[A 20-year-old woman with ulcerative colitis and acute liver failure].

Internist (Berl). 2017 Mar 07;:

Authors: Forker R, Escher M, Stange EF

Abstract
A 20-year-old woman presented with acute exacerbation of ulcerative colitis. After treatment with infliximab, she developed a fulminant liver failure. Under supportive therapy and steroid medication, recovery of symptoms and transaminases occurred. A few case reports about hepatic side effects of anti-TNF-α antibodies in patients with inflammatory bowel disease have been published. These side effects ranged from asymptomatic increase of transaminases to fulminant liver failure necessitating transplantation. The pathomechanism is not fully understood; in some case reports autoimmune phenomena have been described.

PMID: 28271269 [PubMed - as supplied by publisher]

Cytokine biomarkers to predict antitumor responses to nivolumab suggested in a phase II study for advanced melanoma.

Cancer Sci. 2017 Mar 07;:

Authors: Yamazaki N, Kiyohara Y, Uhara H, Iizuka H, Uehara J, Otsuka F, Fujisawa Y, Takenouchi T, Isei T, Iwatsuki K, Uchi H, Ihn H, Minami H, Tahara H

Abstract
Promising anti-tumor activities of nivolumab, a fully humanized IgG4 inhibitor antibody against the programmed death-1 protein, were suggested in previous phase 1 studies. The present phase 2, single-arm study (JAPIC-CTI #111681) evaluated the anti-tumor activities of nivolumab and explored its predictive correlates in advanced melanoma patients at 11 sites in Japan. Intravenous nivolumab 2 mg/kg was given repeatedly at 3-week intervals to 35 of 37 patients enrolled from December 2011 to May 2012 until they experienced unacceptable toxicity, disease progression, or complete response. The primary endpoint was objective response rate. Serum levels of immune modulators were assessed at multiple time points. As of 21 October 2014, the median response duration, median progression-free survival, and median overall survival were 463 days, 169 days, and 18.0 months, respectively. The overall response rate and 1- and 2-year survival rates were 28.6%, 54.3%, and 42.9%, respectively. Thirteen patients remained alive at the end of the observation period and no deaths were drug related. Grade 3-4 drug-related adverse events were observed in 31.4% of patients. Pre-treatment serum interferon-γ, and interleukin-6 and -10 levels were significantly higher in the patients with objective tumor responses than in those with tumor progression. In conclusion, repeated intravenous administration of nivolumab had potent and durable anti-tumor effects and a manageable safety profile in advanced melanoma patients, strongly suggesting the usefulness of nivolumab for advanced melanoma and the usefulness of pre-treatment serum cytokine profiles as correlates for predicting treatment efficacy. This article is protected by copyright. All rights reserved.

PMID: 28266140 [PubMed - as supplied by publisher]

Efficacy and safety of morinidazole in pelvic inflammatory disease: results of a multicenter, double-blind, randomized trial.

Eur J Clin Microbiol Infect Dis. 2017 Mar 06;:

Authors: Cao C, Luo A, Wu P, Weng D, Zheng H, Wang S

Abstract
This multicenter, double-blind, randomized, parallel-group, non-inferiority study compared the efficacy and safety of morinidazole with those of ornidazole in women with pelvic inflammatory disease. Women from 18 hospitals in China received a 14-day course of either intravenous morinidazole, 500 mg twice daily (n = 168), or intravenous ornidazole, 500 mg twice daily (n = 170). A total of 312 of 338 patients in the full analysis set (FAS) (92.3%) were included in the per protocol set (PPS) analyses, 61 (19.6%) of whom were included in the microbiologically valid (MBV) population. The clinical resolution rates in the PPS population at the test of cure (TOC, primary efficacy end point, 7-30 days post-therapy) visit were 96.86% (154/159) for morinidazole and 96.73% (148/153) for ornidazole (95% CI: -3.79% to 4.03%). The bacteriological success rates in the MBV population at the TOC visit were 100% (32/32) for morinidazole and 89.66% (26/29) for ornidazole (95% CI: -16.15% to 11.21%). Drug-related adverse events occurred less frequently with morinidazole (32.74%, 55/168) than with ornidazole (47.06%, 80/170) (p < 0.01). For women with pelvic inflammatory disease, twice-daily morinidazole for 14 days was clinically and bacteriologically as efficacious as twice-daily ornidazole for 14 days, while the former was associated with fewer drug-related adverse events than the latter.

PMID: 28265816 [PubMed - as supplied by publisher]

Interventional Analgesic Management of Lung Cancer Pain.

Front Oncol. 2017;7:17

Authors: Hochberg U, Elgueta MF, Perez J

Abstract
Lung cancer is one of the four most prevalent cancers worldwide. Comprehensive patient care includes not only adherence to clinical guidelines to control and when possible cure the disease but also appropriate symptom control. Pain is one of the most prevalent symptoms in patients diagnosed with lung cancer; it can arise from local invasion of chest structures or metastatic disease invading bones, nerves, or other anatomical structures potentially painful. Pain can also be a consequence of therapeutic approaches like surgery, chemotherapy, or radiotherapy. Conventional medical management of cancer pain includes prescription of opioids and coadjuvants at doses sufficient to control the symptoms without causing severe drug effects. When an adequate pharmacological medical management fails to provide satisfactory analgesia or when it causes limiting side effects, interventional cancer pain techniques may be considered. Interventional pain management is devoted to the use of invasive techniques such as joint injections, nerve blocks and/or neurolysis, neuromodulation, and cement augmentation techniques to provide diagnosis and treatment of pain syndromes resistant to conventional medical management. Advantages of interventional approaches include better analgesic outcomes without experiencing drug-related side effects and potential for opioid reduction thus avoiding central side effects. This review will describe various pain syndromes frequently described in lung cancer patients and those interventional techniques potentially indicated for those cases.

PMID: 28261561 [PubMed - in process]

A case-based discussion of clinical problems in the management of patients treated with ruxolitinib for myelofibrosis.

Intern Med J. 2017 Mar;47(3):262-268

Authors: Ho PJ, Bajel A, Burbury K, Dunlop L, Durrant S, Forsyth C, Perkins AC, Ross DM

Abstract
Ruxolitinib is a dual janus kinase 1 (JAK1)/JAK2 inhibitor used to treat splenomegaly and symptoms associated with myelofibrosis (MF). Current therapeutic options for symptomatic MF include supportive care, myelosuppressive therapy (such as hydroxycarbamide) and janus kinase (JAK) inhibitors (in particular ruxolitinib). Allogeneic stem cell transplantation remains the only potentially curative treatment for MF, and younger transplant-eligible patients should still be considered for allogeneic stem cell transplantation; however, this is applicable only to a small proportion of patients. There is now increasing and extensive experience of the efficacy and safety of ruxolitinib in MF, both in clinical trials and in 'real-world' practice. The drug has been shown to be of benefit in intermediate-1 risk patients with symptomatic splenomegaly or other MF-related symptoms, and higher risk disease. Optimal use of the drug is required to maximise clinical benefit, requiring an understanding of the balance between dose-dependent responses and dose-limiting toxicities. There is also increasing experience in the use of ruxolitinib in the pre-transplantation setting. This paper aims to utilise several 'real-life' cases to illustrate several strategies that may help to optimise clinical practice.

PMID: 28260257 [PubMed - in process]

Protein Kinase CK2: Intricate Relationships within Regulatory Cellular Networks.

Pharmaceuticals (Basel). 2017 Mar 05;10(1):

Authors: Nuñez de Villavicencio-Diaz T, Rabalski AJ, Litchfield DW

Abstract
Protein kinase CK2 is a small family of protein kinases that has been implicated in an expanding array of biological processes. While it is widely accepted that CK2 is a regulatory participant in a multitude of fundamental cellular processes, CK2 is often considered to be a constitutively active enzyme which raises questions about how it can be a regulatory participant in intricately controlled cellular processes. To resolve this apparent paradox, we have performed a systematic analysis of the published literature using text mining as well as mining of proteomic databases together with computational assembly of networks that involve CK2. These analyses reinforce the notion that CK2 is involved in a broad variety of biological processes and also reveal an extensive interplay between CK2 phosphorylation and other post-translational modifications. The interplay between CK2 and other post-translational modifications suggests that CK2 does have intricate roles in orchestrating cellular events. In this respect, phosphorylation of specific substrates by CK2 could be regulated by other post-translational modifications and CK2 could also have roles in modulating other post-translational modifications. Collectively, these observations suggest that the actions of CK2 are precisely coordinated with other constituents of regulatory cellular networks.

PMID: 28273877 [PubMed - in process]

Drug target identification using network analysis: Taking active components in Sini decoction as an example.

Sci Rep. 2016 Apr 20;6:24245

Authors: Chen S, Jiang H, Cao Y, Wang Y, Hu Z, Zhu Z, Chai Y

Abstract
Identifying the molecular targets for the beneficial effects of active small-molecule compounds simultaneously is an important and currently unmet challenge. In this study, we firstly proposed network analysis by integrating data from network pharmacology and metabolomics to identify targets of active components in sini decoction (SND) simultaneously against heart failure. To begin with, 48 potential active components in SND against heart failure were predicted by serum pharmacochemistry, text mining and similarity match. Then, we employed network pharmacology including text mining and molecular docking to identify the potential targets of these components. The key enriched processes, pathways and related diseases of these target proteins were analyzed by STRING database. At last, network analysis was conducted to identify most possible targets of components in SND. Among the 25 targets predicted by network analysis, tumor necrosis factor α (TNF-α) was firstly experimentally validated in molecular and cellular level. Results indicated that hypaconitine, mesaconitine, higenamine and quercetin in SND can directly bind to TNF-α, reduce the TNF-α-mediated cytotoxicity on L929 cells and exert anti-myocardial cell apoptosis effects. We envisage that network analysis will also be useful in target identification of a bioactive compound.

PMID: 27095146 [PubMed - indexed for MEDLINE]

Synthesis of human parainfluenza virus 2 nucleocapsid protein in yeast as nucleocapsid-like particles and investigation of its antigenic structure.

Appl Microbiol Biotechnol. 2016 May;100(10):4523-34

Authors: Bulavaitė A, Lasickienė R, Vaitiekaitė A, Sasnauskas K, Žvirblienė A

Abstract
The aim of this study was to investigate the suitability of yeast Saccharomyces cerevisiae expression system for the production of human parainfluenza virus type 2 (HPIV2) nucleocapsid (N) protein in the form of nucleocapsid-like particles (NLPs) and to characterize its antigenic structure. The gene encoding HPIV2 N amino acid (aa) sequence RefSeq NP_598401.1 was cloned into the galactose-inducible S. cerevisiae expression vector and its high-level expression was achieved. However, this recombinant HPIV2 N protein did not form NLPs. The PCR mutagenesis was carried out to change the encoded aa residues to the ones conserved across HPIV2 isolates. Synthesis of the modified proteins in yeast demonstrated that the single aa substitution NP_598401.1:p.D331V was sufficient for the self-assembly of NLPs. The significance of certain aa residues in this position was confirmed by analysing HPIV2 N protein structure models. To characterize the antigenic structure of NLP-forming HPIV2 N protein, a panel of monoclonal antibodies (MAbs) was generated. The majority of the MAbs raised against the recombinant NLPs recognized HPIV2-infected cells suggesting the antigenic similarity between the recombinant and virus-derived HPIV2 N protein. Fine epitope mapping revealed the C-terminal part (aa 386-504) as the main antigenic region of the HPIV2 N protein. In conclusion, the current study provides new data on the impact of HPIV2 N protein sequence variants on the NLP self-assembly and demonstrates an efficient production of recombinant HPIV2 N protein in the form of NLPs.

PMID: 26821928 [PubMed - indexed for MEDLINE]

Social media for arthritis-related comparative effectiveness and safety research and the impact of direct-to-consumer advertising.

Arthritis Res Ther. 2017 Mar 07;19(1):48

Authors: Curtis JR, Chen L, Higginbotham P, Nowell WB, Gal-Levy R, Willig J, Safford M, Coe J, O'Hara K, Sa'adon R

Abstract
BACKGROUND: Social media may complement traditional data sources to answer comparative effectiveness/safety questions after medication licensure.
METHODS: The Treato platform was used to analyze all publicly available social media data including Facebook, blogs, and discussion boards for posts mentioning inflammatory arthritis (e.g. rheumatoid, psoriatic). Safety events were self-reported by patients and mapped to medical ontologies, resolving synonyms. Disease and symptom-related treatment indications were manually redacted. The units of analysis were unique terms in posts. Pre-specified conditions (e.g. herpes zoster (HZ)) were selected based upon safety signals from clinical trials and reported as pairwise odds ratios (ORs); drugs were compared with Fisher's exact test. Empirically identified events were analyzed using disproportionality analysis and reported as relative reporting ratios (RRRs). The accuracy of a natural language processing (NLP) classifier to identify cases of shingles associated with arthritis medications was assessed.
RESULTS: As of October 2015, there were 785,656 arthritis-related posts. Posts were predominantly US posts (75%) from patient authors (87%) under 40 years of age (61%). For HZ posts (n = 1815), ORs were significantly increased with tofacitinib versus other rheumatoid arthritis therapies. ORs for mentions of perforated bowel (n = 13) were higher with tocilizumab versus other therapies. RRRs associated with tofacitinib were highest in conditions related to baldness and hair regrowth, infections and cancer. The NLP classifier had a positive predictive value of 91% to identify HZ. There was a threefold increase in posts following television direct-to-consumer advertisement (p = 0.04); posts expressing medication safety concerns were significantly more frequent than favorable posts.
CONCLUSION: Social media is a challenging yet promising data source that may complement traditional approaches for comparative effectiveness research for new medications.

PMID: 28270190 [PubMed - in process]

Controlling testing volume for respiratory viruses using machine learning and text mining.

AMIA Annu Symp Proc. 2016;2016:1910-1919

Authors: Mai MV, Krauthammer M

Abstract
Viral testing for pediatric inpatients with respiratory symptoms is common, with considerable associated charges. In an attempt to reduce testing volumes, we studied whether data available at the time of admission could aid in identifying children with low likelihood of having a particular viral origin of their symptoms, and thus safely forgo broad viral testing. We collected clinical data for 1,685 pediatric inpatients receiving respiratory virus testing from 2010-2012. Machine-learning on the data allowed us to construct pre-test models predicting whether a patient would test positive for a particular virus. Text mining improved the predictions for one viral test. Cost-sensitive models optimized for test sensitivity showed reasonable test specificities and an ability to reduce test volume by up to 46% for single viral tests. We conclude that diverse forms of data in the electronic medical record can be used productively to build models that help physicians reduce testing volumes.

PMID: 28269950 [PubMed - in process]

Ensembles of NLP Tools for Data Element Extraction from Clinical Notes.

AMIA Annu Symp Proc. 2016;2016:1880-1889

Authors: Kuo TT, Rao P, Maehara C, Doan S, Chaparro JD, Day ME, Farcas C, Ohno-Machado L, Hsu CN

Abstract
Natural Language Processing (NLP) is essential for concept extraction from narrative text in electronic health records (EHR). To extract numerous and diverse concepts, such as data elements (i.e., important concepts related to a certain medical condition), a plausible solution is to combine various NLP tools into an ensemble to improve extraction performance. However, it is unclear to what extent ensembles of popular NLP tools improve the extraction of numerous and diverse concepts. Therefore, we built an NLP ensemble pipeline to synergize the strength of popular NLP tools using seven ensemble methods, and to quantify the improvement in performance achieved by ensembles in the extraction of data elements for three very different cohorts. Evaluation results show that the pipeline can improve the performance of NLP tools, but there is high variability depending on the cohort.

PMID: 28269947 [PubMed - in process]

Investigating Longitudinal Tobacco Use Information from Social History and Clinical Notes in the Electronic Health Record.

AMIA Annu Symp Proc. 2016;2016:1209-1218

Authors: Wang Y, Chen ES, Pakhomov S, Lindemann E, Melton GB

Abstract
The electronic health record (EHR) provides an opportunity for improved use of clinical documentation including leveraging tobacco use information by clinicians and researchers. In this study, we investigated the content, consistency, and completeness of tobacco use data from structured and unstructured sources in the EHR. A natural language process (NLP) pipeline was utilized to extract details about tobacco use from clinical notes and free-text tobacco use comments within the social history module of an EHR system. We analyzed the consistency of tobacco use information within clinical notes, comments, and available structured fields for tobacco use. Our results indicate that structured fields for tobacco use alone may not be able to provide complete tobacco use information. While there was better consistency for some elements (e.g., status and type), inconsistencies were found particularly for temporal information. Further work is needed to improve tobacco use information integration from different parts of the EHR.

PMID: 28269918 [PubMed - in process]

Towards Comprehensive Clinical Abbreviation Disambiguation Using Machine-Labeled Training Data.

AMIA Annu Symp Proc. 2016;2016:560-569

Authors: Finley GP, Pakhomov SV, McEwan R, Melton GB

Abstract
Abbreviation disambiguation in clinical texts is a problem handled well by fully supervised machine learning methods. Acquiring training data, however, is expensive and would be impractical for large numbers of abbreviations in specialized corpora. An alternative is a semi-supervised approach, in which training data are automatically generated by substituting long forms in natural text with their corresponding abbreviations. Most prior implementations of this method either focus on very few abbreviations or do not test on real-world data. We present a realistic use case by testing several semi-supervised classification algorithms on a large hand-annotated medical record of occurrences of 74 ambiguous abbreviations. Despite notable differences between training and test corpora, classifiers achieve up to 90% accuracy. Our tests demonstrate that semi-supervised abbreviation disambiguation is a viable and extensible option for medical NLP systems.

PMID: 28269852 [PubMed - in process]

Automated Detection of Privacy Sensitive Conditions in C-CDAs: Security Labeling Services at the Department of Veterans Affairs.

AMIA Annu Symp Proc. 2016;2016:332-341

Authors: Bouhaddou O, Davis M, Donahue M, Mallia A, Griffin S, Teal J, Nebeker J

Abstract
Care coordination across healthcare organizations depends upon health information exchange. Various policies and laws govern permissible exchange, particularly when the information includes privacy sensitive conditions. The Department of Veterans Affairs (VA) privacy policy has required either blanket consent or manual sensitivity review prior to exchanging any health information. The VA experience has been an expensive, administratively demanding burden on staffand Veterans alike, particularly for patients without privacy sensitive conditions. Until recently, automatic sensitivity determination has not been feasible. This paper proposes a policy-driven algorithmic approach (Security Labeling Service or SLS) to health information exchange that automatically detects the presence or absence of specific privacy sensitive conditions and then, to only require a Veteran signed consent for release when actually present. The SLS was applied successfully to a sample of real patient Consolidated-Clinical Document Architecture(C-CDA) documents. The SLS identified standard terminology codes by both parsing structured entries and analyzing textual information using Natural Language Processing (NLP).

PMID: 28269828 [PubMed - in process]

Visualizing patient journals by combining vital signs monitoring and natural language processing.

Conf Proc IEEE Eng Med Biol Soc. 2016 Aug;2016:2529-2532

Authors: Vilic A, Petersen JA, Hoppe K, Sorensen HB

Abstract
This paper presents a data-driven approach to graphically presenting text-based patient journals while still maintaining all textual information. The system first creates a timeline representation of a patients' physiological condition during an admission, which is assessed by electronically monitoring vital signs and then combining these into Early Warning Scores (EWS). Hereafter, techniques from Natural Language Processing (NLP) are applied on the existing patient journal to extract all entries. Finally, the two methods are combined into an interactive timeline featuring the ability to see drastic changes in the patients' health, and thereby enabling staff to see where in the journal critical events have taken place.

PMID: 28268838 [PubMed - in process]

S2NI: a mobile platform for nutrition monitoring from spoken data.

Conf Proc IEEE Eng Med Biol Soc. 2016 Aug;2016:1991-1994

Authors: Hezarjaribi N, Reynolds CA, Miller DT, Chaytor N, Ghasemzadeh H

Abstract
Diet and physical activity are important lifestyle and behavioral factors in self-management and prevention of many chronic diseases. Mobile sensors such as accelerometers have been used in the past to objectively measure physical activity or detect eating time. Diet monitoring, however, still relies on self-recorded data by end users where individuals use mobile devices for recording nutrition intake by either entering text or taking images. Such approaches have shown low adherence in technology adoption and achieve only moderate accuracy. In this paper, we propose development and validation of Speech-to-Nutrient-Information (S2NI), a comprehensive nutrition monitoring system that combines speech processing, natural language processing, and text mining in a unified platform to extract nutrient information such as calorie intake from spoken data. After converting the voice data to text, we identify food name and portion size information within the text. We then develop a tiered matching algorithm to search the food name in our nutrition database and to accurately compute calorie intake. Due to its pervasive nature and ease of use, S2NI enables users to report their diet routine more frequently and at anytime through their smartphone. We evaluate S2NI using real data collected with 10 participants. Our experimental results show that S2NI achieves 80.6% accuracy in computing calorie intake.

PMID: 28268720 [PubMed - in process]

Unsupervised Ensemble Ranking of Terms in Electronic Health Record Notes Based on Their Importance to Patients.

J Biomed Inform. 2017 Mar 03;:

Authors: Chen J, Yu H

Abstract
BACKGROUND: Allowing patients to access their own electronic health record (EHR) notes through online patient portals has the potential to improve patient-centered care. However, EHR notes contain abundant medical jargon that can be difficult for patients to comprehend. One way to help patients is to reduce information overload and help them focus on medical terms that matter most to them. Targeted education can then be developed to improve patient EHR comprehension and the quality of care.
OBJECTIVE: The aim of this work was to develop FIT (Finding Important Terms for patients), an unsupervised natural language processing (NLP) system that ranks medical terms in EHR notes based on their importance to patients.
METHODS: We built FIT on a new unsupervised ensemble ranking model derived from the biased random walk algorithm to combine heterogeneous information resources for ranking candidate terms from each EHR note. Specifically, FIT integrates four single views (rankers) for term importance: patient use of medical concepts, document-level term salience, word-occurrence based term relatedness, and topic coherence. It also incorporates partial information of term importance as conveyed by terms' unfamiliarity levels and semantic types. We evaluated FIT on 90 expert-annotated EHR notes and used the four single-view rankers as baselines. In addition, we implemented three benchmark unsupervised ensemble ranking methods as strong baselines.
RESULTS: FIT achieved 0.885 AUC-ROC for ranking candidate terms from EHR notes to identify important terms. When including term identification, the performance of FIT for identifying important terms from EHR notes was 0.813 AUC-ROC. Both performance scores significantly exceeded the corresponding scores from the four single rankers (P<.001). FIT also outperformed the three ensemble rankers for most metrics. Its performance is relatively insensitive to its parameter.
CONCLUSIONS: FIT can automatically rank EHR terms important to patients. It may help develop future interventions to improve quality of care. By using unsupervised learning as well as a robust and flexible framework for information fusion, FIT can be readily applied to other domains and applications.

PMID: 28267590 [PubMed - as supplied by publisher]

Metabolomic network analysis of estrogen-stimulated MCF-7 cells: a comparison of overrepresentation analysis, quantitative enrichment analysis and pathway analysis versus metabolite network analysis.

Arch Toxicol. 2017 Jan;91(1):217-230

Authors: Maertens A, Bouhifd M, Zhao L, Odwin-DaCosta S, Kleensang A, Yager JD, Hartung T

Abstract
In the context of the Human Toxome project, mass spectroscopy-based metabolomics characterization of estrogen-stimulated MCF-7 cells was studied in order to support the untargeted deduction of pathways of toxicity. A targeted and untargeted approach using overrepresentation analysis (ORA), quantitative enrichment analysis (QEA) and pathway analysis (PA) and a metabolite network approach were compared. Any untargeted approach necessarily has some noise in the data owing to artifacts, outliers and misidentified metabolites. Depending on the chemical analytical choices (sample extraction, chromatography, instrument and settings, etc.), only a partial representation of all metabolites will be achieved, biased by both the analytical methods and the database used to identify the metabolites. Here, we show on the one hand that using a data analysis approach based exclusively on pathway annotations has the potential to miss much that is of interest and, in the case of misidentified metabolites, can produce perturbed pathways that are statistically significant yet uninformative for the biological sample at hand. On the other hand, a targeted approach, by narrowing its focus and minimizing (but not eliminating) misidentifications, renders the likelihood of a spurious pathway much smaller, but the limited number of metabolites also makes statistical significance harder to achieve. To avoid an analysis dependent on pathways, we built a de novo network using all metabolites that were different at 24 h with and without estrogen with a p value <0.01 (53) in the STITCH database, which links metabolites based on known reactions in the main metabolic network pathways but also based on experimental evidence and text mining. The resulting network contained a "connected component" of 43 metabolites and helped identify non-endogenous metabolites as well as pathways not visible by annotation-based approaches. Moreover, the most highly connected metabolites (energy metabolites such as pyruvate and alpha-ketoglutarate, as well as amino acids) showed only a modest change between proliferation with and without estrogen. Here, we demonstrate that estrogen has subtle but potentially phenotypically important alterations in the acyl-carnitine fatty acids, acetyl-putrescine and succinoadenosine, in addition to likely subtle changes in key energy metabolites that, however, could not be verified consistently given the technical limitations of this approach. Finally, we show that a network-based approach combined with text mining identifies pathways that would otherwise neither be considered statistically significant on their own nor be identified via ORA, QEA, or PA.

PMID: 27039105 [PubMed - indexed for MEDLINE]

BIOMedical Search Engine Framework: Lightweight and customized implementation of domain-specific biomedical search engines.

Comput Methods Programs Biomed. 2016 Jul;131:63-77

Authors: Jácome AG, Fdez-Riverola F, Lourenço A

Abstract
BACKGROUND AND OBJECTIVES: Text mining and semantic analysis approaches can be applied to the construction of biomedical domain-specific search engines and provide an attractive alternative to create personalized and enhanced search experiences. Therefore, this work introduces the new open-source BIOMedical Search Engine Framework for the fast and lightweight development of domain-specific search engines. The rationale behind this framework is to incorporate core features typically available in search engine frameworks with flexible and extensible technologies to retrieve biomedical documents, annotate meaningful domain concepts, and develop highly customized Web search interfaces.
METHODS: The BIOMedical Search Engine Framework integrates taggers for major biomedical concepts, such as diseases, drugs, genes, proteins, compounds and organisms, and enables the use of domain-specific controlled vocabulary. Technologies from the Typesafe Reactive Platform, the AngularJS JavaScript framework and the Bootstrap HTML/CSS framework support the customization of the domain-oriented search application. Moreover, the RESTful API of the BIOMedical Search Engine Framework allows the integration of the search engine into existing systems or a complete web interface personalization.
RESULTS: The construction of the Smart Drug Search is described as proof-of-concept of the BIOMedical Search Engine Framework. This public search engine catalogs scientific literature about antimicrobial resistance, microbial virulence and topics alike. The keyword-based queries of the users are transformed into concepts and search results are presented and ranked accordingly. The semantic graph view portraits all the concepts found in the results, and the researcher may look into the relevance of different concepts, the strength of direct relations, and non-trivial, indirect relations. The number of occurrences of the concept shows its importance to the query, and the frequency of concept co-occurrence is indicative of biological relations meaningful to that particular scope of research. Conversely, indirect concept associations, i.e. concepts related by other intermediary concepts, can be useful to integrate information from different studies and look into non-trivial relations.
CONCLUSIONS: The BIOMedical Search Engine Framework supports the development of domain-specific search engines. The key strengths of the framework are modularity and extensibilityin terms of software design, the use of open-source consolidated Web technologies, and the ability to integrate any number of biomedical text mining tools and information resources. Currently, the Smart Drug Search keeps over 1,186,000 documents, containing more than 11,854,000 annotations for 77,200 different concepts. The Smart Drug Search is publicly accessible at http://sing.ei.uvigo.es/sds/. The BIOMedical Search Engine Framework is freely available for non-commercial use at https://github.com/agjacome/biomsef.

PMID: 27265049 [PubMed - indexed for MEDLINE]

Text mining for improved exposure assessment.

PLoS One. 2017;12(3):e0173132

Authors: Larsson K, Baker S, Silins I, Guo Y, Stenius U, Korhonen A, Berglund M

Abstract
Chemical exposure assessments are based on information collected via different methods, such as biomonitoring, personal monitoring, environmental monitoring and questionnaires. The vast amount of chemical-specific exposure information available from web-based databases, such as PubMed, is undoubtedly a great asset to the scientific community. However, manual retrieval of relevant published information is an extremely time consuming task and overviewing the data is nearly impossible. Here, we present the development of an automatic classifier for chemical exposure information. First, nearly 3700 abstracts were manually annotated by an expert in exposure sciences according to a taxonomy exclusively created for exposure information. Natural Language Processing (NLP) techniques were used to extract semantic and syntactic features relevant to chemical exposure text. Using these features, we trained a supervised machine learning algorithm to automatically classify PubMed abstracts according to the exposure taxonomy. The resulting classifier demonstrates good performance in the intrinsic evaluation. We also show that the classifier improves information retrieval of chemical exposure data compared to keyword-based PubMed searches. Case studies demonstrate that the classifier can be used to assist researchers by facilitating information retrieval and classification, enabling data gap recognition and overviewing available scientific literature using chemical-specific publication profiles. Finally, we identify challenges to be addressed in future development of the system.

PMID: 28257498 [PubMed - in process]