [PubMed - as supplied by publisher]

Exome Sequencing of Extreme Clopidogrel Response Phenotypes Identifies B4GALT2 as a Determinant of On-treatment Platelet Reactivity.

Clin Pharmacol Ther. 2016 May 23;

Authors: Scott SA, Collet JP, Baber U, Yang Y, Peter I, Linderman M, Sload J, Qiao W, Kini AS, Sharma SK, Desnick RJ, Fuster V, Hajjar RJ, Montalescot G, Hulot JS

Abstract
Interindividual variability in platelet aggregation is common among patients treated with clopidogrel, and both high and low (LTPR) on-treatment platelet reactivity increase risks for adverse clinical outcomes. CYP2C19 influences clopidogrel response but only accounts for ∼12% of the variability in platelet reactivity. To identify novel variants implicated in on-treatment platelet reactivity, coronary artery disease (CAD) patients with extreme pharmacodynamic responses to clopidogrel and wild-type CYP2C19 were subjected to exome sequencing. Candidate variants that clustered in the LTPR subgroup subsequently were genotyped across the discovery cohort (n=636). Importantly, carriers of B4GALT2 c.909C>T had lower on-treatment P2Y12 reaction units (PRU; p=0.0077) and residual platelet aggregation (p=0.0008) compared to non-carriers, which remained significant after adjusting for CYP2C19 and other clinical variables in both the discovery (p=0.0298) and replication (n=160; PRU: p=0.0001) cohorts. B4GALT2 is a platelet-expressed galactosyltransferase, indicating that B4GALT2 c.909C>T may influence clopidogrel sensitivity through atypical cell-surface glycoprotein processing and platelet adhesion. This article is protected by copyright. All rights reserved.

PMID: 27213804 [PubMed - as supplied by publisher]

Systems biology in kidney transplantation: The application of multi-omics to a complex model.

Am J Transplant. 2016 May 23;

Authors: Bontha SV, Maluf DG, Mueller TF, Mas VR

Abstract
In spite of reduction of rejection rates and improvement in short term survival post kidney transplantation, modest progress has occurred in long-term graft attrition over the years. Timely identification of molecular events that precede clinical and histopathological changes might help in early intervention and thereby increase the graft half-life. Evolution of -omics tools has enabled systemic investigation of the influence of whole genome, epigenome, transcriptome, proteome, microbiome on transplant function and survival. In this 'omics' era, systemic approaches, in-depth clinical phenotyping and use of strict validation methods is the key for further understanding of the complex mechanisms associated with graft function. Systems biology is an inter-disciplinary holistic approach that focuses on complex and dynamic interactions within biological systems. The complexity of human kidney transplant is unlikely to be captured by a reductionist approach. It appears essential to integrate multi - omics data that can elucidate the multidimensional and multilayered regulation of the underlying heterogeneous and complex kidney transplant model. Here, we discuss studies which focus on genetic biomarkers, emerging technologies and systems biology approaches, which should increase the ability to discover biomarkers, understand mechanisms and stratify patients and responses post kidney transplantation. This article is protected by copyright. All rights reserved.

PMID: 27214826 [PubMed - as supplied by publisher]

Characterization of physiological responses to 22 gene knockouts in Escherichia coli central carbon metabolism.

Metab Eng. 2016 May 19;

Authors: Long CP, Gonzalez JE, Sandoval NR, Antoniewicz MR

Abstract
Understanding the impact of gene knockouts on cellular physiology, and metabolism in particular, is centrally important to quantitative systems biology and metabolic engineering. Here, we present a comprehensive physiological characterization of wild-type Escherichia coli and 22 knockouts of enzymes in the upper part of central carbon metabolism, including the PTS system, glycolysis, pentose phosphate pathway and Entner-Doudoroff pathway. Our results reveal significant metabolic changes that are affected by specific gene knockouts. Analysis of collective trends and correlations in the data using principal component analysis (PCA) provide new, and sometimes surprising, insights into E. coli physiology. Additionally, by comparing the data-to-model predictions from constraint-based approaches such as FBA, MOMA and RELATCH we demonstrate the important role of less well-understood kinetic and regulatory effects in central carbon metabolism.

PMID: 27212692 [PubMed - as supplied by publisher]

Multi-Omics of Single Cells: Strategies and Applications.

Trends Biotechnol. 2016 May 19;

Authors: Bock C, Farlik M, Sheffield NC

Abstract
Most genome-wide assays provide averages across large numbers of cells, but recent technological advances promise to overcome this limitation. Pioneering single-cell assays are now available for genome, epigenome, transcriptome, proteome, and metabolome profiling. Here, we describe how these different dimensions can be combined into multi-omics assays that provide comprehensive profiles of the same cell.

PMID: 27212022 [PubMed - as supplied by publisher]

Characterizing Strain Variation in Engineered E. coli Using a Multi-Omics-Based Workflow.

Cell Syst. 2016 May 19;

Authors: Brunk E, George KW, Alonso-Gutierrez J, Thompson M, Baidoo E, Wang G, Petzold CJ, McCloskey D, Monk J, Yang L, O'Brien EJ, Batth TS, Martin HG, Feist A, Adams PD, Keasling JD, Palsson BO, Lee TS

Abstract
Understanding the complex interactions that occur between heterologous and native biochemical pathways represents a major challenge in metabolic engineering and synthetic biology. We present a workflow that integrates metabolomics, proteomics, and genome-scale models of Escherichia coli metabolism to study the effects of introducing a heterologous pathway into a microbial host. This workflow incorporates complementary approaches from computational systems biology, metabolic engineering, and synthetic biology; provides molecular insight into how the host organism microenvironment changes due to pathway engineering; and demonstrates how biological mechanisms underlying strain variation can be exploited as an engineering strategy to increase product yield. As a proof of concept, we present the analysis of eight engineered strains producing three biofuels: isopentenol, limonene, and bisabolene. Application of this workflow identified the roles of candidate genes, pathways, and biochemical reactions in observed experimental phenomena and facilitated the construction of a mutant strain with improved productivity. The contributed workflow is available as an open-source tool in the form of iPython notebooks.

PMID: 27211860 [PubMed - as supplied by publisher]

Systems toxicology-based assessment of the candidate modified risk tobacco product THS2.2 for the adhesion of monocytic cells to human coronary arterial endothelial cells.

Toxicology. 2016 Jan 2;339:73-86

Authors: Poussin C, Laurent A, Peitsch MC, Hoeng J, De Leon H

Abstract
Alterations of endothelial adhesive properties by cigarette smoke (CS) can progressively favor the development of atherosclerosis which may cause cardiovascular disorders. Modified risk tobacco products (MRTPs) are tobacco products developed to reduce smoking-related risks. A systems biology/toxicology approach combined with a functional in vitro adhesion assay was used to assess the impact of a candidate heat-not-burn technology-based MRTP, Tobacco Heating System (THS) 2.2, on the adhesion of monocytic cells to human coronary arterial endothelial cells (HCAECs) compared with a reference cigarette (3R4F). HCAECs were treated for 4h with conditioned media of human monocytic Mono Mac 6 (MM6) cells preincubated with low or high concentrations of aqueous extracts from THS2.2 aerosol or 3R4F smoke for 2h (indirect treatment), unconditioned media (direct treatment), or fresh aqueous aerosol/smoke extracts (fresh direct treatment). Functional and molecular investigations revealed that aqueous 3R4F smoke extract promoted the adhesion of MM6 cells to HCAECs via distinct direct and indirect concentration-dependent mechanisms. Using the same approach, we identified significantly reduced effects of aqueous THS2.2 aerosol extract on MM6 cell-HCAEC adhesion, and reduced molecular changes in endothelial and monocytic cells. Ten- and 20-fold increased concentrations of aqueous THS2.2 aerosol extract were necessary to elicit similar effects to those measured with 3R4F in both fresh direct and indirect exposure modalities, respectively. Our systems toxicology study demonstrated reduced effects of an aqueous aerosol extract from the candidate MRTP, THS2.2, using the adhesion of monocytic cells to human coronary endothelial cells as a surrogate pathophysiologically relevant event in atherogenesis.

PMID: 26655683 [PubMed - indexed for MEDLINE]

Network Modeling Reveals Cross Talk of MAP Kinases during Adaptation to Caspofungin Stress in Aspergillus fumigatus.

PLoS One. 2015;10(9):e0136932

Authors: Altwasser R, Baldin C, Weber J, Guthke R, Kniemeyer O, Brakhage AA, Linde J, Valiante V

Abstract
Mitogen activated protein kinases (MAPKs) are highly conserved in eukaryotic organisms. In pathogenic fungi, their activities were assigned to different physiological functions including drug adaptation and resistance. Aspergillus fumigatus is a human pathogenic fungus, which causes life-threatening invasive infections. Therapeutic options against invasive mycoses are still limited. One of the clinically used drugs is caspofungin, which specifically targets the fungal cell wall biosynthesis. A systems biology approach, based on comprehensive transcriptome data sets and mathematical modeling, was employed to infer a regulatory network and identify key interactions during adaptation to caspofungin stress in A. fumigatus. Mathematical modeling and experimental validations confirmed an intimate cross talk occurring between the cell wall-integrity and the high osmolarity-glycerol signaling pathways. Specifically, increased concentrations of caspofungin promoted activation of these signalings. Moreover, caspofungin affected the intracellular transport, which caused an additional osmotic stress that is independent of glucan inhibition. High concentrations of caspofungin reduced this osmotic stress, and thus decreased its toxic activity. Our results demonstrated that MAPK signaling pathways play a key role during caspofungin adaptation and are contributing to the paradoxical effect exerted by this drug.

PMID: 26356475 [PubMed - indexed for MEDLINE]

Improving Biochemical Named Entity Recognition Performance Using PSO Classifier Selection and Bayesian Combination Method.

IEEE/ACM Trans Comput Biol Bioinform. 2016 May 18;

Authors: Akkasi A, Varoglu E

Abstract
Named Entity Recognition (NER) is a basic step for large number of consequent text mining tasks in the biochemical domain. Increasing the performance of such recognition systems is of high importance and always poses a challenge. In this study, a new community based decision making system is proposed which aims at increasing the efficiency of NER systems in the chemical/drug name context. Particle Swarm Optimization (PSO) algorithm is chosen as the expert selection strategy along with the Bayesian combination method to merge the outputs of the selected classifiers as well as evaluate the fitness of the selected candidates. The proposed system performs in two steps. The first step is focuses on creating various numbers of baseline classifiers for NER with different features sets using the Conditional Random Fields (CRFs). The second step involves the selection and efficient combination of the classifiers using PSO and Bayesisan combination. Two comprehensive corpora from BioCreative events, namely ChemDNER and CEMP, are used for the experiments conducted. Results show that the ensemble of classifiers selected by means of the proposed approach perform better than the single best classifier as well as ensembles formed using other popular selection/combination strategies for both corpora. Furthermore, the proposed method outperforms the best performing system at the Biocreative IV ChemDNER track by achieving an F-score of 87.95%.

PMID: 27214909 [PubMed - as supplied by publisher]

Two Similarity Metrics for Medical Subject Headings (MeSH): An Aid to Biomedical Text Mining and Author Name Disambiguation.

J Biomed Discov Collab. 2016;7:e1

Authors: Smalheiser NR, Bonifield G

Abstract
In the present paper, we have created and characterized several similarity metrics for relating any two Medical Subject Headings (MeSH terms) to each other. The article-based metric measures the tendency of two MeSH terms to appear in the MEDLINE record of the same article. The author-based metric measures the tendency of two MeSH terms to appear in the body of articles written by the same individual (using the 2009 Author-ity author name disambiguation dataset as a gold standard). The two metrics are only modestly correlated with each other (r = 0.50), indicating that they capture different aspects of term usage. The article-based metric provides a measure of semantic relatedness, and MeSH term pairs that co-occur more often than expected by chance may reflect relations between the two terms. In contrast, the author metric is indicative of how individuals practice science, and may have value for author name disambiguation and studies of scientific discovery. We have calculated article metrics for all MeSH terms appearing in at least 25 articles in MEDLINE (as of 2014) and author metrics for MeSH terms published as of 2009. The dataset is freely available for download and can be queried at http://arrowsmith.psych.uic.edu/arrowsmith_uic/mesh_pair_metrics.html. Handling editor: Elizabeth Workman, MLIS, PhD.

PMID: 27213780 [PubMed - as supplied by publisher]

Update on the safety of second generation antipsychotics in youths: a call for collaboration among paediatricians and child psychiatrists.

Ital J Pediatr. 2016;42(1):51

Authors: Pisano S, Catone G, Veltri S, Lanzara V, Pozzi M, Clementi E, Iuliano R, Riccio MP, Radice S, Molteni M, Capuano A, Gritti A, Coppola G, Milone A, Bravaccio C, Masi G

Abstract
During the past decade, a substantial increase in the use of second generation antipsychotics (SGAs) has occurred for a number of juvenile psychiatric disorders, often as off-label prescriptions. Although they were thought to be safer than older, first generation antipsychotics, mainly due to a lower risk of neurological adverse reactions, recent studies have raised significant concerns regarding their safety regarding metabolic, endocrinological and cardiovascular side effects. Aim of this paper is to update with a narrative review, the latest findings on safety of SGAs in youths. Results suggest that different SGAs may present different safety profiles. Metabolic adverse events are the most frequent and troublesome, with increasing evidences of heightened risk for type II diabetes mellitus. Results are discussed with specific emphasis on possible strategies of an active monitoring, which could enable both paediatricians and child psychiatrists to a possible prevention, early detection, and a timely management of such effects.

PMID: 27209326 [PubMed - as supplied by publisher]

DNetDB: The human disease network database based on dysfunctional regulation mechanism.

BMC Syst Biol. 2016;10(1):36

Authors: Yang J, Wu SJ, Yang SY, Peng JW, Wang SN, Wang FY, Song YX, Qi T, Li YX, Li YY

Abstract
Disease similarity study provides new insights into disease taxonomy, pathogenesis, which plays a guiding role in diagnosis and treatment. The early studies were limited to estimate disease similarities based on clinical manifestations, disease-related genes, medical vocabulary concepts or registry data, which were inevitably biased to well-studied diseases and offered small chance of discovering novel findings in disease relationships. In other words, genome-scale expression data give us another angle to address this problem since simultaneous measurement of the expression of thousands of genes allows for the exploration of gene transcriptional regulation, which is believed to be crucial to biological functions. Although differential expression analysis based methods have the potential to explore new disease relationships, it is difficult to unravel the upstream dysregulation mechanisms of diseases. We therefore estimated disease similarities based on gene expression data by using differential coexpression analysis, a recently emerging method, which has been proved to be more potential to capture dysfunctional regulation mechanisms than differential expression analysis. A total of 1,326 disease relationships among 108 diseases were identified, and the relevant information constituted the human disease network database (DNetDB). Benefiting from the use of differential coexpression analysis, the potential common dysfunctional regulation mechanisms shared by disease pairs (i.e. disease relationships) were extracted and presented. Statistical indicators, common disease-related genes and drugs shared by disease pairs were also included in DNetDB. In total, 1,326 disease relationships among 108 diseases, 5,598 pathways, 7,357 disease-related genes and 342 disease drugs are recorded in DNetDB, among which 3,762 genes and 148 drugs are shared by at least two diseases. DNetDB is the first database focusing on disease similarity from the viewpoint of gene regulation mechanism. It provides an easy-to-use web interface to search and browse the disease relationships and thus helps to systematically investigate etiology and pathogenesis, perform drug repositioning, and design novel therapeutic interventions.Database URL: http://app.scbit.org/DNetDB/ #.

PMID: 27209279 [PubMed - as supplied by publisher]

Clinical decision-making and secondary findings in systems medicine.

BMC Med Ethics. 2016;17(1):32

Authors: Fischer T, Brothers KB, Erdmann P, Langanke M

Abstract
BACKGROUND: Systems medicine is the name for an assemblage of scientific strategies and practices that include bioinformatics approaches to human biology (especially systems biology); "big data" statistical analysis; and medical informatics tools. Whereas personalized and precision medicine involve similar analytical methods applied to genomic and medical record data, systems medicine draws on these as well as other sources of data. Given this distinction, the clinical translation of systems medicine poses a number of important ethical and epistemological challenges for researchers working to generate systems medicine knowledge and clinicians working to apply it.
DISCUSSION: This article focuses on three key challenges: First, we will discuss the conflicts in decision-making that can arise when healthcare providers committed to principles of experimental medicine or evidence-based medicine encounter individualized recommendations derived from computer algorithms. We will explore in particular whether controlled experiments, such as comparative effectiveness trials, should mediate the translation of systems medicine, or if instead individualized findings generated through "big data" approaches can be applied directly in clinical decision-making. Second, we will examine the case of the Riyadh Intensive Care Program Mortality Prediction Algorithm, pejoratively referred to as the "death computer," to demonstrate the ethical challenges that can arise when big-data-driven scoring systems are applied in clinical contexts. We argue that the uncritical use of predictive clinical algorithms, including those envisioned for systems medicine, challenge basic understandings of the doctor-patient relationship. Third, we will build on the recent discourse on secondary findings in genomics and imaging to draw attention to the important implications of secondary findings derived from the joint analysis of data from diverse sources, including data recorded by patients in an attempt to realize their "quantified self." This paper examines possible ethical challenges that are likely to be raised as systems medicine to be translated into clinical medicine. These include the epistemological challenges for clinical decision-making, the use of scoring systems optimized by big data techniques and the risk that incidental and secondary findings will significantly increase. While some ethical implications remain still hypothetical we should use the opportunity to prospectively identify challenges to avoid making foreseeable mistakes when systems medicine inevitably arrives in routine care.

PMID: 27209083 [PubMed - as supplied by publisher]

Mixed Phenotype Acute Leukemia (MPAL) Exhibits Frequent Mutations in DNMT3A and Activated Signaling Genes.

Exp Hematol. 2016 May 18;

Authors: Eckstein OS, Wang L, Punia JN, Kornblau SM, Andreeff M, Wheeler DA, Goodell MA, Rau RE

Abstract
Mixed phenotype acute leukemia (MPAL) is a heterogeneous group of poor-prognosis leukemias with immunophenotypic features of at least two cell lineages. The full spectrum of genetic mutations in this rare disease has not been elucidated, limiting our understanding of disease pathogenesis and our ability to devise targeted therapeutic strategies. We sought to define the mutational landscape of MPAL by performing whole exome sequencing on samples from 23 adult and pediatric MPAL patients. We identified frequent mutations of epigenetic modifiers, most notably mutations of DNMT3A in 33% of adult MPAL patients. Mutations of activated signaling pathways, tumor suppressors and transcription factors were also frequent. Importantly, many of the identified mutations are potentially therapeutically targetable with agents currently available or in various stages of clinical development. Therefore, the mutational spectrum we identified provides potential biological insights and is likely to have clinical relevance for patients with this poor-prognosis disease.

PMID: 27208809 [PubMed - as supplied by publisher]

Childhood epidermolysis bullosa acquisita during squaric acid dibutylester (SADBE) immunotherapy for alopecia areata.

Br J Dermatol. 2016 May 21;

Authors: Guerra L, Pacifico V, Calabresi V, De Luca N, Castiglia D, Angelo C, Zambruno G, Di Zenzo G

Abstract
Epidermolysis bullosa acquisita (EBA) is a rare acquired subepidermal blistering disease associated with autoantibodies against type VII collagen. Although EBA manifests more frequently in adults, it can occur in childhood. We describe a 6-year-old male who developed the inflammatory variant of EBA shortly after the initiation of immunotherapy with squaric acid dibutylester (SADBE) for scalp alopecia areata (AA). The disease rapidly regressed following SADBE discontinuation and starting of a combined steroid and dapsone therapy and never recurred after treatment tapering and withdrawal. The association of EBA with other autoimmune diseases is common, but EBA occurring during AA has not been previously described. The development of EBA during SADBE treatment is also noticeable: the clinical history and therapeutic response in our patient point to a possible role of SADBE in EBA onset. This article is protected by copyright. All rights reserved.

PMID: 27208509 [PubMed - as supplied by publisher]