BACKGROUND: Road traffic injuries (RTIs) are a growing but neglected global health crisis, requiring effective prevention to promote sustainable safety. Low- and middle-income countries (LMICs) share a disproportionately high burden with 90% of the world's road traffic deaths, and where RTIs are escalating due to rapid urbanization and motorization. Although several studies have assessed the effectiveness of a specific intervention, no systematic reviews have been conducted summarizing the effectiveness of RTI prevention initiatives specifically performed in LMIC settings; this study will help fill this gap. METHODS: In accordance with PRISMA guidelines we searched the electronic databases MEDLINE, EMBASE, Scopus, Web of Science, TRID, Lilacs, Scielo and Global Health. Articles were eligible if they considered RTI prevention in LMICs by evaluating a prevention-related intervention with outcome measures of crash, RTI, or death. In addition, a reference and citation analysis was conducted as well as a data quality assessment. A qualitative metasummary approach was used for data analysis and effect sizes were calculated to quantify the magnitude of emerging themes. RESULTS: Of the 8560 articles from the literature search, 18 articles from 11 LMICs fit the eligibility and inclusion criteria. Of these studies, four were from Sub-Saharan Africa, ten from Latin America and the Caribbean, one from the Middle East, and three from Asia. Half of the studies focused specifically on legislation, while the others focused on speed control measures, educational interventions, enforcement, road improvement, community programs, or a multifaceted intervention. CONCLUSION: Legislation was the most common intervention evaluated with the best outcomes when combined with strong enforcement initiatives or as part of a multifaceted approach. Because speed control is crucial to crash and injury prevention, road improvement interventions in LMIC settings should carefully consider how the impact of improvements will affect speed and traffic flow. Further road traffic injury prevention interventions should be performed in LMICs with patient-centered outcomes in order to guide injury prevention in these complex settings.

BACKGROUND: Clinical Natural Language Processing (NLP) systems require a semantic schema comprised of domain-specific concepts, their lexical variants, and associated modifiers to accurately extract information from clinical texts. An NLP system leverages this schema to structure concepts and extract meaning from the free texts. In the clinical domain, creating a semantic schema typically requires input from both a domain expert, such as a clinician, and an NLP expert who will represent clinical concepts created from the clinician's domain expertise into a computable format usable by an NLP system. The goal of this work is to develop a web-based tool, Knowledge Author, that bridges the gap between the clinical domain expert and the NLP system development by facilitating the development of domain content represented in a semantic schema for extracting information from clinical free-text. RESULTS: Knowledge Author is a web-based, recommendation system that supports users in developing domain content necessary for clinical NLP applications. Knowledge Author's schematic model leverages a set of semantic types derived from the Secondary Use Clinical Element Models and the Common Type System to allow the user to quickly create and modify domain-related concepts. Features such as collaborative development and providing domain content suggestions through the mapping of concepts to the Unified Medical Language System Metathesaurus database further supports the domain content creation process. Two proof of concept studies were performed to evaluate the system's performance. The first study evaluated Knowledge Author's flexibility to create a broad range of concepts. A dataset of 115 concepts was created of which 87 (76 %) were able to be created using Knowledge Author. The second study evaluated the effectiveness of Knowledge Author's output in an NLP system by extracting concepts and associated modifiers representing a clinical element, carotid stenosis, from 34 clinical free-text radiology reports using Knowledge Author and an NLP system, pyConText. Knowledge Author's domain content produced high recall for concepts (targeted findings: 86 %) and varied recall for modifiers (certainty: 91 % sidedness: 80 %, neurovascular anatomy: 46 %). CONCLUSION: Knowledge Author can support clinical domain content development for information extraction by supporting semantic schema creation by domain experts.

BACKGROUND: In the United States, 795,000 people suffer strokes each year; 10-15 % of these strokes can be attributed to stenosis caused by plaque in the carotid artery, a major stroke phenotype risk factor. Studies comparing treatments for the management of asymptomatic carotid stenosis are challenging for at least two reasons: 1) administrative billing codes (i.e., Current Procedural Terminology (CPT) codes) that identify carotid images do not denote which neurovascular arteries are affected and 2) the majority of the image reports are negative for carotid stenosis. Studies that rely on manual chart abstraction can be labor-intensive, expensive, and time-consuming. Natural Language Processing (NLP) can expedite the process of manual chart abstraction by automatically filtering reports with no/insignificant carotid stenosis findings and flagging reports with significant carotid stenosis findings; thus, potentially reducing effort, costs, and time. METHODS: In this pilot study, we conducted an information content analysis of carotid stenosis mentions in terms of their report location (Sections), report formats (structures) and linguistic descriptions (expressions) from Veteran Health Administration free-text reports. We assessed an NLP algorithm, pyConText's, ability to discern reports with significant carotid stenosis findings from reports with no/insignificant carotid stenosis findings given these three document composition factors for two report types: radiology (RAD) and text integration utility (TIU) notes. RESULTS: We observed that most carotid mentions are recorded in prose using categorical expressions, within the Findings and Impression sections for RAD reports and within neither of these designated sections for TIU notes. For RAD reports, pyConText performed with high sensitivity (88 %), specificity (84 %), and negative predictive value (95 %) and reasonable positive predictive value (70 %). For TIU notes, pyConText performed with high specificity (87 %) and negative predictive value (92 %), reasonable sensitivity (73 %), and moderate positive predictive value (58 %). pyConText performed with the highest sensitivity processing the full report rather than the Findings or Impressions independently. CONCLUSION: We conclude that pyConText can reduce chart review efforts by filtering reports with no/insignificant carotid stenosis findings and flagging reports with significant carotid stenosis findings from the Veteran Health Administration electronic health record, and hence has utility for expediting a comparative effectiveness study of treatment strategies for stroke prevention.

BACKGROUND: Natural Language Processing (NLP) systems can be used for specific Information Extraction (IE) tasks such as extracting phenotypic data from the electronic medical record (EMR). These data are useful for translational research and are often found only in free text clinical notes. A key required step for IE is the manual annotation of clinical corpora and the creation of a reference standard for (1) training and validation tasks and (2) to focus and clarify NLP system requirements. These tasks are time consuming, expensive, and require considerable effort on the part of human reviewers. METHODS: Using a set of clinical documents from the VA EMR for a particular use case of interest we identify specific challenges and present several opportunities for annotation tasks. We demonstrate specific methods using an open source annotation tool, a customized annotation schema, and a corpus of clinical documents for patients known to have a diagnosis of Inflammatory Bowel Disease (IBD). We report clinician annotator agreement at the document, concept, and concept attribute level. We estimate concept yield in terms of annotated concepts within specific note sections and document types. RESULTS: Annotator agreement at the document level for documents that contained concepts of interest for IBD using estimated Kappa statistic (95% CI) was very high at 0.87 (0.82, 0.93). At the concept level, F-measure ranged from 0.61 to 0.83. However, agreement varied greatly at the specific concept attribute level. For this particular use case (IBD), clinical documents producing the highest concept yield per document included GI clinic notes and primary care notes. Within the various types of notes, the highest concept yield was in sections representing patient assessment and history of presenting illness. Ancillary service documents and family history and plan note sections produced the lowest concept yield. CONCLUSION: Challenges include defining and building appropriate annotation schemas, adequately training clinician annotators, and determining the appropriate level of information to be annotated. Opportunities include narrowing the focus of information extraction to use case specific note types and sections, especially in cases where NLP systems will be used to extract information from large repositories of electronic clinical note documents.

OBJECTIVE: We present a narrative review of recent work on the utilisation of Natural Language Processing (NLP) for the analysis of social media (including online health communities) specifically for public health applications. METHODS: We conducted a literature review of NLP research that utilised social media or online consumer-generated text for public health applications, focussing on the years 2016 to 2018. Papers were identified in several ways, including PubMed searches and the inspection of recent conference proceedings from the Association of Computational Linguistics (ACL), the Conference on Human Factors in Computing Systems (CHI), and the International AAAI (Association for the Advancement of Artificial Intelligence) Conference on Web and Social Media (ICWSM). Popular data sources included Twitter, Reddit, various online health communities, and Facebook. RESULTS: In the recent past, communicable diseases (e.g., influenza, dengue) have been the focus of much social media-based NLP health research. However, mental health and substance use and abuse (including the use of tobacco, alcohol, marijuana, and opioids) have been the subject of an increasing volume of research in the 2016 - 2018 period. Associated with this trend, the use of lexicon-based methods remains popular given the availability of psychologically validated lexical resources suitable for mental health and substance abuse research. Finally, we found that in the period under review "modern" machine learning methods (i.e. deep neural-network-based methods), while increasing in popularity, remain less widely used than "classical" machine learning methods.

Objective: To describe a framework for leveraging big data for research and quality improvement purposes and demonstrate implementation of the framework for design of the Department of Veterans Affairs (VA) Colonoscopy Collaborative. Methods: We propose that research utilizing large-scale electronic health records (EHRs) can be approached in a 4 step framework: 1) Identify data sources required to answer research question; 2) Determine whether variables are available as structured or free-text data; 3) Utilize a rigorous approach to refine variables and assess data quality; 4) Create the analytic dataset and perform analyses. We describe implementation of the framework as part of the VA Colonoscopy Collaborative, which aims to leverage big data to 1) prospectively measure and report colonoscopy quality and 2) develop and validate a risk prediction model for colorectal cancer (CRC) and high-risk polyps. Results: Examples of implementation of the 4 step framework are provided. To date, we have identified 2,337,171 Veterans who have undergone colonoscopy between 1999 and 2014. Median age was 62 years, and 4.6 percent (n = 106,860) were female. We estimated that 2.6 percent (n = 60,517) had CRC diagnosed at baseline. An additional 1 percent (n = 24,483) had a new ICD-9 code-based diagnosis of CRC on follow up. Conclusion: We hope our framework may contribute to the dialogue on best practices to ensure high quality epidemiologic and quality improvement work. As a result of implementation of the framework, the VA Colonoscopy Collaborative holds great promise for 1) quantifying and providing novel understandings of colonoscopy outcomes, and 2) building a robust approach for nationwide VA colonoscopy quality reporting.

The Health Insurance Portability and Accountability Act (HIPAA) Safe Harbor method requires removal of 18 types of protected health information (PHI) from clinical documents to be considered "de-identified" prior to use for research purposes. Human review of PHI elements from a large corpus of clinical documents can be tedious and error-prone. Indeed, multiple annotators may be required to consistently redact information that represents each PHI class. Automated de-identification has the potential to improve annotation quality and reduce annotation time. For instance, using machine-assisted annotation by combining de-identification system outputs used as pre-annotations and an interactive annotation interface to provide annotators with PHI annotations for "curation" rather than manual annotation from "scratch" on raw clinical documents. In order to assess whether machine-assisted annotation improves the reliability and accuracy of the reference standard quality and reduces annotation effort, we conducted an annotation experiment. In this annotation study, we assessed the generalizability of the VA Consortium for Healthcare Informatics Research (CHIR) annotation schema and guidelines applied to a corpus of publicly available clinical documents called MTSamples. Specifically, our goals were to (1) characterize a heterogeneous corpus of clinical documents manually annotated for risk-ranked PHI and other annotation types (clinical eponyms and person relations), (2) evaluate how well annotators apply the CHIR schema to the heterogeneous corpus, (3) compare whether machine-assisted annotation (experiment) improves annotation quality and reduces annotation time compared to manual annotation (control), and (4) assess the change in quality of reference standard coverage with each added annotator's annotations.

The mammalian target of rapamycin (mTOR) signalling pathway is a central regulator of metabolism in all cells. It senses intracellular and extracellular signals and nutrient levels, and coordinates the metabolic requirements for cell growth, survival, and proliferation. Genetic alterations that deregulate mTOR signalling lead to metabolic reprogramming, resulting in the development of several cancers including those of the head and neck. Gain-of-function mutations in EGFR, PIK3CA, and HRAS, or loss-of-function in p53 and PTEN are often associated with mTOR hyperactivation, whereas mutations identified from The Cancer Genome Atlas (TCGA) dataset that potentially lead to aberrant mTOR signalling are found in the EIF4G1, PLD1, RAC1, and SZT2 genes. In this review, we discuss how these mutant genes could affect mTOR signalling and highlight their impact on metabolic processes, as well as suggest potential targets for therapeutic intervention, primarily in head and neck cancer.

Cancer has long been considered as a heterogeneous population of uncontrolled proliferation of different transformed cell types. The recent findings concerning tumorigeneses have highlighted the fact that tumors can progress through tight relationships among tumor cells, cellular, and non-cellular components which are present within tumor tissues. In recent years, studies have shown that mesenchymal stem cells (MSCs) are essential components of non-tumor cells within the tumor tissues that can strongly affect tumor development. Several forms of MSCs have been identified within tumor stroma. Naïve (innate) mesenchymal stem cells (N-MSCs) derived from different sources are mostly recruited into the tumor stroma. N-MSCs exert dual and divergent effects on tumor growth through different conditions and factors such as toll-like receptor priming (TLR-priming), which is the primary underlying causes of opposite effects. Moreover, MSCs also have the contrary effects by various molecular mechanisms relying on direct cellto- cell connections and indirect communications through the autocrine, paracrine routes, and tumor microenvironment (TME). Overall, cell-based therapies will hold great promise to provide novel anticancer treatments. However, the application of intact MSCs in cancer treatment can theoretically cause adverse clinical outcomes. It is essential that to extensively analysis the effective factors and conditions in which underlying mechanisms are adopted by MSCs when encounter with cancer. The aim is to review the cellular and molecular mechanisms underlying the dual effects of MSCs followed by the importance of polarization of MSCs through priming of TLRs.

Background: Cardiometabolic diseases are the leading cause of death and disability in many low- and middle-income countries. As the already severe burden from these conditions continues to increase in low- and middle-income countries, cardiometabolic diseases introduce new and salient public health challenges to primary health care systems. In this mixed-method study, we aim to assess the capacity of grassroots primary health care facilities to deliver essential services for the prevention and control of cardiometabolic diseases. Built on this information, our goal is to propose evidence-based recommendations to promote a stronger primary health care system in resource-limited settings. Methods: The study will be conducted in resource-limited settings in China, Kenya, Nepal, and Vietnam using a mixed-method approach that incorporates a literature review, surveys, and in-depth interviews. The literature, statistics, and document review will extract secondary data on the burden of cardiometabolic diseases in each country, the existing policies and interventions related to strengthening primary health care services, and improving care related to non-communicable disease prevention and control. We will also conduct primary data collection. In each country, ten grassroots primary health care facilities across representative urban-rural regions will be selected. Health care professionals and patients recruited from these facilities will be invited to participate in the facility assessment questionnaire and patients' survey. Stakeholders - including patients, health care professionals, policymakers at the local, regional, and national levels, and local authorities - will be invited to participate in in-depth interviews. A standard protocol will be designed to allow for adaption and localization in data collection instruments and procedures within each country. Discussion: With a special focus on the capacity of primary health care facilities in resource-limited settings in low- and middle-income countries, this study has the potential to add new evidence for policymakers and academia by identifying the most common and significant barriers primary health care services face in managing and preventing cardiometabolic diseases. With these findings, we will generate evidence-based recommendations on potential strategies that are feasible for resource-limited settings in combating the increasing challenges of cardiometabolic diseases.

Ventricular standstill (VS) is a potentially fatal arrhythmia that is usually associated with syncope, if prolonged and is rarely asymptomatic[1]. Its mechanism involves either a lack of supraventricular impulse or an interruption in the transmission of these signals from the atria to the ventricles, resulting in a sudden loss of cardiac output[2]. Although rare, ventricular arrhythmias have been associated with intravenous (IV) erythromycin. However, to our knowledge, VS has not been reported following the administration of IV erythromycin. The Authors describe a rare case of asymptomatic VS and subsequent third-degree atrioventricular block, following the administration of IV erythromycin in a 49-year-old woman with borderline hypokalemia. Through this case, the Authors highlight the importance of cardiac monitoring and electrolyte replacement when administering IV erythromycin, as well as discuss several other mechanisms that contribute to ventricular arrhythmias. LEARNING POINTS: Intravenous erythromycin is associated with prolongation of the QTc interval and ventricular arrhythmias.Ventricular standstill is a rare but potentially fatal arrhythmia, and may have an association with the administration of intravenous erythromycin.Cardiac monitoring in patients with baseline QTc prolongation and correction of electrolyte disturbances are important when administering intravenous erythromycin.