Search

Background: The emergence of the artificial intelligence chatbot ChatGPT in November 2022 has garnered substantial attention across diverse disciplines. Despite widespread adoption in various sectors, the exploration of its application in libraries, especially within the medical domain, remains limited., Aims/objectives: Many areas of interest remain unexplored like ChatGPT in medical libraries and this review aims to synthesise what is currently known about it to identify gaps and stimulate further research., Methods: Employing Cooper's integrative review method, this study involves a comprehensive analysis of existing literature on ChatGPT and its potential implementations within library contexts., Results: A systematic literature search across various databases yielded 166 papers, with 30 excluded for irrelevance. After abstract reviews and methodological assessments, 136 articles were selected. Critical Appraisal Skills Programme qualitative checklist further narrowed down to 29 papers, forming the basis for the present study. The literature analysis reveals diverse applications of ChatGPT in medical libraries, including aiding users in finding relevant medical information, answering queries, providing recommendations and facilitating access to resources. Potential challenges and ethical considerations associated with ChatGPT in this context are also highlighted., Conclusion: Positioned as a review, our study elucidates the applications of ChatGPT in medical libraries and discusses relevant considerations. The integration of ChatGPT into medical library services holds promise for enhancing information retrieval and user experience, benefiting library users and the broader medical community., (© 2024 Health Libraries Group.)

Synthetic DNA is the linchpin of the rapidly accelerating biotechnological era and is perhaps the most promising candidate for long-term digital data storage. Despite huge advances, manufacturing error-free DNA at low cost and high throughput remains challenging. Borrowing from well-established sequencing-by-synthesis technologies, we describe a new solution for DNA error correction., Competing Interests: Declaration of Interests Material discussed here is subject of a patent application. F.H. is a shareholder in Camena Bioscience., (Copyright © 2021. Published by Elsevier Ltd.)

The COVID-19 pandemic has led to delays in cancer diagnosis, in part due to postponement of cancer screening. We used Google Trends data to assess public attention to cancer screening during the first peak of the COVID-19 pandemic. Search volume for terms related to established cancer screening tests ("colonoscopy," "mammogram," "lung cancer screening," and "pap smear") showed a marked decrease of up to 76% compared to the pre-pandemic period, a significantly greater drop than for search volume for terms denoting common chronic diseases. Maintaining awareness of cancer screening during future public health crises may decrease delays in cancer diagnosis.

We researchers have taken searching for information for granted for far too long. The COVID-19 pandemic shows us the boundaries of academic searching capabilities, both in terms of our know-how and of the systems we have. With hundreds of studies published daily on COVID-19, for example, we struggle to find, stay up-to-date, and synthesize information-all hampering evidence-informed decision making. This COVID-19 information crisis is indicative of the broader problem of information overloaded academic research. To improve our finding capabilities, we urgently need to improve how we search and the systems we use. We respond to Klopfenstein and Dampier (Res Syn Meth. 2020) who commented on our 2020 paper and proposed a way of improving PubMed's and Google Scholar's search functionalities. Our response puts their commentary in a larger frame and suggests how we can improve academic searching altogether. We urge that researchers need to understand that search skills require dedicated education and training. Better and more efficient searching requires an initial understanding of the different goals that define the way searching needs to be conducted. We explain the main types of searching that we academics routinely engage in; distinguishing lookup, exploratory, and systematic searching. These three types must be conducted using different search methods (heuristics) and using search systems with specific capabilities. To improve academic searching, we introduce the "Search Triangle" model emphasizing the importance of matching goals, heuristics, and systems. Further, we suggest an urgently needed agenda toward search literacy as the norm in academic research and fit-for-purpose search systems., (© 2020 The Authors. Research Synthesis Methods published by John Wiley & Sons Ltd.)

Information retrieval among different modalities becomes a significant issue with many promising applications. However, inconsistent feature representation of various multimedia data causes the "heterogeneity gap" among various modalities, which is a challenge in cross-modal retrieval. For bridging the "heterogeneity gap," the popular methods attempt to project the original data into a common representation space, which needs great fitting ability of the model. To address the above issue, we propose a novel Graph Representation Learning (GRL) method for bridging the heterogeneity gap, which does not project the original feature into an aligned representation space but adopts a cross-modal graph to link different modalities. The GRL approach consists of two subnetworks, Feature Transfer Learning Network (FTLN) and Graph Representation Learning Network (GRLN). Firstly, FTLN model finds a latent space for each modality, where the cosine similarity is suitable to describe their similarity. Then, we build a cross-modal graph to reconstruct the original data and their relationships. Finally, we abandon the features in the latent space and turn into embedding the graph vertexes into a common representation space directly. During the process, the proposed Graph Representation Learning method bypasses the most challenging issue by utilizing a cross-modal graph as a bridge to link the "heterogeneity gap" among different modalities. This attempt utilizes a cross-modal graph as an intermediary agent to bridge the "heterogeneity gap" in cross-modal retrieval, which is simple but effective. Extensive experiment results on six widely-used datasets indicate that the proposed GRL outperforms other state-of-the-art cross-modal retrieval methods., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

The purpose of the study is to solve problems, i.e., increasingly significant processing delay of massive monitoring data and imbalanced tasks in the scheduling and monitoring center for a railway network. To tackle these problems, a method by using a smooth weighted round-robin scheduling based on backpressure flow control (BF-SWRR) is proposed. The method is developed based on a model for message queues and real-time streaming computing. By using telemetry data flow as input data sources, the fields of data sources are segmented into different sets by using a distributed model of stream computing parallel processing. Moreover, the round-robin (RR) scheduling method for the distributed server is improved. The parallelism, memory occupancy, and system delay are tested by taking a high-speed train section of a certain line as an example. The result showed that the BF-SWRR method for clusters can control the delay to within 1 s. When the parallelism of distributed clusters is set to 8, occupancy rates of the CPU and memory can be decreased by about 15%. In this way, the overall load of the cluster during stream computing is more balanced., Competing Interests: The authors have declared that no competing interests exist.

Background: An excessive overload of information causes an ineffective management of information, stress and indefiniteness. Furthermore, this situation can prevent persons from learning and making conscious decisions., Objective: This study aims to determine the cancer information overload (CIO) and the factors related to it in adults who are Internet users., Methods: A cross-sectional study with 482 Internet users was conducted. The data were collected by using an Introductory Information Form and the Cancer Information Overload Scale., Results: It was found that the Internet was the most used information source (62.2%). The CIO of those with a university level education was found to be high (P = 0.012). It was found that the CIO of individuals who used the Internet (P = 0.031) and newspapers/magazines (P = 0.004) as sources of information was high compared with those who did not use these sources. It was determined from the information obtained that those who found the information to be beneficial and enough had a low CIO (P = 0.004, P = 0.00)., Conclusion: Health literacy around cancer information is challenging for frequent Internet users. Health professionals, information specialists and librarians should orient people to reliable sources., (© 2020 Health Libraries Group.)

Access to medical data is central for conducting research on genomics. However, to tap these metadata (observable traits and phenotypes, diagnoses and medication, and labels), researchers must grapple with the complex and sensitive nature of the information. In this Perspective, we argue that, at this exciting time for genomics and artificial intelligence, several critical aspects of data generation, infrastructure and management are pillars of a modern data ecosystem. Many risks to privacy and many obstacles to medical research can be eliminated or mitigated by new secure data analytics. Finally, we discuss the potential consequences of medical data exiting the institutions and being managed by individuals. These shifts in data ownership have the potential for profound disruption and opportunity across many fields.

Linking online identities of users among countless heterogeneous network services on the Internet can provide an explicit digital representation of users, which can benefit both research and industry. In recent years, user identity linkage (UIL) through the Internet has become an emerging task with great potential and many challenges. Existing works mainly focus on online social networks that consider inconsistent profiles, content, and networks as features or use sparse location-based data sets to link the online behaviors of a real person. To extend the UIL problem to a general scenario, we try to link the web-browsing behaviors of users, which can help to distinguish specific users from others, such as children or malicious users. More specifically, we propose a Siamese neural network (NN) architecture-based UIL (SAUIL) model that learns and compares the highest-level feature representation of input web-browsing behaviors with deep NNs. Although the number of matching and nonmatching pairs for the UIL problem is highly imbalanced, previous studies have not considered imbalanced UIL data sets. Therefore, we further address the imbalanced learning issue by proposing cost-sensitive SAUIL (C-SAUIL) model, which assumes higher costs for misclassifying the minority class. In the experiments, the proposed model is robust and exhibits a good performance on very large, real-world data sets collected from different regions with distinct characteristics.

DNA emerged as a novel potential material for mass data storage, offering the possibility to cheaply solve a great data storage problem. Large oligonucleotide pools demonstrated high potential of large-scale data storage in test tube, meanwhile, living cell with high fidelity in information replication. Here we show a mixed culture of bacterial cells carrying a large oligo pool that was assembled in a high-copy-number plasmid was presented as a stable material for large-scale data storage. The underlying principle was explored by deep bioinformatic analysis. Although homology assembly showed sequence context dependent bias, the large oligonucleotide pools in the mixed culture were constant over multiple successive passages. Finally, over ten thousand distinct oligos encompassing 2304 Kbps encoding 445 KB digital data, were stored in cells, the largest storage in living cells reported so far and present a previously unreported approach for bridging the gap between in vitro and in vivo systems.

Many real-world data analyses use common data models (CDMs) to standardize terminologies for medication use, medical events and procedures, data structures, and interpretations of data to facilitate analyses across data sources. For decision makers, key aspects that influence the choice of a CDM may include (i) adaptability to a specific question; (ii) transparency to reproduce findings, assess validity, and instill confidence in findings; and (iii) ease and speed of use. Organizing CDMs preserve the original information from a data source and have maximum adaptability. Full mapping data models, or preconfigured rules systems, are easy to use, since all raw codes are mapped to medical constructs. Adaptive rule systems grow libraries of reusable measures that can easily adjust to preserve adaptability, expedite analyses, and ensure study-specific transparency., (© 2019 The Authors Clinical Pharmacology & Therapeutics © 2019 American Society for Clinical Pharmacology and Therapeutics.)

As a consequence of the epidemiological transition towards non-communicable diseases, integrated care approaches are required, not solely focused on medical purposes, but also on a range of essential activities for the maintenance of the individuals' quality of life. In order to allow the exchange of information, these integrated approaches might be supported by digital platforms, which need to provide trustful environments and to guarantee the integrity of the information exchanged. Therefore, together with mechanisms such as authentication, logging or auditing, the definition of access control policies assumes a paramount importance. This article focuses on the development of a parser as a component of a platform to support the care of community-dwelling older adults, the SOCIAL platform, to allow the definition of access control policies and rules using natural languages.

Objective: To increase knowledge of National Library of Medicine resources by using a train-the-trainer approach., Methods: Workshops were held in spring 2016 to increase knowledge of 4 National Library of Medicine tools. Data were collected before the workshop and immediately, 3 months, and 1 year after the workshop. Knowledge questions were scored as 1 point per question; an aggregated knowledge score could range from 0 to 16 points. A paired t test assessed the change in knowledge from before to after the workshop., Results: Four workshops were hosted, with a total of 74 attendees. The response rate for the surveys ranged from 50% to 100%. Knowledge scores changed significantly from 7.2 to 11.9 (t = 15, P < .001). One year after the workshop, more of the participants reported having informally trained others (56.8%) than reported providing 1 or more formal training session (8.1%)(P < .001)., Conclusion: Objective measures of knowledge and information dissemination showed that the National Library of Medicine workshop was successful and resulted in both short- and long-term gains. This workshop could be repeated with other populations to further disseminate information regarding the National Library of Medicine tools, which could help improve disaster response.

Objective: To assess the current use of big data and artificial intelligence (AI) in the field of rheumatic and musculoskeletal diseases (RMDs)., Methods: A systematic literature review was performed in PubMed MEDLINE in November 2018, with key words referring to big data, AI and RMDs. All original reports published in English were analysed. A mirror literature review was also performed outside of RMDs on the same number of articles. The number of data analysed, data sources and statistical methods used (traditional statistics, AI or both) were collected. The analysis compared findings within and beyond the field of RMDs., Results: Of 567 articles relating to RMDs, 55 met the inclusion criteria and were analysed, as well as 55 articles in other medical fields. The mean number of data points was 746 million (range 2000-5 billion) in RMDs, and 9.1 billion (range 100 000-200 billion) outside of RMDs. Data sources were varied: in RMDs, 26 (47%) were clinical, 8 (15%) biological and 16 (29%) radiological. Both traditional and AI methods were used to analyse big data (respectively, 10 (18%) and 45 (82%) in RMDs and 8 (15%) and 47 (85%) out of RMDs). Machine learning represented 97% of AI methods in RMDs and among these methods, the most represented was artificial neural network (20/44 articles in RMDs)., Conclusions: Big data sources and types are varied within the field of RMDs, and methods used to analyse big data were heterogeneous. These findings will inform a European League Against Rheumatism taskforce on big data in RMDs., Competing Interests: Competing interests: RC is an employee of Orange Healthcare, and HS is an employee of Sanoïa, a Digital CRO providing clinical research services including data science. There are no competing interests for the other authors.

Background: Stem cell injections are being offered to patients as a nonoperative treatment for osteoarthritis of the hip and knee. To our knowledge, no peer-reviewed data exist to document the usage frequency of these injections nor to quantify the public interest in these injections. We sought to use Google Trends to provide a quantitative analysis of interest in hip and knee stem cell injections at the population level., Methods: Google Trends search parameters were set to obtain query data from January 2010 through December 2017. 'Arthritis,' 'osteoarthritis,' 'stem cell,' 'injection,' 'knee,' and 'hip' were entered in various combinations to obtain the highest yield search volume. Trend analyses were performed., Results: Six linear models were generated to show trends in the volume of searches for the United States and the World. Model fit was good, and regression analysis showed significant trends over time for all searches. Use of search terms increased significantly over time (all models P < .001). Adjusted R-square values ranged from 54.4% to 78.1%. All trends showed an upward trajectory for the entirety of the study time period., Conclusion: There has been a marked and statistically significant rise in search query volume related to stem cells and osteoarthritis of the hip and knee since 2010. Online interest in stem cell injections may suggest increased utilization of these procedures. Well-designed clinical studies are required to keep pace with the rising popularity and public interest in this intervention for hip and knee arthritis., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Synthetic DNA has emerged as a novel substrate to encode computer data with the potential to be orders of magnitude denser than contemporary cutting edge techniques. However, even with the help of automated synthesis and sequencing devices, many intermediate steps still require expert laboratory technicians to execute. We have developed an automated end-to-end DNA data storage device to explore the challenges of automation within the constraints of this unique application. Our device encodes data into a DNA sequence, which is then written to a DNA oligonucleotide using a custom DNA synthesizer, pooled for liquid storage, and read using a nanopore sequencer and a novel, minimal preparation protocol. We demonstrate an automated 5-byte write, store, and read cycle with a modular design enabling expansion as new technology becomes available.

The Open Targets Platform integrates evidence from genetics, genomics, transcriptomics, drugs, animal models and scientific literature to score and rank target-disease associations for drug target identification. The associations are displayed in an intuitive user interface (https://www.targetvalidation.org), and are available through a REST-API (https://api.opentargets.io/v3/platform/docs/swagger-ui) and a bulk download (https://www.targetvalidation.org/downloads/data). In addition to target-disease associations, we also aggregate and display data at the target and disease levels to aid target prioritisation. Since our first publication two years ago, we have made eight releases, added new data sources for target-disease associations, started including causal genetic variants from non genome-wide targeted arrays, added new target and disease annotations, launched new visualisations and improved existing ones and released a new web tool for batch search of up to 200 targets. We have a new URL for the Open Targets Platform REST-API, new REST endpoints and also removed the need for authorisation for API fair use. Here, we present the latest developments of the Open Targets Platform, expanding the evidence and target-disease associations with new and improved data sources, refining data quality, enhancing website usability, and increasing our user base with our training workshops, user support, social media and bioinformatics forum engagement.

The primary goal of this project is to understand how each National Cancer Institute-designated cancer center library, and all libraries that support cancer research, function within their institutions. Through an in-depth survey focused on three major areas (staff, content and tools procurement, and user services), the research team hopes to determine how a cancer-centric library can be successful in supporting quality patient care, research excellence, and education. Additionally, the survey will examine the necessary minimum staffing levels for librarians and information professionals based on organizational size and degree of research focus. The survey will seek out the new skills librarians will need to deliver optimal services. The survey will also explore how content libraries purchase reflects and maps to constituents' current medical and research activities. Libraries within a research intense environment have a responsibility to align with researchers and health care professionals to provide resources and services that support their workflows. Cancer libraries need to be attuned to their institutions' missions, whether that includes excellent patient care, research endeavors, or cutting-edge educational programs. The information gathered from the survey will provide data for this research team to define the vision and standards of excellence for a cancer specialized research library.

Data repositories are a strategy in line with precision medicine and big data initiatives, and are an efficient way to maximize data utility and form collaborative research relationships. Nurse researchers are uniquely positioned to make a valuable contribution using this strategy. The purpose of this article is to present a review of the benefits and challenges associated with developing data repositories, and to describe the process we used to develop and maintain a data repository in HIV research. Systematic planning, data collection, synthesis, and data sharing have enabled us to conduct robust cross-sectional and longitudinal analyses with more than 200 people living with HIV. Our repository building has also led to collaboration and training, both in and out of our organization. We present a pragmatic and affordable way that nurse scientists can build and maintain a data repository, helping us continue to make to our understanding of health phenomena.

One of the most important resources for researchers of noncoding RNAs is the information available in public databases spread over the internet. However, the effective exploration of this data can represent a daunting task, given the large amount of databases available and the variety of stored data. This chapter describes a classification of databases based on information source, type of RNA, source organisms, data formats, and the mechanisms for information retrieval, detailing the relevance of each of these classifications and its usability by researchers. This classification is used to update a 2012 review, indexing now more than 229 public databases. This review will include an assessment of the new trends for ncRNA research based on the information that is being offered by the databases. Additionally, we will expand the previous analysis focusing on the usability and application of these databases in pathogen and disease research. Finally, this chapter will analyze how currently available database schemas can help the development of new and improved web resources.

Background: There are racial/ethnic disparities in breast cancer mortality may be attributed to differences in receipt of adjuvant cancer treatment. Our purpose was to determine whether the mortality disparities could be explained by racial/ethnic differences in long-term adherence to adjuvant endocrine therapy (AET)., Methods: We conducted a retrospective cohort study with the Texas Cancer Registry and Medicaid claims-linked dataset of women (20-64 years) diagnosed with local and regional breast cancer who filled a prescription for AET from 2000-2008. Adherence to AET was measured at three time points (1-, 3-, and 5-year adherence) using a value for the percentage of medication filled for each period divided by the total number of possible prescriptions prescribed (Medication Possession Ratio, MPR). We created a binary variable of adherence (MPR≥80%). We performed multivariable logistic regressions to assess racial differences for the odds of AET adherence and Cox proportional hazard models to determine the risk of mortality adjusting for potential confounding variables of SES, comorbidities, tumor prognostic factors, and other cancer treatment., Results: Of the 1,497 women with breast cancer who initiated AET, 56.9%, 42.3%, and 33.3% were adherent for 1, 3, and 5-years, respectively. Hispanics compared to non-Hispanic whites did differ in the proportion that were adherent to 5-years of AET. In the adjusted analysis for long-term adherence to AET, Hispanics did not have a significantly increased risk of death compared to non-Hispanic white patients (HR: 1.13, 95% CI: 0.58-2.21). However, black compared to non-Hispanic white patients had significantly lower odds of three-year adherence (OR: 0.45, 95% CI: 0.28-0.73). After controlling for 5-year adherence to AET, the risk of death for black compared to non-Hispanic white patients was 12% lower (HR: 1.90; 95% CI: 1.03-3.51) and in the fully adjusted model, the disparity was reduced and no longer significant (OR: 1.86, 95% CI: 0.94-3.66)., Conclusions: Long-term adherence in the Medicaid population is suboptimal and racial/ethnic differences in AET adherence may partially explain disparities in mortality. This study underscores the critical need to ensure long-term adherence to AET for all racial/ethnic groups to decrease disparities in mortality.

Clinical trial data are essential for assessments of the effectiveness of health care interventions. Information about ongoing or completed, but not yet formally published, trials has been more difficult to identify until the development of clinical trials registers and portals. This paper summarises research evidence on identifying sources of trial data, how and when to search those sources, and which future developments may enhance access to and retrieval of unpublished trial evidence. We conducted a literature search for relevant studies and provide a narrative review of the evidence from these studies. Clinical trial data can be found in resources including clinical trials registers, regulatory agency sources, health technology assessment websites and manufacturers' websites, and submissions for regulatory approval. The challenges of searching these resources are described. Trials registers are relatively unsophisticated in terms of their search interfaces, and searchers need to adapt to each individual register. There is overlap across registers, but little research on the degree and nature of overlap and how best to search. Despite these challenges, trials registers and other resources can be rich sources of additional unique trial data, which may not be available from journal reports. New initiatives, such as OpenTrials, aim to consolidate and link all structured data and documentation related to clinical trials. No single resource gives access to all trials, and multiple registers should be searched as sensitively as possible. Searching is challenging and should be adequately resourced. Information specialists should monitor new developments which may reduce the challenges over the coming years., (© 2018 John Wiley & Sons, Ltd.)

Introduction: Registration and publication of trial protocols has become increasingly important and a requirement in some sources of funding and publication. Increased access to protocols yields many potential benefits, but there are issues regarding identification of published protocols. The aim of this investigation is to compare methods of retrieval for identifying trial protocols in a systematic review., Methods: Six stages of searching (checking published trial reports, searching journal Web sites, Internet searching, trial registers, bibliographic databases, and contact with authors) were completed to identify 74 trial protocols., Results: Fifty-seven percent of the trial protocols were identified upon completion of all 6 stages of searching. The most comprehensive method was searching trial registers that identified 51% of the protocols. Contact with authors was most effective at uniquely identifying protocols; 12% were retrieved via this single method. Contact with authors was the only effective method of identifying protocols for trials pre-2005., Discussion: When attempting to identify trial protocols to include in systematic reviews, some methods are relatively quick to undertake but deliver a low yield. The most effective search strategy for most sources was retrieval by trial registration number where available., Conclusions: For protocols of trial results published pre-2005, review authors should contact authors as a priority. For protocols post-2005, they should check the trial publication for protocol details, search trial registers, and contact authors, ceasing searching once a predetermined point of diminishing returns has been reached., (© 2017 John Wiley & Sons, Ltd.)

There has been an ascending growth in the capacity of information being generated. The increased production of data in turn has put forward other challenges as well thus, and there is the need to store this information and not only to store it but also to retain it for a prolonged time period. The reliance on DNA as a dense storage medium with high storage capacity and its ability to withstand extreme environmental conditions has increased over the past few years. There have been developments in reading and writing different forms of data on DNA, codes for encrypting data and using DNA as a way of secret writing leading towards new styles like stenography and cryptography. The article outlines different methods adopted for storing digital data on DNA with pros and cons of each method that has been applied plus the advantages and limitations of using DNA as a storage medium.

Over the past decade, cloud software has transformed numerous industries-from finance to logistics, marketing to manufacturing. The simplified aggregation of data, enabled by cloud computing, empowers individuals to glean insights and make data-driven decisions rapidly. In science, however, such a transformation has yet to emerge. The domain lacks centralized, machine-readable repositories of scientific data; this absence inhibits analytics and expedient decision-making. Recently, the Internet of Things (IoT) has served as a catalyst for digitizing and automating science. IoT enables the centralized collection and analysis of scientific data (e.g., instruments, sensors, and environments). Here, we discuss this new technology trend, its applications in laboratories and promise as a platform for improved efficiency, more innovative capabilities, and machine learning/artificial intelligence.

Background: To examine the trends in incidence and socio-demographic, organizational, academic, and clinical risk markers of student alcohol intoxication associated with emergency department (ED) visits., Methods: Student admission data from 2009 to 2015 were linked to primary healthcare data and subsequent ED visits with alcohol intoxication identified using ICD-9 codes within one year following the first (index) enrollment each year. Incidence rate per 10,000 person-years was calculated. Cox proportional hazard regression provided adjusted hazard ratios (HR) (95 % CIs) for the association between student characteristics and subsequent ED visits with alcohol intoxication., Results: Of 177,128 students aged 16-49 enrolled, 889 had at least one ED visit with alcohol intoxication, resulting in an incidence rate of 59/10,000 person-years. Incidence increased linearly from 45/10,000 person-years in 2009-10 to 71/10,000 person-years in the 2014-15 academic year (p < 0.001). HRs (95%CIs) of student characteristics associated with this outcome were: males (versus females): 1.38 (1.21-1.58); below 20 years of age (versus 25-30 years): 3.36 (1.99-5.65); Hispanic (versus Asian) students: 1.61 (1.16-2.25); parental tax dependency: 1.49 (1.16-1.91); Greek life member: 1.96 (1.69-2.26); member of an athletic team: 0.51 (0.36-0.72); undergraduate (versus graduate) students: 2.65 (1.88-3.74). Past year alcohol use or having been diagnosed with depression or anxiety were also significant predictors. Adjustments for campus-related factors strongly attenuated the associations between student socio-demographic characteristics with this outcome., Conclusions: Linking student admission data with ED clinical data can help monitor student alcohol intoxication associated with ED visits and identify student groups at higher risk who subsequently can be targeted for intervention efforts., (Copyright © 2018 Elsevier B.V. All rights reserved.)

"-Omics" systems sciences are at the epicenter of personalized medicine and public health, and drivers of knowledge-based biotechnology innovation. Bioinformatics, a core component of omics research, is one of the disciplines that first employed Free/Libre Open Source Software (FLOSS), and thus provided a fertile ground for its further development. Understanding the use and characteristics of FLOSS deployed in the omics field is valuable for future innovation strategies, policy and funding priorities. We conducted a bibliometric, longitudinal study of the use of FLOSS in sepsis omics research from 2011 to 2015 in the United States, EU-28 and China. Because sepsis is an interdisciplinary field at the intersection of multiple omics technologies and medical specialties, it was chosen as a model innovation ecosystem for this empirical analysis, which used publicly available data. Despite development of and competition from proprietary commercial software, scholars in omics continue to employ FLOSS routinely, and independent of the type of omics technology they work with. The number of articles using FLOSS increased significantly over time in the EU-28, as opposed to the United States and China (R = 0.96, p = 0.004). Furthermore, in an era where sharing of knowledge is being strongly advocated and promoted by public agencies and social institutions, we discuss possible correlations between the use of FLOSS and various funding sources in omics research. These observations and analyses provide new insights into the use of FLOSS in sepsis omics research across three (supra)national regions. Further benchmarking studies are warranted for FLOSS trends in other omics fields and geographical settings. These could, in time, lead to the development of new composite innovation and technology use metrics in omics systems sciences and bioinformatics communities.

Metabolites are small molecules produced by enzymatic reactions in a given organism. Metabolomics or metabolic phenotyping is a well-established omics aimed at comprehensively assessing metabolites in biological systems. These comprehensive analyses use analytical platforms, mainly nuclear magnetic resonance spectroscopy and mass spectrometry, along with associated separation methods to gather qualitative and quantitative data. Metabolomics holistically evaluates biological systems in an unbiased, data-driven approach that may ultimately support generation of hypotheses. The approach inherently allows the molecular characterization of a biological sample with regard to both internal (genetics) and environmental (exosome, microbiome) influences. Metabolomics workflows are based on whether the investigator knows a priori what kind of metabolites to assess. Thus, a targeted metabolomics approach is defined as a quantitative analysis (absolute concentrations are determined) or a semiquantitative analysis (relative intensities are determined) of a set of metabolites that are possibly linked to common chemical classes or a selected metabolic pathway. An untargeted metabolomics approach is a semiquantitative analysis of the largest possible number of metabolites contained in a biological sample. This is part I of a review intending to give an overview of the state of the art of major metabolic phenotyping technologies. Furthermore, their inherent analytical advantages and limits regarding experimental design, sample handling, standardization and workflow challenges are discussed.

This work reports the second part of a review intending to give the state of the art of major metabolic phenotyping strategies. It particularly deals with inherent advantages and limits regarding data analysis issues and biological information retrieval tools along with translational challenges. This Part starts with introducing the main data preprocessing strategies of the different metabolomics data. Then, it describes the main data analysis techniques including univariate and multivariate aspects. It also addresses the challenges related to metabolite annotation and characterization. Finally, functional analysis including pathway and network strategies are discussed. The last section of this review is devoted to practical considerations and current challenges and pathways to bring metabolomics into clinical environments.

Healthcare is considered as one of the most promising areas where big data can be applied to foster innovation for the benefit of patients and of the whole system. Healthcare analytics have the potential to accelerate R&D, increase knowledge on diseases and risk factors, improve treatments, develop personalised medicine, help physicians with decision support systems… The access to data is also a driving force for patients' empowerment and for the democratic debate. However, there are also concerns about the societal, economic and ethic impacts of this wave of digitization and of the growing use of data, algorithms and artificial intelligence. Given the issues at stake, collecting and analysing data generated by health care systems is a strategic challenge in all countries; in that respect the French National System of Health Data (a national data warehouse linking data from several sources and giving a vision of the care pathways for the entire population, with a ten-year history) is an asset, but it has to be completed and enriched with data from electronic health records., (© 2018 médecine/sciences – Inserm.)

Objective: To evaluate the impact of assisted reproductive technology (ART) on the offspring of Chinese population., Design: Retrospective, data-linkage cohort., Setting: Not applicable., Patient(s): Live births resulting from ART or natural conception., Intervention(s): None., Main Outcome Measure(s): Birth defects coded according to ICD-10., Result(s): Births after ART were more likely to be female and multiple births, especially after intracytoplasmic sperm injection (ICSI). ART was associated with a significantly increased risk of birth defects, especially, among singleton births, a significantly increased risk in fresh-embryo cycles after in vitro fertilization (IVF) and frozen-embryo cycles after ICSI. Associations between ART and multiple defects, between ART and gastrointestinal malformation, genital organs malformation, and musculoskeletal malformation among singleton births, and between ART and cardiac septa malformation among multiple births were observed., Conclusion(s): This study suggests that ART increases the risk of birth defects. Subgroup analyses indicate higher risk for both fresh and frozen embryos, although nonsignificantly for frozen embryos after IVF and for fresh embryos were presented with low power. Larger sample size research is needed to clarify effects from fresh- or frozen-embryo cycles after IVF and ICSI., (Copyright © 2018 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.)

As data volume explodes, nurse scientists grapple with ways to adapt to the big data movement without jeopardizing its epistemic values and theoretical focus that celebrate while acknowledging the authority and unity of its body of knowledge. In this article, the authors describe big data and emphasize ways that nursing science brings value to its study. Collective nursing voices that call for more nursing engagement in the big data era are answered with ways to adapt and integrate theoretical and domain expertise from nursing into data science.

Background: Use of the internet as an information search tool has increased dramatically. Our study assessed preoperative use of the internet by patients to search for information regarding anaesthesia, surgery, pain or outcomes., Objective(s): The aim of this study was to test whether patients used the internet prior to surgery and what kinds of information they looked for (anaesthetic technique, pain, adverse events, outcomes and surgery). Correlation between patient age and information sought about surgery from the internet was also explored., Design: A prospective multicentre observational study., Setting: In total, 14 French private and public institutions from May 2015 to January 2016., Patients: In total, 3161 adult patients scheduled for elective surgery under regional or general anaesthesia., Intervention(s): An anonymous questionnaire was presented to adult patients scheduled for elective surgery under regional or general anaesthesia for completion before the first meeting with the anaesthesiologist. The investigator at each centre completed specific items that the patient could not complete., Main Outcome Measures: We defined the primary endpoint as the number of patients who searched for information about their anaesthesia or surgery on the internet by the time of the their preanaesthetic consultation., Results: Of the 3234 questionnaires distributed, responses were received from 3161 patients. Within this respondent sample, 1304 (45%) were professionally active and 1664 (59%) used the internet at least once per day. Among 3098 (98%) patients who answered the question concerning the primary endpoint, 1506 (48%) had searched the internet for information about their health. In total, 784 (25%) used the internet to find information about their surgery and 113 (3.5%) looked for specific information about anaesthesia. Of the 3161, 52% reported difficulty searching for appropriate information about anaesthesia on the internet. 'Daily use of the web' [odds ratio (OR) 2.0; (95% CI: 1.65 to 2.55) P < 0.001], 'use of the web on mobile devices' [OR 1.24; (95% CI: 1.02 to 1.50) P = 0.02] and 'asking general practitioner or surgeon about information' [OR 1.35; (95% CI: 1.11 to 1.64) P = 0.002] were significantly associated with the primary endpoint., Conclusion: The internet was not widely used by patients scheduled for elective surgery to search for information about anaesthesia and surgery in our French multicentre study., Trial Registration: Clinicaltrials.gov identifier: NCT02442609.

Background: Early contact with the justice system is associated with a multitude of negative outcomes across the life course. This includes an increased risk of ongoing justice contact, social disadvantage and marginalization, and mental health and substance use issues. Children whose mothers have an alcohol use disorder may be at risk of early justice system contact, and we sought to quantify this relationship in a Western Australian cohort., Methods: This population cohort study made use of linked administrative data. Those in-scope for the study were women who had a birth recorded on the Midwives Notification System (1983 to 2007). The exposed cohort were mothers who had an alcohol-related diagnosis (ICD9/10), recorded on administrative data. This included mental and behavioral disorders which were alcohol related, diseases which could be entirely attributed to alcohol and other ICD alcohol codes. These women were considered to have an alcohol use disorder, which was a proxy for heavy drinking. The comparison cohort was frequency-matched sample with no alcohol-related diagnosis identified on administrative data sets., Results: After adjusting for potential confounders, children whose mothers had a maternal alcohol use disorder had a significantly increased odds of justice contact when compared to those whose mothers had no diagnosis (odds ratio [OR] = 1.79, 95% confidence interval [CI] = 1.60 to 1.99). Additional significant maternal factors associated with child justice contact included being Indigenous (OR = 5.14, 95% CI = 4.54 to 5.81), low maternal age, low socioeconomic status, being unmarried, and a history of a mental health problems. Significant child-level factors, which were associated with increased odds of justice contact, included being male, a mental health diagnosis, child protection contact, parity, and academic failure., Conclusions: Children who were exposed to a maternal alcohol use disorder had significantly increased odds of contact with the justice system. Additional risk was associated with being Indigenous and with markers of social disadvantage. These results suggest that prevention and early intervention services should span across agencies in an effort to reduce risk., (Copyright © 2017 The Authors. Alcoholism: Clinical & Experimental Research published by Wiley Periodicals, Inc. on behalf of Research Society on Alcoholism.)

Cross-experiment comparisons in public data compendia are challenged by unmatched conditions and technical noise. The ADAGE method, which performs unsupervised integration with denoising autoencoder neural networks, can identify biological patterns, but because ADAGE models, like many neural networks, are over-parameterized, different ADAGE models perform equally well. To enhance model robustness and better build signatures consistent with biological pathways, we developed an ensemble ADAGE (eADAGE) that integrated stable signatures across models. We applied eADAGE to a compendium of Pseudomonas aeruginosa gene expression profiling experiments performed in 78 media. eADAGE revealed a phosphate starvation response controlled by PhoB in media with moderate phosphate and predicted that a second stimulus provided by the sensor kinase, KinB, is required for this PhoB activation. We validated this relationship using both targeted and unbiased genetic approaches. eADAGE, which captures stable biological patterns, enables cross-experiment comparisons that can highlight measured but undiscovered relationships., (Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.)

Objective  To investigate whether the success rate of retrieving individual participant data (IPD) for use in IPD meta-analyses has increased over time, and to explore the characteristics associated with IPD retrieval. Design  Systematic review of published IPD meta-analyses, supplemented by a reflection of the Cochrane Epilepsy Group's 20 years' experience of requesting IPD. Data sources  Medline, CENTRAL, Scopus, Web of Science, CINAHL Plus, and PsycINFO. Eligibility criteria for study selection  IPD meta-analyses of studies of all designs and all clinical areas published in English. Results  760 IPD meta-analyses which identified studies by systematic methods that had been published between 1987 and 2015 were included. Only 188 (25%) of these IPD meta-analyses retrieved 100% of the eligible IPD for analysis, with 324 (43%) of these IPD meta-analyses retrieving 80% or more of relevant IPD. There is insufficient evidence to suggest that IPD retrieval rates have improved over time. IPD meta-analyses that included only randomised trials, had an authorship policy, included fewer eligible participants, and were conducted outside of the Cochrane Database of Systematic Reviews were associated with a high or complete IPD retrieval rate. There was no association between the source of funding of the IPD meta-analyses and IPD retrieval rate. The IPD retrieval rate of the Cochrane Epilepsy Group has declined from 83% (up to 2005) to 65% (between 2012 and 2015) and the reported reasons for lack of data availability have changed in recent years. Conclusions  IPD meta-analyses are considered to be the "gold standard" for the synthesis of data from clinical research studies; however, only 25% of published IPD meta-analyses have had access to all IPD., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.)