Supporting Topic Modeling and Trends Analysis in Biomedical Literature.

J Biomed Inform. 2020 Sep 21;:103574

Authors: Kavvadias S, Drosatos G, Kaldoudi E

Abstract
Topic modeling refers to a suite of probabilistic algorithms for extracting popular topics from a collection of documents. A common approach involves the use of the Latent Dirichlet Allocation (LDA) algorithm, and, although free implementations are available, their deployment in general requires a certain degree of programming expertise. This paper presents a user-friendly web-based application, specifically designed for the biomedical professional, that supports the entire process of topic modeling and comparative trends analysis of scientific literature. The application was evaluated for its efficacy and usability by intended users with no programming expertise (15 biomedical professionals). Results of evaluation showed a positive acceptance of system functionalities and an overall usability score of 76/100 in the System Usability Score (SUS) scale. This suggests that literature topic modeling can become more popular amongst biomedical professionals via the use of a user-friendly application that fully supports the entire workflow, thus opening new perspectives for literature review and scientific research.

PMID: 32971274 [PubMed - as supplied by publisher]

User-Centered Design of a Web-Based Crowdsourcing-Integrated Semantic Text Annotation Tool for Building a Mental Health Knowledge Base.

J Biomed Inform. 2020 Sep 19;:103571

Authors: He X, Zhang H, Bian J

Abstract
BACKGROUND: One in five U.S. adults lives with some kind of mental health condition and 4.6% of all U.S. adults have a serious mental illness. The Internet has become the first place for these people to seek online mental health information for help. However, online mental health information is not well-organized and often of low quality. There have been efforts in building evidence-based mental health knowledgebases curated with information manually extracted from the high-quality scientific literature. Manual extraction is inefficient. Crowdsourcing can potentially be a low-cost mechanism to collect labeled data from non-expert laypeople. However, there is not an existing annotation tool integrated with popular crowdsourcing platforms to perform the information extraction tasks. In our previous work, we prototyped a Semantic Text Annotation Tool (STAT) to address this gap.
OBJECTIVE: We aimed to refine the STAT prototype (1) to improve its usability and (2) to enhance the crowdsourcing workflow efficiency to facilitate the construction of evidence-based mental health knowledgebase, following a user-centered design (UCD) approach.
METHODS: Following UCD principles, we conducted four design iterations to improve the initial STAT prototype. In the first two iterations, usability testing focus groups were conducted internally with 8 participants recruited from a convenient sample, and the usability was evaluated with a modified System Usability Scale (SUS). In the following two iterations, usability testing was conducted externally using the Amazon Mechanical Turk (MTurk) platform. In each iteration, we summarized the usability testing results through thematic analysis, identified usability issues, and conducted a heuristic evaluation to map identified usability issues to Jakob Nielsen's usability heuristics. We collected suggested improvements in the usability testing sessions and enhanced STAT accordingly in the next UCD iteration. After four UCD iterations, we conducted a case study of the system on MTurk using mental health related scientific literature. We compared the performance of crowdsourcing workers with two expert annotators from two aspects: efficiency and quality.
RESULTS: The SUS score increased from 70.3 ± 12.5 to 81.1 ± 9.8 after the two internal UCD iterations as we improved STAT's functionality based on the suggested improvements. We then evaluated STAT externally through MTurk in the following two iterations. The SUS score decreased to 55.7 ± 20.1 in the third iteration, probably because of the complexity of the tasks. After further simplification of STAT and the annotation tasks with an improved annotation guideline, the SUS score increased to 73.8 ± 13.8 in the fourth iteration of UCD. In the evaluation case study, on average, the workers spent 125.5 ± 69.2 seconds on the onboarding tutorial and the crowdsourcing workers spent significantly less time on the annotation tasks compared to the two experts. In terms of annotation quality, the workers' annotation results achieved average F1-scores ranged from 0.62 to 0.84 for the different sentences.
CONCLUSIONS: We successfully developed a web-based semantic text annotation tool, STAT, to facilitate the curation of semantic web knowledgebases through four UCD iterations. The lessons learned from the UCD process could serve as a guide to further enhance STAT and the development and design of other crowdsourcing-based semantic text annotation tasks. Our study also showed that a well-organized, informative annotation guideline is as important as the annotation tool itself. Further, we learned that a crowdsourcing task should consist of multiple simple microtasks rather than a complicated task.

PMID: 32961307 [PubMed - as supplied by publisher]

An interactive retrieval system for clinical trial studies with context-dependent protocol elements.

PLoS One. 2020;15(9):e0238290

Authors: Park J, Park S, Kim K, Hwang W, Yoo S, Yi GS, Lee D

Abstract
A well-defined protocol for a clinical trial guarantees a successful outcome report. When designing the protocol, most researchers refer to electronic databases and extract protocol elements using a keyword search. However, state-of-the-art database systems only offer text-based searches for user-entered keywords. In this study, we present a database system with a context-dependent and protocol-element-selection function for successfully designing a clinical trial protocol. To do this, we first introduce a database for a protocol retrieval system constructed from individual protocol data extracted from 184,634 clinical trials and 13,210 frame structures of clinical trial protocols. The database contains a variety of semantic information that allows the filtering of protocols during the search operation. Based on the database, we developed a web application called the clinical trial protocol database system (CLIPS; available at https://corus.kaist.edu/clips). This system enables an interactive search by utilizing protocol elements. To enable an interactive search for combinations of protocol elements, CLIPS provides optional next element selection according to the previous element in the form of a connected tree. The validation results show that our method achieves better performance than that of existing databases in predicting phenotypic features.

PMID: 32946464 [PubMed - as supplied by publisher]

A systematic literature review of automatic Alzheimer's disease detection from speech and language.

J Am Med Inform Assoc. 2020 Sep 14;:

Authors: Petti U, Baker S, Korhonen A

Abstract
OBJECTIVE: In recent years numerous studies have achieved promising results in Alzheimer's Disease (AD) detection using automatic language processing. We systematically review these articles to understand the effectiveness of this approach, identify any issues and report the main findings that can guide further research.
MATERIALS AND METHODS: We searched PubMed, Ovid, and Web of Science for articles published in English between 2013 and 2019. We performed a systematic literature review to answer 5 key questions: (1) What were the characteristics of participant groups? (2) What language data were collected? (3) What features of speech and language were the most informative? (4) What methods were used to classify between groups? (5) What classification performance was achieved?
RESULTS AND DISCUSSION: We identified 33 eligible studies and 5 main findings: participants' demographic variables (especially age ) were often unbalanced between AD and control group; spontaneous speech data were collected most often; informative language features were related to word retrieval and semantic, syntactic, and acoustic impairment; neural nets, support vector machines, and decision trees performed well in AD detection, and support vector machines and decision trees performed well in decline detection; and average classification accuracy was 89% in AD and 82% in mild cognitive impairment detection versus healthy control groups.
CONCLUSION: The systematic literature review supported the argument that language and speech could successfully be used to detect dementia automatically. Future studies should aim for larger and more balanced datasets, combine data collection methods and the type of information analyzed, focus on the early stages of the disease, and report performance using standardized metrics.

PMID: 32929494 [PubMed - as supplied by publisher]

ANDDigest: a new web-based module of ANDSystem for the search of knowledge in the scientific literature.

BMC Bioinformatics. 2020 Sep 14;21(Suppl 11):228

Authors: Ivanisenko TV, Saik OV, Demenkov PS, Ivanisenko NV, Savostianov AN, Ivanisenko VA

Abstract
BACKGROUND: The rapid growth of scientific literature has rendered the task of finding relevant information one of the critical problems in almost any research. Search engines, like Google Scholar, Web of Knowledge, PubMed, Scopus, and others, are highly effective in document search; however, they do not allow knowledge extraction. In contrast to the search engines, text-mining systems provide extraction of knowledge with representations in the form of semantic networks. Of particular interest are tools performing a full cycle of knowledge management and engineering, including automated retrieval, integration, and representation of knowledge in the form of semantic networks, their visualization, and analysis. STRING, Pathway Studio, MetaCore, and others are well-known examples of such products. Previously, we developed the Associative Network Discovery System (ANDSystem), which also implements such a cycle. However, the drawback of these systems is dependence on the employed ontologies describing the subject area, which limits their functionality in searching information based on user-specified queries.
RESULTS: The ANDDigest system is a new web-based module of the ANDSystem tool, permitting searching within PubMed by using dictionaries from the ANDSystem tool and sets of user-defined keywords. ANDDigest allows performing the search based on complex queries simultaneously, taking into account many types of objects from the ANDSystem's ontology. The system has a user-friendly interface, providing sorting, visualization, and filtering of the found information, including mapping of mentioned objects in text, linking to external databases, sorting of data by publication date, citations number, journal H-indices, etc. The system provides data on trends for identified entities based on dynamics of interest according to the frequency of their mentions in PubMed by years.
CONCLUSIONS: The main feature of ANDDigest is its functionality, serving as a specialized search for information about multiple associative relationships of objects from the ANDSystem's ontology vocabularies, taking into account user-specified keywords. The tool can be applied to the interpretation of experimental genetics data, the search for associations between molecular genetics objects, and the preparation of scientific and analytical reviews. It is presently available at https://anddigest.sysbio.ru/ .

PMID: 32921303 [PubMed - in process]

End-to-end semantic segmentation of personalized deep brain structures for non-invasive brain stimulation.

Neural Netw. 2020 May;125:233-244

Authors: Rashed EA, Gomez-Tames J, Hirata A

Abstract
Electro-stimulation or modulation of deep brain regions is commonly used in clinical procedures for the treatment of several nervous system disorders. In particular, transcranial direct current stimulation (tDCS) is widely used as an affordable clinical application that is applied through electrodes attached to the scalp. However, it is difficult to determine the amount and distribution of the electric field (EF) in the different brain regions due to anatomical complexity and high inter-subject variability. Personalized tDCS is an emerging clinical procedure that is used to tolerate electrode montage for accurate targeting. This procedure is guided by computational head models generated from anatomical images such as MRI. Distribution of the EF in segmented head models can be calculated through simulation studies. Therefore, fast, accurate, and feasible segmentation of different brain structures would lead to a better adjustment for customized tDCS studies. In this study, a single-encoder multi-decoders convolutional neural network is proposed for deep brain segmentation. The proposed architecture is trained to segment seven deep brain structures using T1-weighted MRI. Network generated models are compared with a reference model constructed using a semi-automatic method, and it presents a high matching especially in Thalamus (Dice Coefficient (DC) = 94.70%), Caudate (DC = 91.98%) and Putamen (DC = 90.31%) structures. Electric field distribution during tDCS in generated and reference models matched well each other, suggesting its potential usefulness in clinical practice.

PMID: 32151914 [PubMed - indexed for MEDLINE]

A decision support system on the obesity management and consultation during childhood and adolescence using ontology and semantic rules.

J Biomed Inform. 2020 Sep 07;:103554

Authors: Taçyıldız Ö, Çelik Ertuğrul D

Abstract
BACKGROUND: Obesity is defined as abnormal or excessive fat accumulation that presents a risk to health according to the World Health Organization (WHO). Pediatric or childhood obesity is the most prevalent nutritional disorder among children and adolescents worldwide. In pediatric or childhood obesity, constant monitoring of the pediatric patients by health experts is required to provide efficient obesity management and treatment. Therefore, the patients are examined on a regular basis, the measurements are compared against predefined percentile values and the development of the pediatric patient is examined.
RESULTS: This study discusses the design, implementation, and potential use of an ontology-based obesity management and consultation system which is a decision support system for health experts during treatments of the children and adolescent patients. The system does not only share instant gathered medical data to health experts but also examines the data as a smart medical assistant. The system includes an ontology-based inference engine module, which is a decision support module, and used to infer certain personalized suggestions for patients. Suggestions in four categories emerged as a result: (1) Development Feedback Suggestions, (2) Calorie Intake Suggestions and Physical Activities, (3) Mom Suggestions, and (4) Obesity Treatment Stage Suggestions. The methodologies applied and main technical contributions are discussed in three aspects: (1) Obesity Tracking Ontology, (2) Semantic Web Rule Knowledge base, and (3) Inference Engine Module. In this study, unlike other similar studies, ontology and rule based smart medical assistant which have different functionalities from adults' obesity management is considered especially for obesity management of children and adolescents. The system also includes intensive pediatric health care expert involvement. Eighty case studies from real anonymous pediatric patients are analyzed and discussed in this experimental study.
CONCLUSIONS: The results retrieved from 80 case studies are promising in demonstrating the applicability, effectiveness and efficiency of the proposed approach. The inference engine module of the proposed system can be integrated semantically into intelligent and distributed decision support systems, and the system ontology can be used as a knowledge base in similar systems.

PMID: 32911081 [PubMed - as supplied by publisher]

SCALEUS-FD: A FAIR Data Tool for Biomedical Applications.

Biomed Res Int. 2020;2020:3041498

Authors: Pereira A, Lopes RP, Oliveira JL

Abstract
The Semantic Web and Linked Data concepts and technologies have empowered the scientific community with solutions to take full advantage of the increasingly available distributed and heterogeneous data in distinct silos. Additionally, FAIR Data principles established guidelines for data to be Findable, Accessible, Interoperable, and Reusable, and they are gaining traction in data stewardship. However, to explore their full potential, we must be able to transform legacy solutions smoothly into the FAIR Data ecosystem. In this paper, we introduce SCALEUS-FD, a FAIR Data extension of a legacy semantic web tool successfully used for data integration and semantic annotation and enrichment. The core functionalities of the solution follow the Semantic Web and Linked Data principles, offering a FAIR REST API for machine-to-machine operations. We applied a set of metrics to evaluate its "FAIRness" and created an application scenario in the rare diseases domain.

PMID: 32908882 [PubMed - in process]

Ontological approach to the knowledge systematization of a toxic process and toxic course representation framework for early drug risk management.

Sci Rep. 2020 Sep 03;10(1):14581

Authors: Yamagata Y, Yamada H

Abstract
Various types of drug toxicity can halt the development of a drug. Because drugs are xenobiotics, they inherently have the potential to cause injury. Clarifying the mechanisms of toxicity to evaluate and manage drug safety during drug development is extremely important. However, toxicity mechanisms, especially hepatotoxic mechanisms, are very complex. The significant exposure of liver cells to drugs can cause dysfunction, cell injury, and organ failure in the liver. To clarify potential risks in drug safety management, it is necessary to systematize knowledge from a consistent viewpoint. In this study, we adopt an ontological approach. Ontology provides a controlled vocabulary for sharing and reusing of various data with a computer-friendly manner. We focus on toxic processes, especially hepatotoxic processes, and construct the toxic process ontology (TXPO). The TXPO systematizes knowledge concerning hepatotoxic courses with consistency and no ambiguity. In our application study, we developed a toxic process interpretable knowledge system (TOXPILOT) to bridge the gaps between basic science and medicine for drug safety management. Using semantic web technology, TOXPILOT supports the interpretation of toxicity mechanisms and provides visualizations of toxic courses with useful information based on ontology. Our system will contribute to various applications for drug safety evaluation and management.

PMID: 32883995 [PubMed - in process]

Design and Use of Semantic Resources: Findings from the Section on Knowledge Representation and Management of the 2020 International Medical Informatics Association Yearbook.

Yearb Med Inform. 2020 Aug;29(1):163-168

Authors: Dhombres F, Charlet J, Section Editors for the IMIA Yearbook Section on Knowledge Representation and Management

Abstract
OBJECTIVE: To select, present, and summarize the best papers in the field of Knowledge Representation and Management (KRM) published in 2019.
METHODS: A comprehensive and standardized review of the biomedical informatics literature was performed to select the most interesting papers of KRM published in 2019, based on PubMed and ISI Web Of Knowledge queries.
RESULTS: Four best papers were selected among 1,189 publications retrieved, following the usual International Medical Informatics Association Yearbook reviewing process. In 2019, research areas covered by pre-selected papers were represented by the design of semantic resources (methods, visualization, curation) and the application of semantic representations for the integration/enrichment of biomedical data. Besides new ontologies and sound methodological guidance to rethink knowledge bases design, we observed large scale applications, promising results for phenotypes characterization, semantic-aware machine learning solutions for biomedical data analysis, and semantic provenance information representations for scientific reproducibility evaluation.
CONCLUSION: In the KRM selection for 2019, research on knowledge representation demonstrated significant contributions both in the design and in the application of semantic resources. Semantic representations serve a great variety of applications across many medical domains, with actionable results.

PMID: 32823311 [PubMed - as supplied by publisher]

Language and Speech Markers of Primary Progressive Aphasia: A Systematic Review.

Am J Speech Lang Pathol. 2020 Aug 18;:1-20

Authors: Stalpaert J, Cocquyt EM, Criel Y, Segers L, Miatton M, Van Langenhove T, van Mierlo P, De Letter M

Abstract
Purpose This systematic review aimed to establish language and speech markers to support the clinical diagnosis of primary progressive aphasia (PPA) and its clinical phenotypes. Our first objective was to identify behavioral language and speech markers of early-stage PPA. Our second objective was to identify the electrophysiological correlates of the language and speech characteristics in PPA. Method The databases MEDLINE, Web of Science, and Embase were searched for relevant articles. To identify behavioral markers, the initial subjective complaints and the language and speech deficits detected during the initial diagnostic evaluation were summarized for PPA in general and each clinical variant according to the 2011 consensus diagnostic criteria (nonfluent variant [NFV], semantic variant, and logopenic variant [LV]). To identify electrophysiological markers, the studies in which event-related potentials (ERPs) were elicited by a language or speech paradigm in patients with PPA were included. Results In total, 114 relevant studies were identified, including 110 behavioral studies and only four electrophysiological studies. This review suggests that patients with the semantic variant could be accurately differentiated from the NFV and LV in the initial stages based on the consensus criteria. Nonetheless, the early differentiation between the NFV and LV is not straightforward. In the four electrophysiological studies, differences in the latency, amplitude, and topographical distribution of the semantic N400 component were found between patients with PPA and healthy controls. Conclusions To accurately differentiate the NFV from the LV, it could be important to assess the language and speech degeneration by more specific assessments and by more objective diagnostic methods that offer insights into the language-related processes. Electrophysiological markers of PPA were not identified in this review due to the low number of studies that investigated language-related ERPs. More controlled ERP studies in larger patient cohorts are needed to investigate the diagnostic applicability of language-related ERPs in PPA. Supplemental Material https://doi.org/10.23641/asha.12798080.

PMID: 32810414 [PubMed - as supplied by publisher]

COVID-19 outbreak: organisation of a geriatric assessment and coordination unit. A French example.

Age Ageing. 2020 07 01;49(4):516-522

Authors: Koeberle S, Tannou T, Bouiller K, Becoulet N, Outrey J, Chirouze C, Aubry R

Abstract
Older people are particularly affected by the COVID-19 outbreak because of their vulnerability as well as the complexity of health organisations, particularly in the often-compartmentalised interactions between community, hospital and nursing home actors. In this endemic situation, with massive flows of patients requiring holistic management including specific and intensive care, the appropriate assessment of each patient's level of care and the organisation of specific networks is essential. To that end, we propose here a territorial organisation of health care, favouring communication between all actors. This organisation of care is based on three key points: To use the basis of territorial organisation of health by facilitating the link between hospital settings and geriatric sectors at the regional level.To connect private, medico-social and hospital actors through a dedicated centralised unit for evaluation, geriatric coordination of care and decision support. A geriatrician coordinates this multidisciplinary unit. It includes an emergency room doctor, a supervisor from the medical regulation centre (Centre 15), an infectious disease physician, a medical hygienist and a palliative care specialist.To organise an ad hoc follow-up channel, including the necessary resources for the different levels of care required, according to the resources of the territorial network, and the creation of a specific COVID geriatric palliative care service. This organisation meets the urgent health needs of all stakeholders, facilitating its deployment and allows the sustainable implementation of a coordinated geriatric management dynamic between the stakeholders on the territory.

PMID: 32725209 [PubMed - indexed for MEDLINE]

Sentiment Analysis and Emotion Understanding during the COVID-19 Pandemic in Spain and Its Impact on Digital Ecosystems.

Int J Environ Res Public Health. 2020 Jul 31;17(15):

Authors: de Las Heras-Pedrosa C, Sánchez-Núñez P, Peláez JI

Abstract
COVID-19 has changed our lives forever. The world we knew until now has been transformed and nowadays we live in a completely new scenario in a perpetual restructuring transition, in which the way we live, relate, and communicate with others has been altered permanently. Within this context, risk communication is playing a decisive role when informing, transmitting, and channeling the flow of information in society. COVID-19 has posed a real pandemic risk management challenge in terms of impact, preparedness, response, and mitigation by governments, health organizations, non-governmental organizations (NGOs), mass media, and stakeholders. In this study, we monitored the digital ecosystems during March and April 2020, and we obtained a sample of 106,261 communications through the analysis of APIs and Web Scraping techniques. This study examines how social media has affected risk communication in uncertain contexts and its impact on the emotions and sentiments derived from the semantic analysis in Spanish society during the COVID-19 pandemic.

PMID: 32751866 [PubMed - in process]

Knowledge Graph Approach to Combustion Chemistry and Interoperability.

ACS Omega. 2020 Jul 28;5(29):18342-18348

Authors: Farazi F, Salamanca M, Mosbach S, Akroyd J, Eibeck A, Aditya LK, Chadzynski A, Pan K, Zhou X, Zhang S, Lim MQ, Kraft M

Abstract
In this paper, we demonstrate through examples how the concept of a Semantic Web based knowledge graph can be used to integrate combustion modeling into cross-disciplinary applications and in particular how inconsistency issues in chemical mechanisms can be addressed. We discuss the advantages of linked data that form the essence of a knowledge graph and how we implement this in a number of interconnected ontologies, specifically in the context of combustion chemistry. Central to this is OntoKin, an ontology we have developed for capturing both the content and the semantics of chemical kinetic reaction mechanisms. OntoKin is used to represent the example mechanisms from the literature in a knowledge graph, which itself is part of the existing, more general knowledge graph and ecosystem of autonomous software agents that are acting on it. We describe a web interface, which allows users to interact with the system, upload and compare the existing mechanisms, and query species and reactions across the knowledge graph. The utility of the knowledge-graph approach is demonstrated for two use-cases: querying across multiple mechanisms from the literature and modeling the atmospheric dispersion of pollutants emitted by ships. As part of the query use-case, our ontological tools are applied to identify variations in the rate of a hydrogen abstraction reaction from methane as represented by 10 different mechanisms.

PMID: 32743209 [PubMed]

PyramidTags: Context-, Time- and Word Order-Aware Tag Maps to Explore Large Document Collections.

IEEE Trans Vis Comput Graph. 2020 Jul 17;PP:

Authors: Knittel J, Koch S, Ertl T

Abstract
It is difficult to explore large text collections if no or little information is available on the contained documents. Hence, starting analytic tasks on such corpora is challenging for many stakeholders from various domains. As a remedy, recent visualization research suggests to use visual spatializations of representative text documents or tags to explore text collections. With PyramidTags, we introduce a novel approach for summarizing large text collections visually. In contrast to previous work, PyramidTags in particular aims at creating an improved representation that incorporates both temporal evolution and semantic relationship of visualized tags within the summarized document collection. As a result, it equips analysts with a visual starting point for interactive exploration to not only get an overview of the main terms and phrases of the corpus, but also to grasp important ideas and stories. Analysts can hover and select multiple tags to explore relationships and retrieve the most relevant documents. In this work, we apply PyramidTags to hundreds of thousands of web-crawled news reports. Our benchmarks suggest that PyramidTags creates time- and context-aware layouts, while preserving the inherent word order of important pairs.

PMID: 32746277 [PubMed - as supplied by publisher]

Translation and Validation of the Caffeine Expectancy Questionnaire in Brazil (CaffEQ-BR).

Nutrients. 2020 Jul 28;12(8):

Authors: Mendes GF, Reis CEG, Nakano EY, da Costa THM, Saunders B, Zandonadi RP

Abstract
Caffeine is the world's most commonly used stimulant of the central nervous system. Caffeine is present in coffee and other beverages such as tea, soft drinks, and cocoa-based foods. The caffeine expectancy questionnaire was developed to investigate the effects of caffeine expectations and thus contribute to knowledge about its usage and subjective effects (response expectancies). This study aimed to evaluate caffeine expectation psychometrically in a sample of the Brazilian population. The original version of the "Caffeine Expectancy Questionnaire (CaffEQ)" was translated and validated into Brazilian-Portuguese and adapted to Brazilian culture to be used in the Brazilian adult (19-59 y) population. After the translation and back-translation processes of the original CaffEQ questionnaire, the content and semantic validation were performed by a group of experts. The Brazilian-Portuguese version of the questionnaire consists of 47 items, in seven factors, which assess subjective perceptions about the effects of caffeine. Interobserver reproducibility and internal consistency of the questionnaire were tested with a convenience sample (n = 50) of Brazilian adult consumers of caffeine sources, who completed the Brazilian CaffEQ (CaffEQ-BR) on two occasions separated by 24 h. All of the 47 questions were adequate regarding reliability, clarity, and comprehension. Psychometric properties could be replicated consistently. Appropriate internal consistency and validation were confirmed by Cronbach's alpha (α) 0.948, and an intraclass correlation coefficient of 0.976 was observed. The CaffEQ-BR was applied using a web-based platform to a convenience sample of Brazilian adults from all 27 Brazilian states (n = 4202 participants), along with measures of sociodemographic and caffeine consumption data. Factor validity was verified by confirmatory factor analysis. The seven factors presented a good fit for Root Mean Square Error of Approximation-RMSEA = 0.0332 (95% CI: 0.0290-0.0375). By confirming the validity and reliability of CaffEQ-BR, a useful tool is now available to assess caffeine expectations in the Brazilian adult population.

PMID: 32731330 [PubMed - in process]

Self-harm presentation across healthcare settings by sex in young people: an e-cohort study using routinely collected linked healthcare data in Wales, UK.

Arch Dis Child. 2020 04;105(4):347-354

Authors: Marchant A, Turner S, Balbuena L, Peters E, Williams D, Lloyd K, Lyons R, John A

Abstract
BACKGROUND: This study used individual-level linked data across general practice, emergency departments (EDs), outpatients and hospital admissions to examine contacts across settings and time by sex for self-harm in individuals aged 10-24 years old in Wales, UK.
METHODS: A whole population-based e-cohort study of routinely collected healthcare data was conducted. Rates of self-harm across settings over time by sex were examined. Individuals were categorised based on the service(s) to which they presented.
RESULTS: A total of 937 697 individuals aged 10-24 years contributed 5 369 794 person years of data from 1 January 2003 to 30 September 2015. Self-harm incidence was highest in primary care but remained stable over time (incident rate ratio (IRR)=1.0; 95% CI 0.9 to 1.1). Incidence of ED attendance increased over time (IRR=1.3; 95% CI 1.2 to 1.5) as did hospital admissions (IRR=1.4; 95% CI 1.1 to 1.6). Incidence in the 15-19 years age group was the highest across all settings. The largest increases were seen in the youngest age group. There were increases in ED attendances for both sexes; however, females are more likely than males to be admitted following this. This was most evident in individuals 10-15 years old, where 76% of females were admitted compared with just 49% of males. The majority of associated outpatient appointments were under a mental health specialty.
CONCLUSIONS: This is the first study to compare self-harm in people aged 10-24 years across primary care, EDs and hospital settings in the UK. The high rates of self-harm in primary care and for young men in EDs highlight these as important settings for intervention.

PMID: 31611193 [PubMed - indexed for MEDLINE]

IDP-Seq2Seq: Identification of Intrinsically Disordered Regions based on Sequence to Sequence Learning.

Bioinformatics. 2020 Jul 23;:

Authors: Tang YJ, Pang YH, Liu B

Abstract
MOTIVATION: Related to many important biological functions, intrinsically disordered regions (IDRs) are widely distributed in proteins. Accurate prediction of intrinsically disordered regions is critical for the protein structure and function analysis. However, the existing computational methods construct the predictive models solely in the sequence space, failing to convert the sequence space into the "semantic space" to reflect the structure characteristics of proteins. Furthermore, although the length-dependent predictors showed promising results, new fusion strategies should be explored to improve their predictive performance and the generalization.
RESULTS: In this study, we applied the Sequence to Sequence Learning (Seq2Seq) derived from natural language processing (NLP) to map protein sequences to "semantic space" to reflect the structure patterns with the help of predicted Residue-Residue Contacts (CCMs) and other sequence-based features. Furthermore, the Attention mechanism was employed to capture the global associations between all residue pairs in the proteins. Three length-dependent predictors were constructed: IDP-Seq2Seq-L for long disordered region prediction, IDP-Seq2Seq-S for short disordered region prediction, and IDP-Seq2Seq-G for both long and short disordered region prediction. Finally, these three predictors were fused into one predictor called IDP-Seq2Seq to improve the discriminative power and generalization. Experimental results on four independent test datasets and the CASP test dataset showed that IDP-Seq2Seq is insensitive with the ratios of long and short disordered regions and outperforms other competing methods.
AVAILABILITY: For the convenience of most experimental scientists, a user-friendly and publicly accessible web-server for the powerful new predictor has been established at http://bliulab.net/IDP-Seq2Seq/. It is anticipated that IDP-Seq2Seq will become a very useful tool for identification of intrinsically disordered regions.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID: 32702119 [PubMed - as supplied by publisher]

Dengue during pregnancy and live birth outcomes: a cohort of linked data from Brazil.

BMJ Open. 2019 07 24;9(7):e023529

Authors: Paixão ES, Campbell OM, Teixeira MG, Costa MC, Harron K, Barreto ML, Leal MB, Almeida MF, Rodrigues LC

Abstract
OBJECTIVES: Dengue is the most common viral mosquito-borne disease, and women of reproductive age who live in or travel to endemic areas are at risk. Little is known about the effects of dengue during pregnancy on birth outcomes. The objective of this study is to examine the effect of maternal dengue severity on live birth outcomes.
DESIGN AND SETTING: We conducted a population-based cohort study using routinely collected Brazilian data from 2006 to 2012.
PARTICIPATING: We linked birth registration records and dengue registration records to identify women with and without dengue during pregnancy. Using multinomial logistic regression and Firth method, we estimated risk and ORs for preterm birth (<37 weeks' gestation), low birth weight (<2500 g) and small for gestational age (<10thcentile). We also investigated the effect of time between the onset of the disease and each outcome.
RESULTS: We included 16 738 000 live births. Dengue haemorrhagic fever was associated with preterm birth (OR=2.4; 95% CI 1.3 to 4.4) and low birth weight (OR=2.1; 95% CI 1.1 to 4.0), but there was no evidence of effect for small for gestational age (OR=2.1; 95% CI 0.4 to 12.2). The magnitude of the effects was higher in the acute disease period.
CONCLUSION: This study showed an increased risk of adverse birth outcomes in women with severe dengue during pregnancy. Medical intervention to mitigate maternal risk during severe acute dengue episodes may improve outcomes for infants born to exposed mothers.

PMID: 31345962 [PubMed - indexed for MEDLINE]

A knowledge-based system to find over-the-counter medicines for self-medication.

J Biomed Inform. 2020 Jul 13;:103504

Authors: Sung HY, Chi YL

Abstract
This study developed a medicine query system based on Semantic Web and open data especially for self-medication users to search over-the-counter (OTC) medicines. Most existing medicine query systems are based on keyword searches. If users are uncertain about the exact search words, these query systems do not offer effective help. Furthermore, most systems provide inadequate explanations of symptoms and ailments for users to use with confidence. To remedy these issues, this study builds a knowledge base to enable inference-based searches and data mashup for integrating information from across the Web. Three components were identified: (1) building an ontology model to describe the relationships between ailments and symptoms; (2) upgrading medicinal product datasets to link them with the ontology model on a semantic level; and (3) developing a data mashup to integrate web resources to help users to find references. Furthermore, the aim was to develop a web-based application that utilizes inference mechanisms to provide users with tools for interactive manipulation. A pilot experiment for skin ailments was implemented to learn the problem-solving skills of the system. Finally, two experts utilized a content validity index to rate a four-dimension 15-item scale. The evaluation results show that experts found the proposed system excellent for content validity.

PMID: 32673790 [PubMed - as supplied by publisher]