The Concept of System for Automated Scientific Literature Reviews Generation
Ontology highlight
ABSTRACT:
SUBMITTER: Krzhizhanovskaya V
PROVIDER: S-EPMC7304034 | biostudies-literature | 2020 May
REPOSITORIES: biostudies-literature
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Project description:Systematic reviews of scientific literature are important for mapping the existing state of research and highlighting further growth channels in a field of study, but systematic reviews are inherently tedious, time consuming, and manual in nature. In recent years, keyword co-occurrence networks (KCNs) are exploited for knowledge mapping. In a KCN, each keyword is represented as a node and each co-occurrence of a pair of words is represented as a link. The number of times that a pair of words co-occurs in multiple articles constitutes the weight of the link connecting the pair. The network constructed in this manner represents cumulative knowledge of a domain and helps to uncover meaningful knowledge components and insights based on the patterns and strength of links between keywords that appear in the literature. In this work, we propose a KCN-based approach that can be implemented prior to undertaking a systematic review to guide and accelerate the review process. The novelty of this method lies in the new metrics used for statistical analysis of a KCN that differ from those typically used for KCN analysis. The approach is demonstrated through its application to nano-related Environmental, Health, and Safety (EHS) risk literature. The KCN approach identified the knowledge components, knowledge structure, and research trends that match with those discovered through a traditional systematic review of the nanoEHS field. Because KCN-based analyses can be conducted more quickly to explore a vast amount of literature, this method can provide a knowledge map and insights prior to undertaking a rigorous traditional systematic review. This two-step approach can significantly reduce the effort and time required for a traditional systematic literature review. The proposed KCN-based pre-systematic review method is universal. It can be applied to any scientific field of study to prepare a knowledge map.
| S-EPMC5362196 | biostudies-literature
Project description:Concept-centered semantic maps were created based on a text-mining analysis of PubMed using the BiblioEngine_v2018 software. The objects ("concepts") of a semantic map can be MeSH-terms or other terms (names of proteins, diseases, chemical compounds, etc.) structured in the form of controlled vocabularies. The edges between the two objects were automatically calculated based on the index of semantic similarity, which is proportional to the number of publications related to both objects simultaneously. On the one hand, an individual semantic map created based on the already published papers allows us to trace scientific inquiry. On the other hand, a prospective analysis based on the study of PubMed search history enables us to determine the possible directions for future research.
| S-EPMC6083525 | biostudies-literature
Project description:Reviews have traditionally been based on extensive searches of the available bibliography on the topic of interest. However, this approach is frequently influenced by the authors' background, leading to possible selection bias. Artificial intelligence applied to natural language processing (NLP) is a powerful tool that can be used for systematic reviews by speeding up the process and providing more objective results, but its use in scientific literature reviews is still scarce. This manuscript addresses this challenge by developing a reproducible tool that can be used to develop objective reviews on almost every topic. This tool has been used to review the antibacterial activity of Cistus genus plant extracts as proof of concept, providing a comprehensive and objective state of the art on this topic based on the analysis of 1601 research manuscripts and 136 patents. Data were processed using a publicly available Jupyter Notebook in Google Collaboratory here. NLP, when applied to the study of antibacterial activity of Cistus plants, is able to recover the main scientific manuscripts and patents related to the topic, avoiding any biases. The NLP-assisted literature review reveals that C. creticus and C. monspeliensis are the first and second most studied Cistus species respectively. Leaves and fruits are the most commonly used plant parts and methanol, followed by butanol and water, the most widely used solvents to prepare plant extracts. Furthermore, Staphylococcus. aureus followed by Bacillus. cereus are the most studied bacterial species, which are also the most susceptible bacteria in all studied assays. This new tool aims to change the actual paradigm of the review of scientific literature to make the process more efficient, reliable, and reproducible, according to Open Science standards.
| S-EPMC9952093 | biostudies-literature
Project description:In many scientific disciplines, each new 'product' of research (method, finding, artifact, etc.) is often built upon previous findings-leading to extension and branching of scientific concepts over time. We aim to understand the evolution of scientific concepts by placing them in phylogenetic hierarchies where scientific keyphrases from a large, longitudinal academic corpora are used as a proxy of scientific concepts. These hierarchies exhibit various important properties, including power-law degree distribution, power-law component size distribution, existence of a giant component and less probability of extending an older concept. We present a generative model based on preferential attachment to simulate the graphical and temporal properties of these hierarchies which helps us understand the underlying process behind scientific concept evolution and may be useful in simulating and predicting scientific evolution.
| S-EPMC5825074 | biostudies-literature
Project description:Antioxidants are abundant in natural dietary sources, and the consumption of antioxidants has a lot of potential health benefits. However, there has been no literature analysis on this topic to evaluate its scientific impact in terms of citations. This study is aimed at identifying and analysing the antioxidant publications in the existing scientific literature. In this context, a literature search was performed with the Web of Science database. Full records and cited references of the 299,602 identified manuscripts were imported into VOSviewer for bibliometric analysis. Most of the manuscripts were published since 1991. The publications were mainly related to the categories biochemistry/molecular biology, food science technology, and pharmacology/pharmacy. These topics have been prolific since 1990 and before. Polymer science was prolific before, but its publication share declined in the recent two decades. Brazil, China, India, and South Korea have emerged as upcoming major contributors besides USA. Most prolific journals were Food Chemistry, Journal of Agricultural and Food Chemistry, Free Radical Biology and Medicine, and PLOS One. Clinical conditions with high citations included Alzheimer's disease, cancer, cardiovascular disease, and Parkinson's disease. Chemical terms and structures with high citations included alpha-tocopherol, anthocyanin, ascorbate, beta-carotene, carotenoid, curcumin, cysteine, flavonoid, flavonol, hydrogen peroxide, kaempferol, N-acetylcysteine, nitric oxide, phenolic acid, uric acid, vitamin C, vitamin E, selenium, and resveratrol. Citation patterns temporal analysis revealed a transition of the scientific interest from research focused on antioxidant vitamins and minerals into stronger attention focus on antioxidant phytochemicals (plant secondary metabolites).
| S-EPMC6341248 | biostudies-literature
Project description:BackgroundThe recent, rapid development of digital technologies offers new possibilities for more efficient implementation of electronic health record (EHR) and personal health record (PHR) systems. A growing volume of healthcare data has been the hallmark of this digital transformation. The large healthcare datasets' complexity and their dynamic nature pose various challenges related to processing, analysis, storage, security, privacy, data exchange, and usability.Materials and methodsWe performed a systematic review of systematic reviews to assess technological progress in EHR and PHR systems. We searched MEDLINE, Cochrane, Web of Science, and Scopus for systematic literature reviews on technological advancements that support EHR and PHR systems published between January 1, 2010, and October 06, 2020.ResultsThe searches resulted in a total of 2,448 hits. Of these, we finally selected 23 systematic reviews. Most of the included papers dealt with information extraction tools and natural language processing technology (n = 10), followed by studies that assessed the use of blockchain technology in healthcare (n = 8). Other areas of digital technology research included EHR and PHR systems in austere settings (n = 1), de-identification methods (n = 1), visualization techniques (n = 1), communication tools within EHR and PHR systems (n = 1), and methodologies for defining Clinical Information Models that promoted EHRs and PHRs interoperability (n = 1).ConclusionsTechnological advancements can improve the efficiency in the implementation of EHR and PHR systems in numerous ways. Natural language processing techniques, either rule-based, machine-learning, or deep learning-based, can extract information from clinical narratives and other unstructured data locked in EHRs and PHRs, allowing secondary research (i.e., phenotyping). Moreover, EHRs and PHRs are expected to be the primary beneficiaries of the blockchain technology implementation on Health Information Systems. Governance regulations, lack of trust, poor scalability, security, privacy, low performance, and high cost remain the most critical challenges for implementing these technologies.
| S-EPMC8223493 | biostudies-literature
Project description:Dendritic cells (DCs) are an indispensable part of studying human responses that are important for protective immunity against cancer and infectious diseases as well as prevention of autoimmunity and transplant rejection. These cells are also key elements of personalized vaccines for cancer and infectious diseases. Despite the vital role of DCs in both clinical and basic research contexts, methods for obtaining these cells from individuals remains a comparatively under-developed and inefficient process. DCs are present in very low concentrations (<1%) in blood, thus they must be generated from monocytes and the current methodology in DC generation involves a laborious process of static culture and stimulation with cytokines contained in culture medium. Herein, we describe an automated fluidic system, MicroDEN, that allows for differentiation of monocytes into immature-DCs (iDCs) utilizing continuous perfusion of differentiation media. Manual steps associated with current ex vivo monocyte differentiation are vastly reduced and an aseptic environment is ensured by the use of an enclosed cartridge and tubing network. Benchmark phenotyping was performed on the generated iDCs along with allogeneic T-cell proliferation and syngeneic antigen-specific functional assays. MicroDEN generated iDCs were phenotypically and functionally similar to well plate generated iDCs, thereby demonstrating the feasibility of utilizing MicroDEN in the broad range of applications requiring DCs.
| S-EPMC5946316 | biostudies-literature
Project description:Disagreement is essential to scientific progress but the extent of disagreement in science, its evolution over time, and the fields in which it happens remain poorly understood. Here we report the development of an approach based on cue phrases that can identify instances of disagreement in scientific articles. These instances are sentences in an article that cite other articles. Applying this approach to a collection of more than four million English-language articles published between 2000 and 2015 period, we determine the level of disagreement in five broad fields within the scientific literature (biomedical and health sciences; life and earth sciences; mathematics and computer science; physical sciences and engineering; and social sciences and humanities) and 817 meso-level fields. Overall, the level of disagreement is highest in the social sciences and humanities, and lowest in mathematics and computer science. However, there is considerable heterogeneity across the meso-level fields, revealing the importance of local disciplinary cultures and the epistemic characteristics of disagreement. Analysis at the level of individual articles reveals notable episodes of disagreement in science, and illustrates how methodological artifacts can confound analyses of scientific texts.
| S-EPMC8709576 | biostudies-literature
Project description:Networks of droplets, in which aqueous compartments are separated by lipid bilayers, have shown great potential as a model for biological transmembrane communication. We present a microfluidic system which allows for on-demand generation of droplets that are hydrodynamically locked in a trapping structure. As a result, the system enables the formation of a network of four droplets connected via lipid bilayers and the positions of each droplet in the network can be controlled thanks to automation of microfluidic operations. We perform electrophysiological measurements of ionic currents indicating interactions between nanopores and small molecules to prove the potential of the device in screening of the inhibitors acting on membrane proteins. We also demonstrate, for the first time, a microfluidic droplet interface bilayer (DIB) system in which the testing of inhibitors can be performed without direct contact between the tested sample and the electrodes recording picoampere currents.
| S-EPMC6190347 | biostudies-literature
Project description:ObjectiveTo pilot test a new closed-loop control technology to validate it for a further large clinical trial.Research design and methodsThe t:slim X2 insulin pump with Control-IQ Technology (Tandem Diabetes Care) includes a Dexcom G6 sensor and a closed-loop algorithm implemented in the pump that 1) automates insulin correction boluses, 2) has a dedicated hypoglycemia safety system, and 3) gradually intensifies control overnight, aiming for blood glucose levels of approximately 100-120 mg/dL every morning.ResultsFive patients with type 1 diabetes (mean age 52.8 years, 2/3 male/female, mean A1C 6.5%) used Control-IQ in an outpatient setting (hotel) for approximately 37 h. During the closed loop, mean glucose was 129 mg/dL (135/121 mg/dL during the day/night), time within target range 70-180 mg/dL was 87% (82%/94% during the day/night), and time <60 mg/dL was 1.1% (2.0%/0.0% during the day/night).ConclusionsFollowing this pilot trial, Control-IQ was deployed in several studies, including the large-scale National Institutes of Health International Diabetes Closed-Loop (iDCL) Trial.
| S-EPMC6245207 | biostudies-literature