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Frontiers in Endocrinology 2023The discovery of insulin in 1921 introduced a new branch of research into insulin activity and insulin resistance. Many discoveries in this field have been applied to... (Review)
Review
The discovery of insulin in 1921 introduced a new branch of research into insulin activity and insulin resistance. Many discoveries in this field have been applied to diagnosing and treating diseases related to insulin resistance. In this mini-review, the authors attempt to synthesize the updated discoveries to unravel the related mechanisms and inform the development of novel applications. Firstly, we depict the insulin signaling pathway to explain the physiology of insulin action starting at the receptor sites of insulin and downstream the signaling of the insulin signaling pathway. Based on this, the next part will analyze the mechanisms of insulin resistance with two major provenances: the defects caused by receptors and the defects due to extra-receptor causes, but in this study, we focus on post-receptor causes. Finally, we discuss the recent applications including the diseases related to insulin resistance (obesity, cardiovascular disease, Alzheimer's disease, and cancer) and the potential treatment of those based on insulin resistance mechanisms.
Topics: Humans; Insulin; Insulin Resistance; Signal Transduction; Alzheimer Disease; Binding Sites
PubMed: 37664840
DOI: 10.3389/fendo.2023.1226655 -
Wellcome Open Research 2018The PubMLST.org website hosts a collection of open-access, curated databases that integrate population sequence data with provenance and phenotype information for over...
The PubMLST.org website hosts a collection of open-access, curated databases that integrate population sequence data with provenance and phenotype information for over 100 different microbial species and genera. Although the PubMLST website was conceived as part of the development of the first multi-locus sequence typing (MLST) scheme in 1998 the software it uses, the Bacterial Isolate Genome Sequence database (BIGSdb, published in 2010), enables PubMLST to include all levels of sequence data, from single gene sequences up to and including complete, finished genomes. Here we describe developments in the BIGSdb software made from publication to June 2018 and show how the platform realises microbial population genomics for a wide range of applications. The system is based on the gene-by-gene analysis of microbial genomes, with each deposited sequence annotated and curated to identify the genes present and systematically catalogue their variation. Originally intended as a means of characterising isolates with typing schemes, the synthesis of sequences and records of genetic variation with provenance and phenotype data permits highly scalable (whole genome sequence data for tens of thousands of isolates) means of addressing a wide range of functional questions, including: the prediction of antimicrobial resistance; likely cross-reactivity with vaccine antigens; and the functional activities of different variants that lead to key phenotypes. There are no limitations to the number of sequences, genetic loci, allelic variants or schemes (combinations of loci) that can be included, enabling each database to represent an expanding catalogue of the genetic variation of the population in question. In addition to providing web-accessible analyses and links to third-party analysis and visualisation tools, the BIGSdb software includes a RESTful application programming interface (API) that enables access to all the underlying data for third-party applications and data analysis pipelines.
PubMed: 30345391
DOI: 10.12688/wellcomeopenres.14826.1 -
Health Expectations : An International... Aug 2019Numerous frameworks for supporting, evaluating and reporting patient and public involvement in research exist. The literature is diverse and theoretically heterogeneous.
BACKGROUND
Numerous frameworks for supporting, evaluating and reporting patient and public involvement in research exist. The literature is diverse and theoretically heterogeneous.
OBJECTIVES
To identify and synthesize published frameworks, consider whether and how these have been used, and apply design principles to improve usability.
SEARCH STRATEGY
Keyword search of six databases; hand search of eight journals; ancestry and snowball search; requests to experts.
INCLUSION CRITERIA
Published, systematic approaches (frameworks) designed to support, evaluate or report on patient or public involvement in health-related research.
DATA EXTRACTION AND SYNTHESIS
Data were extracted on provenance; collaborators and sponsors; theoretical basis; lay input; intended user(s) and use(s); topics covered; examples of use; critiques; and updates. We used the Canadian Centre for Excellence on Partnerships with Patients and Public (CEPPP) evaluation tool and hermeneutic methodology to grade and synthesize the frameworks. In five co-design workshops, we tested evidence-based resources based on the review findings.
RESULTS
Our final data set consisted of 65 frameworks, most of which scored highly on the CEPPP tool. They had different provenances, intended purposes, strengths and limitations. We grouped them into five categories: power-focused; priority-setting; study-focused; report-focused; and partnership-focused. Frameworks were used mainly by the groups who developed them. The empirical component of our study generated a structured format and evidence-based facilitator notes for a "build your own framework" co-design workshop.
CONCLUSION
The plethora of frameworks combined with evidence of limited transferability suggests that a single, off-the-shelf framework may be less useful than a menu of evidence-based resources which stakeholders can use to co-design their own frameworks.
Topics: Community Participation; Empowerment; Group Processes; Humans; Patient Participation; Research
PubMed: 31012259
DOI: 10.1111/hex.12888 -
Scientific Data Apr 2023We present a database resulting from high throughput experimentation, primarily on metal oxide solid state materials. The central relational database, the Materials...
We present a database resulting from high throughput experimentation, primarily on metal oxide solid state materials. The central relational database, the Materials Provenance Store (MPS), manages the metadata and experimental provenance from acquisition of raw materials, through synthesis, to a broad range of materials characterization techniques. Given the primary research goal of materials discovery of solar fuels materials, many of the characterization experiments involve electrochemistry, along with optical, structural, and compositional characterizations. The MPS is populated with all information required for executing common data queries, which typically do not involve direct query of raw data. The result is a database file that can be distributed to users so that they can independently execute queries and subsequently download the data of interest. We propose this strategy as an approach to manage the highly heterogeneous and distributed data that arises from materials science experiments, as demonstrated by the management of over 30 million experiments run on over 12 million samples in the present MPS release.
Topics: Semantics; Databases, Factual; Metadata
PubMed: 37024515
DOI: 10.1038/s41597-023-02107-0 -
Journal of Biomedical Semantics Jul 2023Clinical decision support systems have been widely deployed to guide healthcare decisions on patient diagnosis, treatment choices, and patient management through...
BACKGROUND
Clinical decision support systems have been widely deployed to guide healthcare decisions on patient diagnosis, treatment choices, and patient management through evidence-based recommendations. These recommendations are typically derived from clinical practice guidelines created by clinical specialties or healthcare organizations. Although there have been many different technical approaches to encoding guideline recommendations into decision support systems, much of the previous work has not focused on enabling system generated recommendations through the formalization of changes in a guideline, the provenance of a recommendation, and applicability of the evidence. Prior work indicates that healthcare providers may not find that guideline-derived recommendations always meet their needs for reasons such as lack of relevance, transparency, time pressure, and applicability to their clinical practice.
RESULTS
We introduce several semantic techniques that model diseases based on clinical practice guidelines, provenance of the guidelines, and the study cohorts they are based on to enhance the capabilities of clinical decision support systems. We have explored ways to enable clinical decision support systems with semantic technologies that can represent and link to details in related items from the scientific literature and quickly adapt to changing information from the guidelines, identifying gaps, and supporting personalized explanations. Previous semantics-driven clinical decision systems have limited support in all these aspects, and we present the ontologies and semantic web based software tools in three distinct areas that are unified using a standard set of ontologies and a custom-built knowledge graph framework: (i) guideline modeling to characterize diseases, (ii) guideline provenance to attach evidence to treatment decisions from authoritative sources, and (iii) study cohort modeling to identify relevant research publications for complicated patients.
CONCLUSIONS
We have enhanced existing, evidence-based knowledge by developing ontologies and software that enables clinicians to conveniently access updates to and provenance of guidelines, as well as gather additional information from research studies applicable to their patients' unique circumstances. Our software solutions leverage many well-used existing biomedical ontologies and build upon decades of knowledge representation and reasoning work, leading to explainable results.
Topics: Humans; Decision Support Systems, Clinical; Software; Knowledge Bases; Biological Ontologies; Publications
PubMed: 37464259
DOI: 10.1186/s13326-023-00285-9 -
Journal of Medical Internet Research Mar 2023Data provenance refers to the origin, processing, and movement of data. Reliable and precise knowledge about data provenance has great potential to improve... (Review)
Review
BACKGROUND
Data provenance refers to the origin, processing, and movement of data. Reliable and precise knowledge about data provenance has great potential to improve reproducibility as well as quality in biomedical research and, therefore, to foster good scientific practice. However, despite the increasing interest on data provenance technologies in the literature and their implementation in other disciplines, these technologies have not yet been widely adopted in biomedical research.
OBJECTIVE
The aim of this scoping review was to provide a structured overview of the body of knowledge on provenance methods in biomedical research by systematizing articles covering data provenance technologies developed for or used in this application area; describing and comparing the functionalities as well as the design of the provenance technologies used; and identifying gaps in the literature, which could provide opportunities for future research on technologies that could receive more widespread adoption.
METHODS
Following a methodological framework for scoping studies and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, articles were identified by searching the PubMed, IEEE Xplore, and Web of Science databases and subsequently screened for eligibility. We included original articles covering software-based provenance management for scientific research published between 2010 and 2021. A set of data items was defined along the following five axes: publication metadata, application scope, provenance aspects covered, data representation, and functionalities. The data items were extracted from the articles, stored in a charting spreadsheet, and summarized in tables and figures.
RESULTS
We identified 44 original articles published between 2010 and 2021. We found that the solutions described were heterogeneous along all axes. We also identified relationships among motivations for the use of provenance information, feature sets (capture, storage, retrieval, visualization, and analysis), and implementation details such as the data models and technologies used. The important gap that we identified is that only a few publications address the analysis of provenance data or use established provenance standards, such as PROV.
CONCLUSIONS
The heterogeneity of provenance methods, models, and implementations found in the literature points to the lack of a unified understanding of provenance concepts for biomedical data. Providing a common framework, a biomedical reference, and benchmarking data sets could foster the development of more comprehensive provenance solutions.
Topics: Humans; Biomedical Research; Metadata; PubMed; Reproducibility of Results; Software
PubMed: 36972116
DOI: 10.2196/42289 -
Sensors (Basel, Switzerland) Jul 2023Data provenance means recording data origins and the history of data generation and processing. In healthcare, data provenance is one of the essential processes that... (Review)
Review
Data provenance means recording data origins and the history of data generation and processing. In healthcare, data provenance is one of the essential processes that make it possible to track the sources and reasons behind any problem with a user's data. With the emergence of the General Data Protection Regulation (GDPR), data provenance in healthcare systems should be implemented to give users more control over data. This SLR studies the impacts of data provenance in healthcare and GDPR-compliance-based data provenance through a systematic review of peer-reviewed articles. The SLR discusses the technologies used to achieve data provenance and various methodologies to achieve data provenance. We then explore different technologies that are applied in the healthcare domain and how they achieve data provenance. In the end, we have identified key research gaps followed by future research directions.
Topics: Biomedical Research; Delivery of Health Care
PubMed: 37514788
DOI: 10.3390/s23146495 -
Anatomical Record (Hoboken, N.J. : 2007) Apr 2022Human fetal and embryos collections (FECs) peaked in the late 19th century, an era before informed consent, and hence have unclear provenance. These collections are not... (Review)
Review
Human fetal and embryos collections (FECs) peaked in the late 19th century, an era before informed consent, and hence have unclear provenance. These collections are not only historical artifacts, but prized resources for education and research. This study aimed to determine, via a narrative review, the present location, status, and profile of reported human fetal and embryonic collections. Twenty-seven articles that reported on collections appropriate to the study were selected from an initial search pool of 120 articles. The reported collections were in: Australia (n = 1), Germany (n = 6), Japan (n = 1), Spain (n = 1), and the United States (n = 5). The largest collection is reported to contain 45,000 prenatal remains and the smallest, three remains. The purpose of establishing majority of the collections was for education and research. Eight collections contain both embryos and fetuses, one collection contained embryos, exclusively. Another collection contained only fetuses and one neonatal cadaver. The provenance, where mentioned, specified gynecologists and obstetricians as the main source of remains (n = 5). Except for the Kyoto Collection, information regarding informed consent from the next-of-kin was lacking. This paper draws upon the three themes of purpose, provenance, and profile and highlights the need to establish agreed international guidelines for the most appropriate ethical and sustainable practice with respect to establishment, procurement of remains, access, and maintenance of these collections. Nine domains for these guidelines are recommended: consent, privacy, commercial gain, digital and emerging technologies, commemorations and memorials, destruction and disposal, dignity of donors, global database and collaboration, and sustainability.
Topics: Cadaver; Female; Fetus; Germany; Humans; Infant, Newborn; Informed Consent; Pregnancy; Spain; United States
PubMed: 35099840
DOI: 10.1002/ar.24863 -
Journal of Personalized Medicine Jun 2023This article aims to perform a Systematic Literature Review (SLR) to better understand the structures of different methods, techniques, models, methodologies, and... (Review)
Review
AIMS
This article aims to perform a Systematic Literature Review (SLR) to better understand the structures of different methods, techniques, models, methodologies, and technologies related to provenance data management in health information systems (HISs). The SLR developed here seeks to answer the questions that contribute to describing the results.
METHOD
An SLR was performed on six databases using a search string. The backward and forward snowballing technique was also used. Eligible studies were all articles in English that presented on the use of different methods, techniques, models, methodologies, and technologies related to provenance data management in HISs. The quality of the included articles was assessed to obtain a better connection to the topic studied.
RESULTS
Of the 239 studies retrieved, 14 met the inclusion criteria described in this SLR. In order to complement the retrieved studies, 3 studies were included using the backward and forward snowballing technique, totaling 17 studies dedicated to the construction of this research. Most of the selected studies were published as conference papers, which is common when involving computer science in HISs. There was a more frequent use of data provenance models from the PROV family in different HISs combined with different technologies, among which blockchain and middleware stand out. Despite the advantages found, the lack of technological structure, data interoperability problems, and the technical unpreparedness of working professionals are still challenges encountered in the management of provenance data in HISs.
CONCLUSION
It was possible to conclude the existence of different methods, techniques, models, and combined technologies, which are presented in the proposal of a taxonomy that provides researchers with a new understanding about the management of provenance data in HISs.
PubMed: 37373980
DOI: 10.3390/jpm13060991 -
Patterns (New York, N.Y.) Sep 2021Reproducible computational research (RCR) is the keystone of the scientific method for analyses, packaging the transformation of raw data to published results. In... (Review)
Review
Reproducible computational research (RCR) is the keystone of the scientific method for analyses, packaging the transformation of raw data to published results. In addition to its role in research integrity, improving the reproducibility of scientific studies can accelerate evaluation and reuse. This potential and wide support for the FAIR principles have motivated interest in metadata standards supporting reproducibility. Metadata provide context and provenance to raw data and methods and are essential to both discovery and validation. Despite this shared connection with scientific data, few studies have explicitly described how metadata enable reproducible computational research. This review employs a functional content analysis to identify metadata standards that support reproducibility across an analytic stack consisting of input data, tools, notebooks, pipelines, and publications. Our review provides background context, explores gaps, and discovers component trends of embeddedness and methodology weight from which we derive recommendations for future work.
PubMed: 34553169
DOI: 10.1016/j.patter.2021.100322