Coronavirus Disease 2019 mRNA Vaccination Appears Safe in Pediatric Patients With Hypersensitivity to Polyethylene Glycolated Escherichia coli L-asparaginase

Polyethylene glycol-asparaginase (PEGAsp) is an established component of acute leukemia therapy. Hypersensitivity reactions to PEGAsp occur in 10% to 15% of patients, with polyethylene glycol suggested as the antigenic culprit. As coronavirus disease 2019 (COVID-19) mRNA vaccines contain polyethylene glycol, the safety of administration of these vaccines to patients with prior PEGAsp hypersensitivity has been questioned. Between December 21, 2020 and March 3, 2022, 66 patients with acute leukemia and PEGAsp allergy received COVID-19 vaccination. No patients (0/66 0%, 95% CI: 0%-5.4%) experienced an allergic reaction to the vaccine. COVID-19 mRNA vaccination appears to be safe in pediatric and young adult patients with acute lymphoblastic leukemia with PEGAsp allergy.

Opportunities to improve diagnosis in emergency transfers to the pediatric intensive care unit

BACKGROUND

Late recognition of in-hospital deterioration is a source of preventable harm. Emergency transfers (ET), when hospitalized patients require intensive care unit (ICU) interventions within 1 h of ICU transfer, are a proximal measure of late recognition associated with increased mortality and length of stay (LOS).

OBJECTIVE

To apply diagnostic process improvement frameworks to identify missed opportunities for improvement in diagnosis (MOID) in ETs and evaluate their association with outcomes.

DESIGN, SETTINGS, AND PARTICIPANTS

A single-center retrospective cohort study of ETs, January 2015 to June 2019. ET criteria include intubation, vasopressor initiation, or ≥ $\ge \phantom{\rule{}{0ex}}$ 60 mL/kg fluid resuscitation 1 h before to 1 h after ICU transfer. The primary exposure was the presence of MOID, determined using SaferDx. Cases were screened by an ICU and non-ICU physician. Final determinations were made by an interdisciplinary group. Diagnostic process improvement opportunities were identified.

MAIN OUTCOME AND MEASURES

Primary outcomes were in-hospital mortality and posttransfer LOS, analyzed by multivariable regression adjusting for age, service, deterioration category, and pretransfer LOS.

RESULTS

MOID was identified in 37 of 129 ETs (29%, 95% confidence interval [CI] 21%-37%). Cases with MOID differed in originating service, but not demographically. Recognizing the urgency of an identified condition was the most common diagnostic process opportunity. ET cases with MOID had higher odds of mortality (odds ratio 5.5; 95% CI 1.5-20.6; p = .01) and longer posttransfer LOS (rate ratio 1.7; 95% CI 1.1-2.6; p = .02).

CONCLUSION

MOID are common in ETs and are associated with increased mortality risk and posttransfer LOS. Diagnostic improvement strategies should be leveraged to support earlier recognition of clinical deterioration.

Automation of Hematopoietic Cell Transplantation Outcomes Reporting Leads to Dramatic Reduction in Errors Reported to Real-World Data Registry

Institutions that perform hematopoietic cell transplantation (HCT) are required by law to report standardized, structured data on transplantation outcomes. A key post-transplantation outcome is engraftment, the time between HCT infusion and reemergence of circulating neutrophils and platelets. At our center, we found that manual chart abstraction for engraftment data was highly error-prone. We developed a custom R/Shiny application that automatically calculates engraftment dates and displays them in an intuitive format to augment the manual chart review. Our hypothesis was that use of the application to assist with calculating and reporting engraftment dates would be associated with a decreased error rate. The study was conducted at a single tertiary care institution. The application was developed in a collaborative, multidisciplinary fashion by members of an embedded cellular therapy informatics team. Retrospective validation of the application's accuracy was conducted on all malignant HCTs from February 2016 to December 2020 (n = 198). Real-world use of the application was evaluated prospectively from April 2021 through April 2022 (n = 53). The Welch 2-sample t test was used to compare error rates preimplementation and postimplementation. Data were visualized using p charts, and standard special cause variation rules were applied. The accuracy of reported data postdeployment increased dramatically; the engraftment error rate decreased from 15% to 3.8% for neutrophils (P = .003) and from 28% to 1.9% for platelets (P < .001). This study demonstrates the effective deployment of a custom R/Shiny application that was associated with significantly reduced error rates in HCT engraftment reporting for operational, research, and regulatory purposes. Users reported subjective satisfaction with the application and that it addressed difficulties with the legacy manual process. Identifying and correcting erroneous data in engraftment reporting could lead to a more efficient and accurate nationwide assessment of transplantation success. Furthermore, we show that it is possible and practical for academic medical centers to create and support embedded informatics teams that can quickly build applications for clinical operations in a manner compliant with regulatory requirements.

Data standards in pediatric oncology: Past, present, and future

In this commentary, we highlight the central role that data standards play in facilitating data-driven efforts to advance research in pediatric oncology. We discuss the current state of data standards for pediatric oncology and propose five steps to achieve an improved future state with benefits for clinicians, researchers, and patients.

Implementation science in pediatric oncology: A narrative review and future directions

Implementation science (IS) has garnered attention within oncology, and most prior IS work has focused on adult, not pediatric, oncology. This narrative review broadly characterizes IS for pediatric oncology. It includes studies through 2020 using the following search terms in PubMed, Ovid Medline, and Cochrane: "implementation science," "pediatric," "childhood," "cancer," and "oncology." Systematic review was not performed due to the limited number of heterogeneous studies. Of 216 articles initially reviewed, nine were selected as specific to IS and pediatric oncology. All nine examined oncologic supportive care, cancer prevention, or cancer control. The supportive care focus is potentially due to the presence of cooperative study groups such as the Children's Oncology Group, which efficiently drive cancer-directed therapy changes through clinical trials. Future IS within pediatric oncology should embrace this ecosystem and focus on cancer control interventions that benefit patients across multiple cancer types and patients treated outside cooperative group studies.

Development of an Informatics Algorithm to Link Seasonal Infectious Diseases to Birth-Dependent Diseases Across Species: A Case Study with Osteosarcoma

Many diseases have been linked with birth seasonality, and these fall into four main categories: mental, cardiovascular, respiratory and women's reproductive health conditions. Informatics methods are needed to uncover seasonally varying infectious diseases that may be responsible for the increased birth month-dependent disease risk observed. We have developed a method to link seasonal infectious disease data from the USA to birth month dependent disease data from humans and canines. We also include seasonal air pollution and climate data to determine the seasonal factors most likely involved in the response. We test our method with osteosarcoma, a rare bone cancer. We found the Lyme disease incidence was the most strongly correlated significant factor in explaining the birth month-osteosarcoma disease pattern (R=0.418, p=2.80X10-23), and this was true across all populations observed: canines, pediatric, and adult populations.

Big Data for Nutrition Research in Pediatric Oncology: Current State and Framework for Advancement

Recognition and treatment of malnutrition in pediatric oncology patients is crucial because it is associated with increased morbidity and mortality. Nutrition-relevant data collected from cancer clinical trials and nutrition-specific studies are insufficient to drive high-impact nutrition research without augmentation from additional data sources. To date, clinical big data resources are underused for nutrition research in pediatric oncology. Health-care big data can be broadly subclassified into three clinical data categories: administrative, electronic health record (including clinical data research networks and learning health systems), and mobile health. Along with -omics data, each has unique applications and limitations. We summarize the potential use of clinical big data to drive pediatric oncology nutrition research and identify key scientific gaps. A framework for advancement of big data utilization for pediatric oncology nutrition research is presented and focuses on transdisciplinary teams, data interoperability, validated cohort curation, data repurposing, and mobile health applications.

The Effects of Social, Personal, and Behavioral Risk Factors and PM2.5 on Cardio-Metabolic Disparities in a Cohort of Community Health Center Patients

(1) Background: Cardio-metabolic diseases (CMD), including cardiovascular disease, stroke, and diabetes, have numerous common individual and environmental risk factors. Yet, few studies to date have considered how these multiple risk factors together affect CMD disparities between Blacks and Whites. (2) Methods: We linked daily fine particulate matter (PM2.5) measures with survey responses of participants in the Southern Community Cohort Study (SCCS). Generalized linear mixed modeling (GLMM) was used to estimate the relationship between CMD risk and social-demographic characteristics, behavioral and personal risk factors, and exposure levels of PM2.5. (3) Results: The study resulted in four key findings: (1) PM2.5 concentration level was significantly associated with reported CMD, with risk rising by 2.6% for each µg/m3 increase in PM2.5; (2) race did not predict CMD risk when clinical, lifestyle, and environmental risk factors were accounted for; (3) a significant variation of CMD risk was found among participants across states; and (4) multiple personal, clinical, and social-demographic and environmental risk factors played a role in predicting CMD occurrence. (4) Conclusions: Disparities in CMD risk among low social status populations reflect the complex interactions of exposures and cumulative risks for CMD contributed by different personal and environmental factors from natural, built, and social environments.

Evaluation of an automated pediatric malnutrition screen using anthropometric measurements in the electronic health record: a quality improvement initiative

PURPOSE

Malnutrition related to undernutrition in pediatric oncology patients is associated with worse outcomes including increased morbidity and mortality. At a tertiary pediatric center, traditional malnutrition screening practices were ineffective at identifying cancer patients at risk for undernutrition and needing nutrition consultation.

METHODS

To efficiently identify undernourished patients, an automated malnutrition screen using anthropometric data in the electronic health record (EHR) was implemented. The screen utilized pediatric malnutrition (undernutrition) indicators from the 2014 Consensus Statement of the Academy of Nutrition and Dietetics/American Society for Parenteral and Enteral Nutrition with corresponding structured EHR elements. The time periods before (January 2016-August 2017) and after (September 2017-August 2018) screen implementation were compared. Process metrics including nutrition consults, timeliness of nutrition assessments, and malnutrition diagnoses documentation were assessed using statistical process control charts. Outcome metrics including change in nutritional status at least 3 months after positive malnutrition screen were assessed with the Cochran-Armitage trend test.

RESULTS

After automated malnutrition screen implementation, all process metrics demonstrated center line shifts indicating special cause variation. For patient admissions with a positive screen for malnutrition of any severity level, no significant improvement in status of malnutrition was observed after 3 months (P = .13). Sub-analysis of patient admissions with screen-identified severe malnutrition noted improvement in degree of malnutrition after 3 months (P = .02).

CONCLUSIONS

Select 2014 Consensus Statement indicators for pediatric malnutrition can be implemented as an automated screen using structured EHR data. The automated screen efficiently identifies oncology patients at risk of malnutrition and may improve clinical outcomes.

Biclique: an R package for maximal biclique enumeration in bipartite graphs

OBJECTIVE

Bipartite graphs are widely used to model relationships between pairs of heterogeneous data types. Maximal bicliques are foundational structures in such graphs, and their enumeration is an important task in systems biology, epidemiology and many other problem domains. Thus, there is a need for an efficient, general purpose, publicly available tool to enumerate maximal bicliques in bipartite graphs. The statistical programming language R is a logical choice for such a tool, but until now no R package has existed for this purpose. Our objective is to provide such a package, so that the research community can more easily perform this computationally demanding task.

RESULTS

Biclique is an R package that takes as input a bipartite graph and produces a listing of all maximal bicliques in this graph. Input and output formats are straightforward, with examples provided both in this paper and in the package documentation. Biclique employs a state-of-the-art algorithm previously developed for basic research in functional genomics. This package, along with its source code and reference manual, are freely available from the CRAN public repository at https://cran.r-project.org/web/packages/biclique/index.html.

Systemic mastocytosis and probable mast cell leukaemia in a koala (Phascolarctos cinereus)

BACKGROUND

To the authors' knowledge, this is the first report of mast cell neoplasia in a koala (Phascolarctos cinereus).

CASE REPORT

An adult female koala was presented for rapidly deteriorating health and death of a pouch young. Significant weight loss was apparent despite supplemental feeding; the abdomen was distended; and the koala was weak and mentally depressed. Haematology revealed a significant mastocytosis with a concurrent population of atypical mononuclear cells. The koala was euthanised and tissues were collected for histology. Bone marrow, lymph node, lung, stomach and spleen exhibited significant infiltration by mast cells. Atypical round cells consistent with those identified in the peripheral blood were also identified in the marrow. A diagnosis of systemic mastocytosis and probable mast cell leukaemia was made. Immunocytochemical and immunohistochemical staining was not able to further characterise the atypical cell population, and the mast cells exhibited only weak staining with CD117.

CONCLUSION

The histological diagnosis, in this case, was systemic mastocytosis and myeloproliferative disease of uncertain origin. There was a dominant population of mast cells in the peripheral blood and marrow, and a population of circulating atypical mononuclear cells, appearing similar to mast cell leukaemia-acute myeloid leukaemia in humans.

A robustness metric for biological data clustering algorithms

BACKGROUND

Cluster analysis is a core task in modern data-centric computation. Algorithmic choice is driven by factors such as data size and heterogeneity, the similarity measures employed, and the type of clusters sought. Familiarity and mere preference often play a significant role as well. Comparisons between clustering algorithms tend to focus on cluster quality. Such comparisons are complicated by the fact that algorithms often have multiple settings that can affect the clusters produced. Such a setting may represent, for example, a preset variable, a parameter of interest, or various sorts of initial assignments. A question of interest then is this: to what degree do the clusters produced vary as setting values change?

RESULTS

This work introduces a new metric, termed simply "robustness", designed to answer that question. Robustness is an easily-interpretable measure of the propensity of a clustering algorithm to maintain output coherence over a range of settings. The robustness of eleven popular clustering algorithms is evaluated over some two dozen publicly available mRNA expression microarray datasets. Given their straightforwardness and predictability, hierarchical methods generally exhibited the highest robustness on most datasets. Of the more complex strategies, the paraclique algorithm yielded consistently higher robustness than other algorithms tested, approaching and even surpassing hierarchical methods on several datasets. Other techniques exhibited mixed robustness, with no clear distinction between them.

CONCLUSIONS

Robustness provides a simple and intuitive measure of the stability and predictability of a clustering algorithm. It can be a useful tool to aid both in algorithm selection and in deciding how much effort to devote to parameter tuning.

Development and evaluation of a computable phenotype to identify pediatric patients with leukemia and lymphoma treated with chemotherapy using electronic health record data

BACKGROUND

Widespread implementation of electronic health records (EHR) has created new opportunities for pediatric oncology observational research. Little attention has been given to using EHR data to identify patients with pediatric hematologic malignancies.

METHODS

This study used EHR-derived data in a pediatric clinical data research network, PEDSnet, to develop and evaluate a computable phenotype algorithm to identify pediatric patients with leukemia and lymphoma who received treatment with chemotherapy. To guide early development, multiple computable phenotype-defined cohorts were compared to one institution's tumor registry. The most promising algorithm was chosen for formal evaluation and consisted of at least two leukemia/lymphoma diagnoses (Systematized Nomenclature of Medicine codes) within a 90-day period, two chemotherapy exposures, and three hematology-oncology provider encounters. During evaluation, the computable phenotype was executed against EHR data from 2011 to 2016 at three large institutions. Classification accuracy was assessed by masked medical record review with phenotype-identified patients compared to a control group with at least three hematology-oncology encounters.

RESULTS

The computable phenotype had sensitivity of 100% (confidence interval [CI] 99%, 100%), specificity of 99% (CI 99%, 100%), positive predictive value (PPV) and negative predictive value (NPV) of 100%, and C-statistic of 1 at the development institution. The computable phenotype performance was similar at the two test institutions with sensitivity of 100% (CI 99%, 100%), specificity of 99% (CI 99%, 100%), PPV of 96%, NPV of 100%, and C-statistic of 0.99.

CONCLUSION

The EHR-based computable phenotype is an accurate cohort identification tool for pediatric patients with leukemia and lymphoma who have been treated with chemotherapy and is ready for use in clinical studies.

Health-related Google searches performed by parents of pediatric oncology patients

BACKGROUND

Little is known about the specific information parents of children with cancer search for online. Understanding the content of parents' searches over time could offer insight into what matters most to parents and identify knowledge gaps that could inform more comprehensive approaches to family education and support.

METHODS

We describe parents' health-related Google searches starting six months before cancer diagnosis and extending through the date of study enrollment, which was at least one month after initiating cancer treatment. Searches were obtained retrospectively and grouped into health-related and non-health-related categories. The median time to parent enrollment from date of cancer diagnosis was 264 days.

RESULTS

Parents searched for health-related topics more frequently than the general population (13% vs 5%). Health-related searches increased in the months preceding the child's cancer diagnosis and most commonly pertained to symptoms and logistics, "directions to hospital." Health-related search volume peaked about a month after cancer diagnosis when general health-related searches were present in addition to cancer-specific searches. Eighteen percent of health-related searches were cancer specific, and of these cancer-specific searches, 54% pertained to support, for example "cancer quote for son."

CONCLUSIONS

Google search content offers insight into what matters to parents of cancer patients. Understanding search content could inform more comprehensive approaches to family education and support initiatives.

On Finding and Enumerating Maximal and Maximum k-Partite Cliques in k-Partite Graphs

Let k denote an integer greater than 2, let G denote a k-partite graph, and let S denote the set of all maximal k-partite cliques in G. Several open questions concerning the computation of S are resolved. A straightforward and highly-scalable modification to the classic recursive backtracking approach of Bron and Kerbosch is first described and shown to run in O(3 ^n/3) time. A series of novel graph constructions is then used to prove that this bound is best possible in the sense that it matches an asymptotically tight upper limit on |S|. The task of identifying a vertex-maximum element of S is also considered and, in contrast with the k = 2 case, shown to be NP-hard for every k ≥ 3. A special class of k-partite graphs that arises in the context of functional genomics and other problem domains is studied as well and shown to be more readily solvable via a polynomial-time transformation to bipartite graphs. Applications, limitations, potentials for faster methods, heuristic approaches, and alternate formulations are also addressed.

Baseline morphometric, haematological and plasma biochemical parameters in free-ranging eastern water dragons (Intellagama lesueurii lesueurii)

OBJECTIVE

The aim of this study was to determine baseline reference data for morphometric measurements and haematological and plasma biochemical parameters in clinically healthy eastern water dragons (Intellagama lesueurii lesueurii), accounting for the variables of season and sex. The clinical objective was to provide clinicians and researchers with baseline reference intervals (RIs) in order to assess accurately the health of a population or individual animals.

METHODS

The study group comprised 39 free-ranging eastern water dragons. To monitor seasonal changes in data two study periods were assigned: September-October 2009 (spring) and January-March 2010 (summer). The 18 males and 21 females were captured in the grounds of Taronga Zoo, Mosman (33°50'45'' S, 151°15'20'' E) in Sydney, New South Wales, Australia. Basic morphometric, haematological and plasma biochemical parameters were measured. RIs were generated in accordance with the American Society of Veterinary Clinical Pathology guidelines. Data were analysed for the effects of sex, season and sex-season interactions using restricted maximal likelihood modelling.

RESULTS

Reference values were established in eastern water dragons for morphometric data and RIs were generated for haematological and plasma biochemical levels. The study showed seasonal variation in uric acid. Weight, total length, snout-vent length and basophil % showed significant differences according to sex. Sex-season interactions were detected for calcium, monocyte % and total protein (refractometry).

CONCLUSION

Multiple parameters exhibited normal physiological variation between season and sex and an interaction between the two factors. These variables need to be considered in the clinical context when interpreting results of haematological and biochemical analyses in eastern water dragons. The RIs established from this population will provide a basis for further studies in both free-living and captive eastern water dragons, and for comparison in clinical cases.

Relationship Between State-Level Google Online Search Volume and Cancer Incidence in the United States: Retrospective Study

Background

In the United States, cancer is common, with high morbidity and mortality; cancer incidence varies between states. Online searches reflect public awareness, which could be driven by the underlying regional cancer epidemiology.

Objective

The objective of our study was to characterize the relationship between cancer incidence and online Google search volumes in the United States for 6 common cancers. A secondary objective was to evaluate the association of search activity with cancer-related public events and celebrity news coverage.

Methods

We performed a population-based, retrospective study of state-level cancer incidence from 2004 through 2013 reported by the Centers for Disease Control and Prevention for breast, prostate, colon, lung, and uterine cancers and leukemia compared to Google Trends (GT) relative search volume (RSV), a metric designed by Google to allow interest in search topics to be compared between regions. Participants included persons in the United States who searched for cancer terms on Google. The primary measures were the correlation between annual state-level cancer incidence and RSV as determined by Spearman correlation and linear regression with RSV and year as independent variables and cancer incidence as the dependent variable. Temporal associations between search activity and events raising public awareness such as cancer awareness months and cancer-related celebrity news were described.

Results

At the state level, RSV was significantly correlated to incidence for breast (r=.18, P=.001), prostate (r=–.27, P<.001), lung (r=.33, P<.001), and uterine cancers (r=.39, P<.001) and leukemia (r=.13, P=.003) but not colon cancer (r=–.02, P=.66). After adjusting for time, state-level RSV was positively correlated to cancer incidence for all cancers: breast (P<.001, 95% CI 0.06 to 0.19), prostate (P=.38, 95% CI –0.08 to 0.22), lung (P<.001, 95% CI 0.33 to 0.46), colon (P<.001, 95% CI 0.11 to 0.17), and uterine cancers (P<.001, 95% CI 0.07 to 0.12) and leukemia (P<.001, 95% CI 0.01 to 0.03). Temporal associations in GT were noted with breast cancer awareness month but not with other cancer awareness months and celebrity events.

Conclusions

Cancer incidence is correlated with online search volume at the state level. Search patterns were temporally associated with cancer awareness months and celebrity announcements. Online searches reflect public awareness. Advancing understanding of online search patterns could augment traditional epidemiologic surveillance, provide opportunities for targeted patient engagement, and allow public information campaigns to be evaluated in ways previously unable to be measured.

Robust Inference of Genetic Exchange Communities from Microbial Genomes Using TF-IDF

Bacteria and archaea can exchange genetic material across lineages through processes of lateral genetic transfer (LGT). Collectively, these exchange relationships can be modeled as a network and analyzed using concepts from graph theory. In particular, densely connected regions within an LGT network have been defined as genetic exchange communities (GECs). However, it has been problematic to construct networks in which edges solely represent LGT. Here we apply term frequency-inverse document frequency (TF-IDF), an alignment-free method originating from document analysis, to infer regions of lateral origin in bacterial genomes. We examine four empirical datasets of different size (number of genomes) and phyletic breadth, varying a key parameter (word length k) within bounds established in previous work. We map the inferred lateral regions to genes in recipient genomes, and construct networks in which the nodes are groups of genomes, and the edges natively represent LGT. We then extract maximum and maximal cliques (i.e., GECs) from these graphs, and identify nodes that belong to GECs across a wide range of k. Most surviving lateral transfer has happened within these GECs. Using Gene Ontology enrichment tests we demonstrate that biological processes associated with metabolism, regulation and transport are often over-represented among the genes affected by LGT within these communities. These enrichments are largely robust to change of k.

A novel approach to analyzing lung cancer mortality disparities: Using the exposome and a graph-theoretical toolchain

OBJECTIVES

The aim is to identify exposures associated with lung cancer mortality and mortality disparities by race and gender using an exposome database coupled to a graph theoretical toolchain.

METHODS

Graph theoretical algorithms were employed to extract paracliques from correlation graphs using associations between 2162 environmental exposures and lung cancer mortality rates in 2067 counties, with clique doubling applied to compute an absolute threshold of significance. Factor analysis and multiple linear regressions then were used to analyze differences in exposures associated with lung cancer mortality and mortality disparities by race and gender.

RESULTS

While cigarette consumption was highly correlated with rates of lung cancer mortality for both white men and women, previously unidentified novel exposures were more closely associated with lung cancer mortality and mortality disparities for blacks, particularly black women.

CONCLUSIONS

Exposures beyond smoking moderate lung cancer mortality and mortality disparities by race and gender.

POLICY IMPLICATIONS

An exposome approach and database coupled with scalable combinatorial analytics provides a powerful new approach for analyzing relationships between multiple environmental exposures, pathways and health outcomes. An assessment of multiple exposures is needed to appropriately translate research findings into environmental public health practice and policy.

Performance Study Involving a Force-Reflecting Joystick for Spastic Individuals Performing two Types of Tracking Tasks

10 upper-extremity spastic subjects and 10 normal subjects were studied with a force-reflecting joystick in the performance of a continuous time-tracking task as well as an acquisition task termed, “Fitts’ Law.” Certain force-reflection paradigms, in a spatial sense, allowed the spastic subjects to obtain performance proficiency near levels of the normal subjects as measured by a capacity metric.

Proceedings of the 15th Annual UT-KBRIN Bioinformatics Summit 2016

I1 Proceedings of the Fifteenth Annual UT- KBRIN Bioinformatics Summit 2016

Eric C. Rouchka, Julia H. Chariker, Benjamin J. Harrison, Juw Won Park

P1 CC-PROMISE: Projection onto the Most Interesting Statistical Evidence (PROMISE) with Canonical Correlation to integrate gene expression and methylation data with multiple pharmacologic and clinical endpoints

Xueyuan Cao, Stanley Pounds, Susana Raimondi, James Downing, Raul Ribeiro, Jeffery Rubnitz, Jatinder Lamba

P2 Integration of microRNA-mRNA interaction networks with gene expression data to increase experimental power

Bernie J Daigle, Jr.

P3 Designing and writing software for in silico subtractive hybridization of large eukaryotic genomes

Deborah Burgess, Stephanie Gehrlich, John C Carmen

P4 Tracking the molecular evolution of Pax gene

Nicholas Johnson; Chandrakanth Emani

P5 Identifying genetic differences in thermally dimorphic and state specific fungi using in silico genomic comparison

Stephanie Gehrlich, Deborah Burgess, John C Carmen

P6 Identification of conserved genomic regions and variation therein amongst Cetartiodactyla species using next generation sequencing

Kalpani De Silva, Michael P Heaton, Theodore S Kalbfleisch

P7 Mining physiological data to identify patients with similar medical events and phenotypes

Teeradache Viangteeravat, Rahul Mudunuri, Oluwaseun Ajayi, Fatih Şen, Eunice Y Huang

P8 Smart brief for home health monitoring

Mohammad Mohebbi, Luaire Florian, Douglas J Jackson, John F Naber

P9 Side-effect term matching for computational adverse drug reaction predictions

AKM Sabbir, Sally R Ellingson

P10 Enrichment vs robustness: A comparison of transcriptomic data clustering metrics

Yuping Lu, Charles A Phillips, Michael A Langston

P11 Deep neural networks for transcriptome-based cancer classification

Rahul K Sevakula, Raghuveer Thirukovalluru, Nishchal K. Verma, Yan Cui

P12 Motif discovery using K-means clustering

Mohammed Sayed, Juw Won Park

P13 Large scale discovery of active enhancers from nascent RNA sequencing

Jing Wang, Qi Liu, Yu Shyr

P14 Computationally characterizing genomic pipelines and benchmarking results using GATK best practices on the high performance computing cluster at the University of Kentucky

Xiaofei Zhang, Sally R Ellingson

P15 Development of approaches enabling the identification of abnormal gene expression from RNA-Seq in personalized oncology

Naresh Prodduturi, Gavin R Oliver, Diane Grill, Jie Na, Jeanette Eckel-Passow, Eric W Klee

P16 Processing RNA-Seq data of plants infected with coffee ringspot virus

Michael M Goodin, Mark Farman, Harrison Inocencio, Chanyong Jang, Jerzy W Jaromczyk, Neil Moore, Kelly Sovacool

P17 Comparative transcriptomics of three Acinetobacter baumanii clinical isolates with different antibiotic resistance patterns

Leon Dent, Mike Izban, Sammed Mandape, Shruti Sakhare, Siddharth Pratap, Dana Marshall

P18 Metagenomic assessment of possible microbial contamination in the equine reference genome assembly

M Scotty DePriest, James N MacLeod, Theodore S Kalbfleisch

P19 Molecular evolution of cancer driver genes

Chandrakanth Emani, Hanady Adam, Ethan Blandford, Joel Campbell, Joshua Castlen, Brittany Dixon, Ginger Gilbert, Aaron Hall, Philip Kreisle, Jessica Lasher, Bethany Oakes, Allison Speer, Maximilian Valentine

P20 Biorepository Laboratory Information Management System

Naga Satya V Rao Nagisetty, Rony Jose, Teeradache Viangteeravat, Robert Rooney, David Hains

EntropyExplorer: an R package for computing and comparing differential Shannon entropy, differential coefficient of variation and differential expression

Background

Differential Shannon entropy (DSE) and differential coefficient of variation (DCV) are effective metrics for the study of gene expression data. They can serve to augment differential expression (DE), and be applied in numerous settings whenever one seeks to measure differences in variability rather than mere differences in magnitude. A general purpose, easily accessible tool for DSE and DCV would help make these two metrics available to data scientists. Automated p value computations would additionally be useful, and are often easier to interpret than raw test statistic values alone.

Results

EntropyExplorer is an R package for calculating DSE, DCV and DE. It also computes corresponding p values for each metric. All features are available through a single R function call. Based on extensive investigations in the literature, the Fligner-Killeen test was chosen to compute DCV p values. No standard method was found to be appropriate for DSE, and so permutation testing is used to calculate DSE p values.

Conclusions

EntropyExplorer provides a convenient resource for calculating DSE, DCV, DE and associated p values. The package, along with its source code and reference manual, are freely available from the CRAN public repository at http://cran.r-project.org/web/packages/EntropyExplorer/index.html.

Electronic supplementary material

The online version of this article (doi:10.1186/s13104-015-1786-4) contains supplementary material, which is available to authorized users.

GeneWeaver: finding consilience in heterogeneous cross-species functional genomics data

A persistent challenge lies in the interpretation of consensus and discord from functional genomics experimentation. Harmonizing and analyzing this data will enable investigators to discover relations of many genes to many diseases, and from many phenotypes and experimental paradigms to many diseases through their genomic substrates. The GeneWeaver.org system provides a platform for cross-species integration and interrogation of heterogeneous curated and experimentally derived functional genomics data. GeneWeaver enables researchers to store, share, analyze, and compare results of their own genome-wide functional genomics experiments in an environment containing rich companion data obtained from major curated repositories, including the Mouse Genome Database and other model organism databases, along with derived data from highly specialized resources, publications, and user submissions. The data, largely consisting of gene sets and putative biological networks, are mapped onto one another through gene identifiers and homology across species. A versatile suite of interactive tools enables investigators to perform a variety of set analysis operations to find consilience among these often noisy experimental results. Fast algorithms enable real-time analysis of large queries. Specific applications include prioritizing candidate genes for quantitative trait loci, identifying biologically valid mouse models and phenotypic assays for human disease, finding the common biological substrates of related diseases, classifying experiments and the biological concepts they represent from empirical data, and applying patterns of genomic evidence to implicate novel genes in disease. These results illustrate an alternative to strict emphasis on replicability, whereby researchers classify experimental results to identify the conditions that lead to their similarity.

Efficient prediction of human protein-protein interactions at a global scale

BACKGROUND

Our knowledge of global protein-protein interaction (PPI) networks in complex organisms such as humans is hindered by technical limitations of current methods.

RESULTS

On the basis of short co-occurring polypeptide regions, we developed a tool called MP-PIPE capable of predicting a global human PPI network within 3 months. With a recall of 23% at a precision of 82.1%, we predicted 172,132 putative PPIs. We demonstrate the usefulness of these predictions through a range of experiments.

CONCLUSIONS

The speed and accuracy associated with MP-PIPE can make this a potential tool to study individual human PPI networks (from genomic sequences alone) for personalized medicine.

Differential Shannon entropy and differential coefficient of variation: alternatives and augmentations to differential expression in the search for disease-related genes

Differential expression has been a standard tool for analysing case-control transcriptomic data since the advent of microarray technology. It has proved invaluable in characterising the molecular mechanisms of disease. Nevertheless, the expression profile of a gene across samples can be perturbed in ways that leave the expression level unaltered, while a biological effect is nonetheless present. This paper describes and analyses differential Shannon entropy and differential coefficient of variation, two alternate techniques for identifying genes of interest. Ontological analysis across 16 human disease datasets demonstrates that these alternatives are effective at identifying disease-related genes not found by mere differential expression alone. Because the two alternate techniques are based on somewhat different mathematical formulations, they tend to produce somewhat different gene lists. Moreover, each may pinpoint genes completely overlooked by the other. Thus, measures of entropy and variation can be used to replace or better yet augment standard differential expression computations.

On finding bicliques in bipartite graphs: a novel algorithm and its application to the integration of diverse biological data types

BACKGROUND

Integrating and analyzing heterogeneous genome-scale data is a huge algorithmic challenge for modern systems biology. Bipartite graphs can be useful for representing relationships across pairs of disparate data types, with the interpretation of these relationships accomplished through an enumeration of maximal bicliques. Most previously-known techniques are generally ill-suited to this foundational task, because they are relatively inefficient and without effective scaling. In this paper, a powerful new algorithm is described that produces all maximal bicliques in a bipartite graph. Unlike most previous approaches, the new method neither places undue restrictions on its input nor inflates the problem size. Efficiency is achieved through an innovative exploitation of bipartite graph structure, and through computational reductions that rapidly eliminate non-maximal candidates from the search space. An iterative selection of vertices for consideration based on non-decreasing common neighborhood sizes boosts efficiency and leads to more balanced recursion trees.

RESULTS

The new technique is implemented and compared to previously published approaches from graph theory and data mining. Formal time and space bounds are derived. Experiments are performed on both random graphs and graphs constructed from functional genomics data. It is shown that the new method substantially outperforms the best previous alternatives.

CONCLUSIONS

The new method is streamlined, efficient, and particularly well-suited to the study of huge and diverse biological data. A robust implementation has been incorporated into GeneWeaver, an online tool for integrating and analyzing functional genomics experiments, available at http://geneweaver.org. The enormous increase in scalability it provides empowers users to study complex and previously unassailable gene-set associations between genes and their biological functions in a hierarchical fashion and on a genome-wide scale. This practical computational resource is adaptable to almost any applications environment in which bipartite graphs can be used to model relationships between pairs of heterogeneous entities.

The influence of interview on decision making and selection of prospective veterinary undergraduate students

Interviews form part of the selection process in many veterinary programmes worldwide. This study aimed to evaluate the influence of the interview on veterinary selection decisions. An admissions cycle of interviews of 181 shortlisted school-leaving applicants to veterinary medicine was investigated. Selection panel pairs (from cohorts of 5 chairs and 11 cointerviewers) independently evaluated applicants before interview on the basis of their written application. Applicants were then interviewed and selectors repeated their evaluations, both independently and after joint discussion. Results of the preinterview and postinterview evaluations of the applicants were analysed statistically. There was slight to moderate agreement between the chair and cointerviewer on selection decisions/rankings taken before interview, but substantial agreement after interview. Agreement between postinterview decisions/rankings and consensus decisions/rankings postinterview was extremely high. Applicant attributes that had the most influence on selection decisions were: 'communication skills', 'overall knowledge gained from work experience' and 'ability to think on their feet'. There was significantly more agreement possible between interviewers with regard to selection decisions based on interview assessment compared with preinterview assessment of written applications. This study suggests that interviews may be a useful and important aid in decision making for selecting the candidates perceived as most suitable for this veterinary programme.

Psychosocial health in survivors of adult versus childhood cancer

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Background: The growing population of cancer survivors is characterized by two subgroups, those diagnosed as children and those diagnosed as adults. While psychosocial challenges are recognized in these survivors, little data exists comparing the psychosocial health between these two subgroups. Our objective is to compare adult and childhood cancer survivors for symptoms of depression, anxiety, somatic complaints, and post traumatic stress disorder (PTSD). Methods: At entry to the Vanderbilt Cancer Survivorship program, survivors of childhood and adult onset cancer, ages 15 – 55 years old, completed the Achenbach System of Empirically Based Assessment and the revised Impact of Event Scale. Using normalized T scores, independent sample t tests,and χ2 analyses, responses were compared to normative data for each group and responses of the two survivor groups were compared to one another. Results: There were 172 survivors of childhood cancer (M= 9 years old at diagnosis) and 125 survivors of adult cancer (M= 41 years old at diagnosis). Childhood cancer diagnoses included hematologic cancer (56%), sarcoma (17%), brain tumor (8%), and other (19%). Adult cancer diagnoses included breast cancer (45%), colon cancer (8%), hematologic cancer (14%), sarcoma (4%), and other (29%). After adjusting for age-related population norms, adult survivors were significantly more likely to have symptoms of depression (p=.011), anxiety (p=.029), somatic problems (p=.043), and PTSD (p=.004) compared to their childhood counterparts. Compared to age-expected norms, significantly more adult, but not child, survivors scored in the clinical range for depression (29%, p<.001; vs. 18% n.s) and somatic problems (23%, p=.013; vs. 12%; n.s.). Time elapsed from cancer diagnosis to completion of the questionnaires was not significantly correlated with any of the measures. Conclusions: Survivors of adult onset cancer face a significantly higher amount of psychologic distress, particularly depressive and somatic symptoms, compared to their childhood counterparts and age-expected norms. Analyses are ongoing to evaluate other demographic, disease, and treatment related risk factors that may contribute to this age-related phenomenon in order to develop interventions.

The maximum clique enumeration problem: algorithms, applications, and implementations

BACKGROUND

The maximum clique enumeration (MCE) problem asks that we identify all maximum cliques in a finite, simple graph. MCE is closely related to two other well-known and widely-studied problems: the maximum clique optimization problem, which asks us to determine the size of a largest clique, and the maximal clique enumeration problem, which asks that we compile a listing of all maximal cliques. Naturally, these three problems are NP-hard, given that they subsume the classic version of the NP-complete clique decision problem. MCE can be solved in principle with standard enumeration methods due to Bron, Kerbosch, Kose and others. Unfortunately, these techniques are ill-suited to graphs encountered in our applications. We must solve MCE on instances deeply seeded in data mining and computational biology, where high-throughput data capture often creates graphs of extreme size and density. MCE can also be solved in principle using more modern algorithms based in part on vertex cover and the theory of fixed-parameter tractability (FPT). While FPT is an improvement, these algorithms too can fail to scale sufficiently well as the sizes and densities of our datasets grow.

RESULTS

An extensive testbed of benchmark graphs are created using publicly available transcriptomic datasets from the Gene Expression Omnibus (GEO). Empirical testing reveals crucial but latent features of such high-throughput biological data. In turn, it is shown that these features distinguish real data from random data intended to reproduce salient topological features. In particular, with real data there tends to be an unusually high degree of maximum clique overlap. Armed with this knowledge, novel decomposition strategies are tuned to the data and coupled with the best FPT MCE implementations.

CONCLUSIONS

Several algorithmic improvements to MCE are made which progressively decrease the run time on graphs in the testbed. Frequently the final runtime improvement is several orders of magnitude. As a result, instances which were once prohibitively time-consuming to solve are brought into the domain of realistic feasibility.

Genetic dissection of acute ethanol responsive gene networks in prefrontal cortex: functional and mechanistic implications

Background

Individual differences in initial sensitivity to ethanol are strongly related to the heritable risk of alcoholism in humans. To elucidate key molecular networks that modulate ethanol sensitivity we performed the first systems genetics analysis of ethanol-responsive gene expression in brain regions of the mesocorticolimbic reward circuit (prefrontal cortex, nucleus accumbens, and ventral midbrain) across a highly diverse family of 27 isogenic mouse strains (BXD panel) before and after treatment with ethanol.

Results

Acute ethanol altered the expression of ∼2,750 genes in one or more regions and 400 transcripts were jointly modulated in all three. Ethanol-responsive gene networks were extracted with a powerful graph theoretical method that efficiently summarized ethanol's effects. These networks correlated with acute behavioral responses to ethanol and other drugs of abuse. As predicted, networks were heavily populated by genes controlling synaptic transmission and neuroplasticity.

Several of the most densely interconnected network hubs, including Kcnma1 and Gsk3β, are known to influence behavioral or physiological responses to ethanol, validating our overall approach. Other major hub genes like Grm3, Pten and Nrg3 represent novel targets of ethanol effects. Networks were under strong genetic control by variants that we mapped to a small number of chromosomal loci. Using a novel combination of genetic, bioinformatic and network-based approaches, we identified high priority cis-regulatory candidate genes, including Scn1b, Gria1, Sncb and Nell2.

Conclusions

The ethanol-responsive gene networks identified here represent a previously uncharacterized intermediate phenotype between DNA variation and ethanol sensitivity in mice. Networks involved in synaptic transmission were strongly regulated by ethanol and could contribute to behavioral plasticity seen with chronic ethanol. Our novel finding that hub genes and a small number of loci exert major influence over the ethanol response of gene networks could have important implications for future studies regarding the mechanisms and treatment of alcohol use disorders.

Haptic devices as a paradigm to enhance learning/function--theory and empirical studies

Haptics is a modern term employed to describe both afferent and efferent flows of forces with humans. The appropriate design of these force flow fields when coordinated with information to and from computers and other devices to physically challenged individuals can empower the performance of certain tasks previously not attainable. Extensive empirical work presented herein has demonstrated that proper augmentation of particular dynamic force fields, synchronised with certain cognitive tasks, can lead to improved functionality. More specifically, studies in spasticity, weaken responses and having delayed human response can benefit from these exposures to force fields applied suitably. From a theoretical perspective, the use of the well-known Fitts' law provides a unifying framework from which one can quantify the benefits derived from the proper exposure to force fields. Another area where haptics offers an exciting new platform to transfer information to and from humans includes subliminal haptics. This provides a paradigm to improve situational awareness and enhances control capability, where it may be missing previously. This article updates how haptics can be used in a variety of empirical settings to enhance learning, modulate human response to assist task completion, present a theoretical framework and improve the self-esteem of physically challenged individuals.

Truncating and missense BMPR2 mutations differentially affect the severity of heritable pulmonary arterial hypertension

BACKGROUND

Autosomal dominant inheritance of germline mutations in the bone morphogenetic protein receptor type 2 (BMPR2) gene are a major risk factor for pulmonary arterial hypertension (PAH). While previous studies demonstrated a difference in severity between BMPR2 mutation carriers and noncarriers, it is likely disease severity is not equal among BMPR2 mutations. We hypothesized that patients with missense BMPR2 mutations have more severe disease than those with truncating mutations.

METHODS

Testing for BMPR2 mutations was performed in 169 patients with PAH (125 with a family history of PAH and 44 with sporadic disease). Of the 106 patients with a detectable BMPR2 mutation, lymphocytes were available in 96 to functionally assess the nonsense-mediated decay pathway of RNA surveillance. Phenotypic characteristics were compared between BMPR2 mutation carriers and noncarriers, as well as between those carriers with a missense versus truncating mutation.

RESULTS

While there was a statistically significant difference in age at diagnosis between carriers and noncarriers, subgroup analysis revealed this to be the case only for females. Among carriers, there was no difference in age at diagnosis, death, or survival according to exonic location of the BMPR2 mutation. However, patients with missense mutations had statistically significant younger ages at diagnosis and death, as well as shorter survival from diagnosis to death or lung transplantation than those with truncating mutations. Consistent with this data, the majority of missense mutations were penetrant prior to age 36 years, while the majority of truncating mutations were penetrant after age 36 years.

CONCLUSION

In this cohort, BMPR2 mutation carriers have more severe PAH disease than noncarriers, but this is only the case for females. Among carriers, patients with missense mutations that escape nonsense-mediated decay have more severe disease than those with truncating mutations. These findings suggest that treatment and prevention strategies directed specifically at BMPR2 pathway defects may need to vary according to the type of mutation.

The survival of three strains of Arcobacter butzleri in the presence of lemon, orange and bergamot essential oils and their components in vitro and on food

AIMS

To test the effect of oils and vapours of lemon, sweet orange and bergamot and their components against three Arcobacter butzleri strains.

METHODS AND RESULTS

The disc diffusion method was used to screen the oils and vapours against three strains of A. butzleri. In vitro bergamot was the most inhibitory essential oil (EO) and both citral and linalool were effective. On cabbage leaf, the water isolate was the least susceptible to bergamot EO, citral and linalool (1-2 log reduction), with the chicken isolate being the most susceptible (6-8 log reduction). However, the latter appeared not to be susceptible to vapours over 24 h although type strain and water isolate populations reduced by 8 logs. On chicken skin, the effectiveness of the oils was reduced compared with that on cabbage leaf.

CONCLUSIONS

Bergamot was the most effective of the oils tested and linalool the most effective component. All strains tested were less susceptible in food systems than in vitro.

SIGNIFICANCE AND IMPACT OF THE STUDY

Arcobacter isolates vary in their response to EO suggesting that the results of type strain studies should be interpreted with caution. Bergamot EO has the potential for the inhibition of this 'emerging' pathogen.

Generation of MUC1-stimulated mononuclear cells using optimized conditions

Mucin is a glycoprotein found on the surface of cell membranes of adenocarcinomas. The purpose of these studies was to generate MUC1 multiple tandem repeat (VNTR)-stimulated mononuclear cells (M1SMC). We first determined the optimal conditions to influence the immune response. In these studies, peripheral blood mononuclear cells (PBMC), from patients with adenocarcinomas, were stimulated by different numbers of M1SMC stimulations, various concentrations of MUC1 peptide, washing of PBMC prior to stimulation and days in culture, to determine the optimal conditions to influence the immune response. The results of this study indicate that the mononuclear cells (MC) stimulated twice 1 week apart with MUC1 VNTR1 produced a greater specific killing of the breast cancer cell line MCF-7 than the 0, 1, 3 or 4 weekly stimulations. The optimal molarity for inducing cytotoxicity and cytokines (granulocyte macrophage colony-stimulating factor, gamma-interferon and interleukin-10) was 45 x 10(-8) M (1 microg/ml); except for tumour necrosis factor (TNF)-alpha which was 22 x 10(-8) M (0.5 microg/ml). The unwashed MC were superior to washing them with Ficoll-Hypaque. The optimal number of days in culture for cytotoxicity and cytokine production was after two stimulations (i.e. after day 7). Optimum conditions for generation of M1SMC identified in these studies were two stimulations with peptide, concentration of 45 x 10(-8) M (1 microg/ml) peptide, unwashed cells, and after two stimulations or after 8 days in culture. M1SMC were generated from multiple patients with breast cancer which lysed adenocarcinoma cells.

Context of MUC1 epitope: immunogenicity

MUC1 is a glycoprotein found at the secretory poles of normal cells but is hypoglycosylated on the entire surface of cell membranes of adenocarcinomas. In order to determine the influence on the immune response of peptide context for epitope presentation, peripheral blood mononuclear cells (PBMC) from patients with adenocarcinomas, were stimulated with MUC1 peptides derived from the 20 amino acids (aa) long sequence that is characteristic of the MUC1 Variable Number of Tandem Repeats (VNTR). In the seven peptides tested, the T-cell tumor-specific epitope (cTSE) was surrounded by variable numbers of aa and repeated up to 5 times in the same peptide. The results of this study indicate that cultures stimulated with peptide 610 (GSTAPPAHGVTS APDTRPAP) showed the highest specific killing of the MUC1-expressing breast cancer MCF-7 cells. Peptide 610 is also superior to the other peptides in inducing better production of the type 1 cytokines, tissue necrosis factor alpha and interferon gamma. In conclusion, context of the epitope and not sequence alone determines immunogenicity.

The effect of lemon, orange and bergamot essential oils and their components on the survival of Campylobacter jejuni, Escherichia coli O157, Listeria monocytogenes, Bacillus cereus and Staphylococcus aureus in vitro and in food systems

AIMS

To investigate the effectiveness of oils and vapours of lemon (Citrus limon), sweet orange (Citrus sinensis) and bergamot (Citrus bergamia) and their components against a number of common foodborne pathogens.

METHODS AND RESULTS

The disc diffusion method was used to screen the oils and vapours against Listeria monocytogenes, Staphylococcus aureus, Bacillus cereus, Escherichia coli O157 and Campylobacter jejuni. The survival of each species, demonstrated to be susceptible in the in vitro studies, was tested on cabbage leaf for 60 s by direct contact and on chicken skin for 10 min by direct contact and 24 h by vapour. The results indicate that bergamot was the most inhibitory essential oil (EO) and citral and linalool mimicked its effect (P > 0.001). Citral and linalool vapours produced 6 log reductions in L. monocytogenes, Staph. aureus and B. cereus populations on cabbage leaf after 8-10 h exposure but bergamot vapour exposure, while producing a similar reduction in L. monocytogenes and B. cereus populations, had no effect on Staph. aureus.

CONCLUSIONS

Bergamot was the most effective of the oils tested and linalool the most effective anti-bacterial component. Gram-positive bacteria were more susceptible than Gram-negative bacteria in vitro, although Camp. jejuni and E. coli O157 were inhibited by bergamot and linalool oils and by linalool vapour. All bacteria tested were less susceptible in food systems than in vitro. Of the Gram-positive bacteria tested Staph. aureus was the least susceptible to both the oils and the components tested.

SIGNIFICANCE AND IMPACT OF THE STUDY

Results suggest the possibility that citrus EOs, particularly bergamot, could be used as a way of combating the growth of common causes of food poisoning.

Communication education in veterinary in the United Kingdom and Ireland: the NUVACS project coupled to progressive individual school endeavors

This article reports on the coordination of communications skills training in veterinary schools in the United Kingdom and Ireland and describes the progress and status of training that is occurring in six of these schools.

Biomimetic model of skeletal muscle isometric contraction: I. an energetic-viscoelastic model for the skeletal muscle isometric force twitch

This paper describes a revision of the Hill-type muscle model so that it will describe the chemo-mechanical energy conversion process (energetic) and the internal-element stiffness variation (viscoelastic) during a skeletal muscle isometric force twitch contraction. The derivation of this energetic-viscoelastic model is described by a first-order linear ordinary differential equation with constant energetic and viscoelastic coefficients. The model has been implemented as part of a biomimetic model, which describes the excitation-contraction coupling necessary to drive the energetic-viscoelastic model. Finally, the energetic-viscoelastic model is validated by comparing its isometric force-time profile with that of various muscles reported in the literature.

Biomimetic model of skeletal muscle isometric contraction: II. A phenomenological model of the skeletal muscle excitation–contraction coupling process

This paper describes a new macroscopic, phenomenological model of the skeletal muscle excitation-contraction coupling process, as represented by four principal and consecutive compartments (biophysical, biochemical, and biomechanical phases) characteristic of isometric excitation-contraction coupling in mammalian skeletal muscle, and coupled by a system of simultaneous, first-order linear ordinary differential equations. The model is based upon biological compartmental transport kinetics and irreversible thermodynamic energy transformation, and represents a distinct improvement over other biomimetic models. The model was derived using physiological parameter data published in the literature, and validated using MATLAB R12.

Modeling the dynamic characteristics of pneumatic muscle

A pneumatic muscle (PM) system was studied to determine whether a three-element model could describe its dynamics. As far as the authors are aware, this model has not been used to describe the dynamics of PM. A new phenomenological model consists of a contractile (force-generating) element, spring element, and damping element in parallel. The PM system was investigated using an apparatus that allowed precise and accurate actuation pressure (P) control by a linear servo-valve. Length change of the PM was measured by a linear potentiometer. Spring and damping element functions of P were determined by a static perturbation method at several constant P values. These results indicate that at constant P, PM behaves as a spring and damper in parallel. The contractile element function of P was determined by the response to a step input in P, using values of spring and damping elements from the perturbation study. The study showed that the resulting coefficient functions of the three-element model describe the dynamic response to the step input of P accurately, indicating that the static perturbation results can be applied to the dynamic case. This model is further validated by accurately predicting the contraction response to a triangular P waveform. All three elements have pressure-dependent coefficients for pressure P in the range 207 < or = P < or = 621 kPa (30 < or = P < or = 90 psi). Studies with a step decrease in P (relaxation of the PM) indicate that the damping element coefficient is smaller during relaxation than contraction.

Role for c-jun N-terminal kinase in treatment-refractory acute myeloid leukemia (AML): signaling to multidrug-efflux and hyperproliferation

A relationship was proved between constitutive activity of leukemic cell c-jun-N-terminal kinase (JNK) and treatment failure in AML. Specifically, early treatment failure was predicted by the presence of constitutive JNK activity. The mechanistic origins of this association was sought. A multidrug resistant leukemic cell line, HL-60/ADR, characterized by hyperexpression of c-jun and JNK activity, was transfected with a mutant c-jun vector, whose substrate N-terminal c-jun serines were mutated. Down-regulated expression occurred of c-jun/AP-1-dependent genes, catalase and glutathione-S-transferase (GST) pi, which participate in cellular homeostasis to oxidative stress and xenobiotic exposure. MRP-efflux was abrogated in HL-60/ADR cells with dominant-negative c-jun, perhaps because MRP1 protein expression was also lost. Heightened sensitivity to daunorubicin resulted in cells subjected to this change. Biochemical analysis in 67 primary adult AML samples established a statistical correlation between cellular expression of c-jun and JNK activity, JNK activity with hyperleukocytosis at presentation of disease, and with exuberant MRP efflux. These findings reflect the survival role for c-jun/AP-1 and its regulatory kinase previously demonstrated for yeast in homeostatic response to oxidative stress and in operation of ATP-binding cassette efflux pumps, and may support evolutionary conservation of such function. Thus, JNK and c-jun may be salient drug targets in multidrug resistant AML.