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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 -
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 -
BMJ (Clinical Research Ed.) May 2023Ambitious goals that won’t be delivered without investment
Ambitious goals that won’t be delivered without investment
Topics: Humans; Health Policy; Politics; United Kingdom; State Medicine; Health Care Reform
PubMed: 37257901
DOI: 10.1136/bmj.p1232 -
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 -
AMIA ... Annual Symposium Proceedings.... 2023: To implement an open source, free, and easily deployable high throughput natural language processing module to extract concepts from clinician notes and map them to...
: To implement an open source, free, and easily deployable high throughput natural language processing module to extract concepts from clinician notes and map them to Fast Healthcare Interoperability Resources (FHIR). : Using a popular open-source NLP tool (Apache cTAKES), we create FHIR resources that use modifier extensions to represent negation and NLP sourcing, and another extension to represent provenance of extracted concepts. : The SMART Text2FHIR Pipeline is an open-source tool, released through standard package managers, and publicly available container images that implement the mappings, enabling ready conversion of clinical text to FHIR. : With the increased data liquidity because of new interoperability regulations, NLP processes that can output FHIR can enable a common language for transporting structured and unstructured data. This framework can be valuable for critical public health or clinical research use cases. : Future work should include mapping more categories of NLP-extracted information into FHIR resources and mappings from additional open-source NLP tools.
Topics: Humans; Electronic Health Records; Delivery of Health Care; Natural Language Processing; APACHE
PubMed: 38222416
DOI: No ID Found -
Balisage Series on Markup Technologies Aug 2022In this era of big data and FAIR data, data formats must be machine interpretable. XML, among other standards, satisfies this requirement. Yet many standardization...
In this era of big data and FAIR data, data formats must be machine interpretable. XML, among other standards, satisfies this requirement. Yet many standardization initiatives cite human readability as a second, key property in data format development. Examples include the development of STAR in the field of structural biology, W3C PROV for provenance, and even the continuing development of XML. This begs the question(s), what is meant by human readability and can this property be measured for a given data format or compared between competing standards? The broad topic of readability is considered with attention to the various aspects of written text which either foster or counter readability. Drawing on efforts in the educational system, a metric is proposed for estimating the relative human readability of structured data within an archival file format. Comparison is made between the same data represented in various formats, including JSON and XML, to help judge whether these standards have accomplished their simultaneous goals of machine interpretability and human readability.
PubMed: 38650826
DOI: 10.4242/balisagevol27.gryk01 -
BMJ (Clinical Research Ed.) Jan 2023The NHS is in crisis, and talk of fundamental reform is little more than a distraction
The NHS is in crisis, and talk of fundamental reform is little more than a distraction
Topics: Humans; State Medicine; Health Care Reform; Language
PubMed: 36631156
DOI: 10.1136/bmj.p54 -
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