Status and Recommendations of Technological and Data-Driven Innovations in Cancer Care: Focus Group Study.

BACKGROUND: The status of the data-driven management of cancer care as well as the challenges, opportunities, and recommendations aimed at accelerating the rate of progress in this field are topics of great interest. Two international workshops, one conducted in June 2019 in Cordoba, Spain, and one in October 2019 in Athens, Greece, were organized by four Horizon 2020 (H2020) European Union (EU)-funded projects: BOUNCE, CATCH ITN, DESIREE, and MyPal. The issues covered included patient engagement, knowledge and data-driven decision support systems, patient journey, rehabilitation, personalized diagnosis, trust, assessment of guidelines, and interoperability of information and communication technology (ICT) platforms. A series of recommendations was provided as the complex landscape of data-driven technical innovation in cancer care was portrayed. OBJECTIVE: This study aims to provide information on the current state of the art of technology and data-driven innovations for the management of cancer care through the work of four EU H2020-funded projects. METHODS: Two international workshops on ICT in the management of cancer care were held, and several topics were identified through discussion among the participants. A focus group was formulated after the second workshop, in which the status of technological and data-driven cancer management as well as the challenges, opportunities, and recommendations in this area were collected and analyzed. RESULTS: Technical and data-driven innovations provide promising tools for the management of cancer care. However, several challenges must be successfully addressed, such as patient engagement, interoperability of ICT-based systems, knowledge management, and trust. This paper analyzes these challenges, which can be opportunities for further research and practical implementation and can provide practical recommendations for future work. CONCLUSIONS: Technology and data-driven innovations are becoming an integral part of cancer care management. In this process, specific challenges need to be addressed, such as increasing trust and engaging the whole stakeholder ecosystem, to fully benefit from these innovations.

Multiplatform biomarker identification using a data-driven approach enables single-sample classification.

BACKGROUND: High-throughput gene expression profiles have allowed discovery of potential biomarkers enabling early diagnosis, prognosis and developing individualized treatment. However, it remains a challenge to identify a set of reliable and reproducible biomarkers across various gene expression platforms and laboratories for single sample diagnosis and prognosis. We address this need with our Data-Driven Reference (DDR) approach, which employs stably expressed housekeeping genes as references to eliminate platform-specific biases and non-biological variabilities. RESULTS: Our method identifies biomarkers with "built-in" features, and these features can be interpreted consistently regardless of profiling technology, which enable classification of single-sample independent of platforms. Validation with RNA-seq data of blood platelets shows that DDR achieves the superior performance in classification of six different tumor types as well as molecular target statuses (such as MET or HER2-positive, and mutant KRAS, EGFR or PIK3CA) with smaller sets of biomarkers. We demonstrate on the three microarray datasets that our method is capable of identifying robust biomarkers for subgrouping medulloblastoma samples with data perturbation due to different microarray platforms. In addition to identifying the majority of subgroup-specific biomarkers in CodeSet of nanoString, some potential new biomarkers for subgrouping medulloblastoma were detected by our method. CONCLUSIONS: In this study, we present a simple, yet powerful data-driven method which contributes significantly to identification of robust cross-platform gene signature for disease classification of single-patient to facilitate precision medicine. In addition, our method provides a new strategy for transcriptome analysis.

Cigarette Smoke Induces Stem Cell Features of Pancreatic Cancer Cells via PAF1.

BACKGROUND & AIMS: Cigarette smoking is a major risk factor for pancreatic cancer. Aggressive pancreatic tumors contain cancer cells with stem cell features. We investigated whether cigarette smoke induces stem cell features in pancreatic cancer cells. METHODS: KrasG12D; Pdx1-Cre mice were exposed to cigarette smoke or clean air (controls) for up to 20 weeks; pancreata were collected and analyzed by histology, quantitative reverse transcription polymerase chain reaction, and confocal immunofluorescence microscopy. HPNE and Capan1 cells were exposed to cigarette smoke extract (CSE), nicotine and nicotine-derived carcinogens (NNN or NNK), or clean air (controls) for 80 days and evaluated for stem cell markers and features using flow cytometry-based autofluorescence, sphere formation, and immunoblot assays. Proteins were knocked down in cells with small interfering RNAs. We performed RNA sequencing analyses of CSE-exposed cells. We used chromatin immunoprecipitation assays to confirm the binding of FOS-like 1, AP-1 transcription factor subunit (FOSL1) to RNA polymerase II-associated factor (PAF1) promoter. We obtained pancreatic ductal adenocarcinoma (PDAC) and matched nontumor tissues (n = 15) and performed immunohistochemical analyses. RESULTS: Chronic exposure of HPNE and Capan1 cells to CSE caused them to increase markers of stem cells, including autofluorescence and sphere formation, compared with control cells. These cells increased expression of ABCG2, SOX9, and PAF1, via cholinergic receptor nicotinic alpha 7 subunit (CHRNA7) signaling to mitogen-activated protein kinase 1 and FOSL1. CSE-exposed pancreatic cells with knockdown of PAF1 did not show stem cell features. Exposure of cells to NNN and NNK led to increased expression of CHRNA7, FOSL1, and PAF1 along with stem cell features. Pancreata from KrasG12D; Pdx1-Cre mice exposed to cigarette smoke had increased levels of PAF1 mRNA and protein, compared with control mice, as well as increased expression of SOX9. Levels of PAF1 and FOSL1 were increased in PDAC tissues, especially those from smokers, compared with nontumor pancreatic tissue. CSE exposure increased expression of PHD-finger protein 5A, a pluripotent transcription factor and its interaction with PAF1. CONCLUSIONS: Exposure to cigarette smoke activates stem cell features of pancreatic cells, via CHRNA7 signaling and FOSL1 activation of PAF1 expression. Levels of PAF1 are increased in pancreatic tumors of humans and mice with chronic cigarette smoke exposure.

A study of bias and increasing organismal complexity from their post-translational modifications and reaction site interplays.

Post-translational modifications (PTMs) are important steps in the biosynthesis of proteins. Aside from their integral contributions to protein development, i.e. perform specialized proteolytic cleavage of regulatory subunits, the covalent addition of functional groups of proteins or the degradation of entire proteins, PTMs are also involved in enabling proteins to withstand and recover from temporary environmental stresses (heat shock, microgravity and many others). The literature supports evidence of thousands of recently discovered PTMs, many of which may likely contribute similarly (perhaps, even, interchangeably) to protein stress response. Although there are many PTM actors on the biological stage, our study determines that these PTMs are generally cast into organism-specific, preferential roles. In this work, we study the PTM compositions across the mitochondrial (Mt) and non-Mt proteomes of 11 diverse organisms to illustrate that each organism appears to have a unique list of PTMs, and an equally unique list of PTM-associated residue reaction sites (RSs), where PTMs interact with protein. Despite the present limitation of available PTM data across different species, we apply existing and current protein data to illustrate particular organismal biases. We explore the relative frequencies of observed PTMs, the RSs and general amino-acid compositions of Mt and non-Mt proteomes. We apply these data to create networks and heatmaps to illustrate the evidence of bias. We show that the number of PTMs and RSs appears to grow along with organismal complexity, which may imply that environmental stress could play a role in this bias.

Alignment-free genetic sequence comparisons: a review of recent approaches by word analysis.

Modern sequencing and genome assembly technologies have provided a wealth of data, which will soon require an analysis by comparison for discovery. Sequence alignment, a fundamental task in bioinformatics research, may be used but with some caveats. Seminal techniques and methods from dynamic programming are proving ineffective for this work owing to their inherent computational expense when processing large amounts of sequence data. These methods are prone to giving misleading information because of genetic recombination, genetic shuffling and other inherent biological events. New approaches from information theory, frequency analysis and data compression are available and provide powerful alternatives to dynamic programming. These new methods are often preferred, as their algorithms are simpler and are not affected by synteny-related problems. In this review, we provide a detailed discussion of computational tools, which stem from alignment-free methods based on statistical analysis from word frequencies. We provide several clear examples to demonstrate applications and the interpretations over several different areas of alignment-free analysis such as base-base correlations, feature frequency profiles, compositional vectors, an improved string composition and the D2 statistic metric. Additionally, we provide detailed discussion and an example of analysis by Lempel-Ziv techniques from data compression.

An alternative database approach for management of SNOMED CT and improved patient data queries.

OBJECTIVE: SNOMED CT is the international lingua franca of terminologies for human health. Based in Description Logics (DL), the terminology enables data queries that incorporate inferences between data elements, as well as, those relationships that are explicitly stated. However, the ontologic and polyhierarchical nature of the SNOMED CT concept model make it difficult to implement in its entirety within electronic health record systems that largely employ object oriented or relational database architectures. The result is a reduction of data richness, limitations of query capability and increased systems overhead. The hypothesis of this research was that a graph database (graph DB) architecture using SNOMED CT as the basis for the data model and subsequently modeling patient data upon the semantic core of SNOMED CT could exploit the full value of the terminology to enrich and support advanced data querying capability of patient data sets. METHODS: The hypothesis was tested by instantiating a graph DB with the fully classified SNOMED CT concept model. The graph DB instance was tested for integrity by calculating the transitive closure table for the SNOMED CT hierarchy and comparing the results with transitive closure tables created using current, validated methods. The graph DB was then populated with 461,171 anonymized patient record fragments and over 2.1 million associated SNOMED CT clinical findings. Queries, including concept negation and disjunction, were then run against the graph database and an enterprise Oracle relational database (RDBMS) of the same patient data sets. The graph DB was then populated with laboratory data encoded using LOINC, as well as, medication data encoded with RxNorm and complex queries performed using LOINC, RxNorm and SNOMED CT to identify uniquely described patient populations. RESULTS: A graph database instance was successfully created for two international releases of SNOMED CT and two US SNOMED CT editions. Transitive closure tables and descriptive statistics generated using the graph database were identical to those using validated methods. Patient queries produced identical patient count results to the Oracle RDBMS with comparable times. Database queries involving defining attributes of SNOMED CT concepts were possible with the graph DB. The same queries could not be directly performed with the Oracle RDBMS representation of the patient data and required the creation and use of external terminology services. Further, queries of undefined depth were successful in identifying unknown relationships between patient cohorts. CONCLUSION: The results of this study supported the hypothesis that a patient database built upon and around the semantic model of SNOMED CT was possible. The model supported queries that leveraged all aspects of the SNOMED CT logical model to produce clinically relevant query results. Logical disjunction and negation queries were possible using the data model, as well as, queries that extended beyond the structural IS_A hierarchy of SNOMED CT to include queries that employed defining attribute-values of SNOMED CT concepts as search parameters. As medical terminologies, such as SNOMED CT, continue to expand, they will become more complex and model consistency will be more difficult to assure. Simultaneously, consumers of data will increasingly demand improvements to query functionality to accommodate additional granularity of clinical concepts without sacrificing speed. This new line of research provides an alternative approach to instantiating and querying patient data represented using advanced computable clinical terminologies.

A base composition analysis of natural patterns for the preprocessing of metagenome sequences.

BACKGROUND: On the pretext that sequence reads and contigs often exhibit the same kinds of base usage that is also observed in the sequences from which they are derived, we offer a base composition analysis tool. Our tool uses these natural patterns to determine relatedness across sequence data. We introduce spectrum sets (sets of motifs) which are permutations of bacterial restriction sites and the base composition analysis framework to measure their proportional content in sequence data. We suggest that this framework will increase the efficiency during the pre-processing stages of metagenome sequencing and assembly projects. RESULTS: Our method is able to differentiate organisms and their reads or contigs. The framework shows how to successfully determine the relatedness between these reads or contigs by comparison of base composition. In particular, we show that two types of organismal-sequence data are fundamentally different by analyzing their spectrum set motif proportions (coverage). By the application of one of the four possible spectrum sets, encompassing all known restriction sites, we provide the evidence to claim that each set has a different ability to differentiate sequence data. Furthermore, we show that the spectrum set selection having relevance to one organism, but not to the others of the data set, will greatly improve performance of sequence differentiation even if the fragment size of the read, contig or sequence is not lengthy. CONCLUSIONS: We show the proof of concept of our method by its application to ten trials of two or three freshly selected sequence fragments (reads and contigs) for each experiment across the six organisms of our set. Here we describe a novel and computationally effective pre-processing step for metagenome sequencing and assembly tasks. Furthermore, our base composition method has applications in phylogeny where it can be used to infer evolutionary distances between organisms based on the notion that related organisms often have much conserved code.

KPC-4 Is encoded within a truncated Tn4401 in an IncL/M plasmid, pNE1280, isolated from Enterobacter cloacae and Serratia marcescens.

We describe the transfer of bla(KPC-4) from Enterobacter cloacae to Serratia marcescens in a single patient. DNA sequencing revealed that KPC-4 was encoded on an IncL/M plasmid, pNE1280, closely related to pCTX-M360. Further analysis found that KPC-4 was encoded within a novel Tn4401 element (Tn4401f) containing a truncated tnpA and lacking tnpR, ISKpn7 left, and Tn4401 IRL-1, which are conserved in other Tn4401 transposons. This study highlights the continued evolution of Tn4401 transposons and movement to multiple plasmid backbones that results in acquisition by multiple species of Gram-negative bacilli.

A computational approach to demonstrate the control of gene expression via chromosomal access in colorectal cancer.

Improved technologies for chromatin accessibility sequencing such as ATAC-seq have increased our understanding of gene regulation mechanisms, particularly in disease conditions such as cancer. This study introduces a computational tool that quantifies and establishes connections between chromatin accessibility, transcription factor binding, transcription factor mutations, and gene expression using publicly available colorectal cancer data. The tool has been packaged using a workflow management system to allow biologists and researchers to reproduce the results of this study. Through the application of this pipeline, we present compelling evidence linking chromatin accessibility to gene expression, with particular emphasis on SNP mutations and the accessibility of transcription factor genes. Furthermore, we have identified significant upregulation of key transcription factor interactions in colon cancer patients, including the apoptotic regulation facilitated by E2F1, MYC, and MYCN, as well as activation of the BCL-2 protein family facilitated by TP73. The code for this project is openly available on GitHub at the following address: https://github.com/CalebPecka/ATAC-Seq-Pipeline/.

Claudin-1 interacts with EPHA2 to promote cancer stemness and chemoresistance in colorectal cancer.

Therapy resistance is the primary problem in treating late-stage colorectal cancer (CRC). Claudins are frequently dysregulated in cancer, and several are being investigated as novel therapeutic targets and biomarkers. We have previously demonstrated that Claudin-1 (CLDN1) expression in CRC promotes epithelial-mesenchymal transition, metastasis, and resistance to anoikis. Here, we hypothesize that CLDN1 promotes cancer stemness and chemoresistance in CRC. We found that high CLDN1 expression in CRC is associated with cancer stemness and chemoresistance signaling pathways in patient datasets, and it promotes chemoresistance both in vitro and in vivo. Using functional stemness assays, proteomics, biophysical binding assays, and patient-derived organoids, we found that CLDN1 promotes properties of cancer stemness including CD44 expression, tumor-initiating potential, and chemoresistance through a direct interaction with ephrin type-A receptor 2 (EPHA2) tyrosine kinase. This interaction is dependent on the CLDN1 PDZ-binding motif, increases EPHA2 protein expression by inhibiting its degradation, and enhances downstream AKT signaling and CD44 expression to promote stemness and chemoresistance. These results suggest CLDN1 is a viable target for pharmacological intervention and/or biomarker development.

Loss of claudin-3 expression increases colitis risk by promoting Gut Dysbiosis.

Dysregulation of both the gut barrier and microbiota (dysbiosis) promotes susceptibility to and severity of Inflammatory Bowel Diseases (IBD). Leaky gut and dysbiosis often coexist; however, potential interdependence and molecular regulation are not well understood. Robust expression of claudin-3 (CLDN3) characterizes the gut epithelium, and studies have demonstrated a positive association between CLDN3 expression and gut barrier maturity and integrity, including in response to probiotics. However, the exact status and causal role of CLDN3 in IBD and regulation of gut dysbiosis remain unknown. Analysis of mouse and human IBD cohorts helped examine CLDN3 expression in IBD. The causal role was determined by modeling CLDN3 loss of expression during experimental colitis. 16S sequencing and in silico analysis helped examine gut microbiota diversity between Cldn3KO and WT mice and potential host metabolic responses. Fecal microbiota transplant (FMT) studies were performed to assess the role of gut dysbiosis in the increased susceptibility of Cldn3KO mice to colitis. A significant decrease in CLDN3 expression characterized IBD and CLDN3 loss of expression promoted colitis. 16S sequencing analysis suggested gut microbiota changes in Cldn3KO mice that were capable of modulating fatty acid metabolism and oxidative stress response. FMT from naïve Cldn3KO mice promoted colitis susceptibility in recipient germ-free mice (GFM) compared with GFM-receiving microbiota from WT mice. Our data demonstrate a critical role of CLDN3 in maintaining normal gut microbiota and inflammatory responses, which can be harnessed to develop novel therapeutic opportunities for patients with IBD.

Claudin-2 protects against colitis-associated cancer by promoting colitis-associated mucosal healing.

Patients with inflammatory bowel disease (IBD) are susceptible to colitis-associated cancer (CAC). Chronic inflammation promotes the risk for CAC. In contrast, mucosal healing predicts improved prognosis in IBD and reduced risk of CAC. However, the molecular integration among colitis, mucosal healing, and CAC remains poorly understood. Claudin-2 (CLDN2) expression is upregulated in IBD; however, its role in CAC is not known. The current study was undertaken to examine the role for CLDN2 in CAC. The AOM/DSS-induced CAC model was used with WT and CLDN2-modified mice. High-throughput expression analyses, murine models of colitis/recovery, chronic colitis, ex vivo crypt culture, and pharmacological manipulations were employed in order to increase our mechanistic understanding. The Cldn2KO mice showed significant inhibition of CAC despite severe colitis compared with WT littermates. Cldn2 loss also resulted in impaired recovery from colitis and increased injury when mice were subjected to intestinal injury by other methods. Mechanistic studies demonstrated a possibly novel role of CLDN2 in promotion of mucosal healing downstream of EGFR signaling and by regulation of Survivin expression. An upregulated CLDN2 expression protected from CAC and associated positively with crypt regeneration and Survivin expression in patients with IBD. We demonstrate a potentially novel role of CLDN2 in promotion of mucosal healing in patients with IBD and thus regulation of vulnerability to colitis severity and CAC, which can be exploited for improved clinical management.

Reliable epithelial-mesenchymal transition biomarkers for colorectal cancer detection.

Aims: To combat increases in colorectal cancer (CRC) incidence and mortality, biomarkers among differentially expressed genes (DEGs) have been identified to objectively detect cancer. However, DEGs are numerous, and additional parameters may identify more reliable biomarkers. Here, CRC DEGs were filtered into a prioritized list of biomarkers. Materials & methods: Two independent datasets (COAD-READ [n = 698] and GSE50760 [n = 36]) were input alternatively to the recently published data-driven reference method. Results were filtered based on epithelial-mesenchymal transition enrichment (χ-square statistic: 919.05; p = 2.2e-16) to produce 37 potential CRC biomarkers. Results: All 37 genes reliably classified CRC samples and ETV4, CLDN1 and CA2 together were top-ranked by DDR (accuracy: 89%; F1 score: 0.89). Conclusion: Biological and statistical information were combined to produce a better set of CRC detection biomarkers.

Pulsed stable isotope labeling of amino acids in cell culture uncovers the dynamic interactions between HIV-1 and the monocyte-derived macrophage.

Dynamic interactions between human immunodeficiency virus-1 (HIV-1) and the macrophage govern the tempo of viral dissemination and replication in its human host. HIV-1 affects macrophage phenotype, and the macrophage, in turn, can modulate the viral life cycle. While these processes are linked to host-cell function and survival, the precise intracellular pathways involved are incompletely understood. To elucidate such dynamic virus-cell events, we employed pulsed stable isotope labeling of amino acids in cell culture. Alterations in de novo protein synthesis of HIV-1 infected human monocyte-derived macrophages (MDM) were examined after 3, 5, and 7 days of viral infection. Synthesis rates of cellular metabolic, regulatory, and DNA packaging activities were decreased, whereas, those affecting antigen presentation (major histocompatibility complex I and II) and interferon-induced antiviral activities were increased. Interestingly, enrichment of proteins linked to chromatin assembly or disassembly, DNA packaging, and nucleosome assembly were identified that paralleled virus-induced cytopathology and replication. We conclude that HIV-1 regulates a range of host MDM proteins that affect its survival and abilities to contain infection.

Identifying Glycemic Variability in Diabetes Patient Cohorts and Evaluating Disease Outcomes.

Glycemic variability (GV) is an obstacle to effective blood glucose control and an autonomous risk factor for diabetes complications. We, therefore, explored sample data of patients with diabetes mellitus who maintained better amplitude of glycemic fluctuations and compared their disease outcomes with groups having poor control. A retrospective study was conducted using electronic data of patients having hemoglobin A1C (HbA1c) values with five recent time points from Think Whole Person Healthcare (TWPH). The control variability grid analysis (CVGA) plot and coefficient of variability (CV) were used to identify and cluster glycemic fluctuation. We selected important variables using LASSO. Chi-Square, Fisher's exact test, Bonferroni chi-Square adjusted residual analysis, and multivariate Kruskal-Wallis tests were used to evaluate eventual disease outcomes. Patients with very high CV were strongly associated (p < 0.05) with disorders of lipoprotein (p = 0.0014), fluid, electrolyte, and acid-base balance (p = 0.0032), while those with low CV were statistically significant for factors influencing health status such as screening for other disorders (p = 0.0137), long-term (current) drug therapy (p = 0.0019), and screening for malignant neoplasms (p = 0.0072). Reducing glycemic variability may balance alterations in electrolytes and reduce differences in lipid profiles, which may assist in strategies for managing patients with diabetes mellitus.

A Claudin-Based Molecular Signature Identifies High-Risk, Chemoresistant Colorectal Cancer Patients.

Identifying molecular characteristics that are associated with aggressive cancer phenotypes through gene expression profiling can help predict treatment responses and clinical outcomes. Claudins are deregulated in colorectal cancer (CRC). In CRC, increased claudin-1 expression results in epithelial-to-mesenchymal transition and metastasis, while claudin-7 functions as a tumor suppressor. In this study, we have developed a molecular signature based on claudin-1 and claudin-7 associated with poor patient survival and chemoresistance. This signature was validated using an integrated approach including publicly available datasets and CRC samples from patients who either responded or did not respond to standard-of-care treatment, CRC cell lines, and patient-derived rectal and colon tumoroids. Transcriptomic analysis from a patient dataset initially yielded 23 genes that were differentially expressed along with higher claudin-1 and decreased claudin-7. From this analysis, we selected a claudins-associated molecular signature including PIK3CA, SLC6A6, TMEM43, and ASAP-1 based on their importance in CRC. The upregulation of these genes and their protein products was validated using multiple CRC patient datasets, in vitro chemoresistant cell lines, and patient-derived tumoroid models. Additionally, blocking these genes improved 5-FU sensitivity in chemoresistant CRC cells. Our findings propose a new claudin-based molecular signature that associates with poor prognosis as well as characteristics of treatment-resistant CRC including chemoresistance, metastasis, and relapse.

An Integrated Approach to Recognize Potential Protective Effects of Culinary Herbs Against Chronic Diseases.

Secondary metabolites in plants have been of interest to humans for their wide variety of functions, including its use as dye, drugs, or perfumes. They are increasingly recognized as potential sources of new natural drugs and antibiotics. More recently, gut-associated microbes have been found to fulfill important functions in human health. However, our knowledge about the impact of secondary metabolites from culinary herbs on gut microbiome is limited. The present study was conducted to access the availability of computational resources relating to secondary metabolites and bioactive compounds in culinary herbs. A graph-based database HerbMicrobeDataBase (HMDB) was developed using Neo4j framework. It integrates knowledge from key biological entities associated in maintaining gut health and provides efficient storage/retrieval and graphical presentation of botanical, biochemical, and pharmacological data for culinary herbs and the human microbiome. We demonstrate the utility of this resource in understanding the molecular mechanism of metabolite production as well as their therapeutic or toxicological effects on gut microbes.

MTAP: the motif tool assessment platform.

BACKGROUND: In recent years, substantial effort has been applied to de novo regulatory motif discovery. At this time, more than 150 software tools exist to detect regulatory binding sites given a set of genomic sequences. As the number of software packages increases, it becomes more important to identify the tools with the best performance characteristics for specific problem domains. Identifying the correct tool is difficult because of the great variability in motif detection software. Consequently, many labs spend considerable effort testing methods to find one that works well in their problem of interest. RESULTS: In this work, we propose a method (MTAP) that substantially reduces the effort required to assess de novo regulatory motif discovery software. MTAP differs from previous attempts at regulatory motif assessment in that it automates motif discovery tool pipelines (something that traditionally required many manual steps), automatically constructs orthologous upstream sequences, and provides automated benchmarks for many popular tools. As a proof of concept, we have run benchmarks over human, mouse, fly, yeast, E. coli and B. subtilis. CONCLUSION: MTAP presents a new approach to the challenging problem of assessing regulatory motif discovery methods. The most current version of MTAP can be downloaded from http://biobase.ist.unomaha.edu/

Utilizing Twitter data for analysis of chemotherapy.

OBJECTIVE: Twitter has become one of the most popular social media platforms that offers real-world insights to healthy behaviors. The purpose of this study was to assess and compare perceptions about chemotherapy of patients and health-care providers through analysis of chemo-related tweets. MATERIALS AND METHODS: Cancer-related Twitter accounts and their tweets were obtained through using Tweepy (Python library). Multiple text classification algorithms were tested to identify the models with best performance in classifying the accounts into individual and organization. Chemotherapy-specific tweets were extracted from historical tweetset, and the content of these tweets was analyzed using topic model, sentiment analysis and word co-occurrence network. RESULTS: Using the description in Twitter users' profiles, the accounts related with cancer were collected and coded as individual or organization. We employed Long Short Term Memory (LSTM) network with GloVe word embeddings to identify the user into individuals and organizations with accuracy of 85.2%. 13, 273 and 14,051 publicly available chemotherapy-related tweets were retrieved from individuals and organizations, respectively. The content of the chemo-related tweets was analyzed by text mining approaches. The tweets from individual accounts pertained to personal chemotherapy experience and emotions. In contrast with the personal users, professional accounts had a higher proportion of neutral tweets about side effects. The information about the assessment of response to chemotherapy was deficient from organizations on Twitter. DISCUSSION: Examining chemotherapy discussions on Twitter provide new lens into content and behavioral patterns associated with treatments for cancer patients. The methodology described herein allowed us to collect relatively large number of health-related tweets over a greater time period and exploit the potential power of social media, which provide comprehensive view on patients' perceptions of chemotherapy. CONCLUSION: This study sheds light on using Twitter data as a valuable healthcare data source for helping oncologists (organizations) in understanding patients' experiences while undergoing chemotherapy, in developing personalize therapy plans, and a supplement to the clinical electronic medical records (EMRs).

Perspectives of Nurses Toward Telehealth Efficacy and Quality of Health Care: Pilot Study.

BACKGROUND: Telehealth nursing, or the delivery, management, and coordination of nursing care services provided via telecommunications technology, is one of the methods of delivering health care to patients in the United States. It is important to assess the service quality of the involved health professionals as well as the telehealth nursing process. The focus of this study is the innovative model of telehealth care delivery by nurses for managing patients with chronic disease while they are living in their own residence. OBJECTIVE: The primary objective of this pilot study was to examine whether telehealth technology impacts the perceived level of internal service quality delivered by nurses within a telehealth organization. To address this research goal, the notion of telehealth nursing service quality (TNSQ) is empirically tested and validated with a survey instrument. METHODS: Data were collected from nurses belonging to a home care agency based on interview questions inquiring about facilitators and inhibitors to TNSQ. A survey to measure TNSQ based on the SERVQUAL instrument was completed by adjusting descriptions of the original instrument to suit the context. Follow-up interviews were conducted to validate questions on the revised instrument. RESULTS: The findings of this survey research were positive, based on mean differences between expectations and perceptions of TNSQ. This indicates satisfaction with TNSQ and shows that the quality of the service is higher than what the respondents expect. The Wilcoxon signed-rank test using the P value for the test, which is .35, did not show a statistically significant change between the median differences of perception and expectation. The total number of respondents was 13. Results indicate that overall perceived service quality is a positive value (0.05332). This means the perceptions of the level of service are slightly higher than what they expect, indicating there is satisfaction with TNSQ. CONCLUSIONS: The responses to the interview questions and data gathered from the survey showed overall satisfaction with TNSQ. The SERVQUAL instrument was a good framework to assess TNSQ. In a nutshell, the study highlighted how the telehealth process provides daily monitoring of patient health, leading to the benefits of immediate feedback for patients, family, and caregivers as well as convenience of scheduling.