-
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 -
European Respiratory Review : An... Jun 2023COPD and adult-onset asthma (AOA) are the most common noncommunicable respiratory diseases. To improve early identification and prevention, an overview of risk factors... (Review)
Review
BACKGROUND
COPD and adult-onset asthma (AOA) are the most common noncommunicable respiratory diseases. To improve early identification and prevention, an overview of risk factors is needed. We therefore aimed to systematically summarise the nongenetic (exposome) risk factors for AOA and COPD. Additionally, we aimed to compare the risk factors for COPD and AOA.
METHODS
In this umbrella review, we searched PubMed for articles from inception until 1 February 2023 and screened the references of relevant articles. We included systematic reviews and meta-analyses of observational epidemiological studies in humans that assessed a minimum of one lifestyle or environmental risk factor for AOA or COPD.
RESULTS
In total, 75 reviews were included, of which 45 focused on risk factors for COPD, 28 on AOA and two examined both. For asthma, 43 different risk factors were identified while 45 were identified for COPD. For AOA, smoking, a high body mass index (BMI), wood dust exposure and residential chemical exposures, such as formaldehyde exposure or exposure to volatile organic compounds, were amongst the risk factors found. For COPD, smoking, ambient air pollution including nitrogen dioxide, a low BMI, indoor biomass burning, childhood asthma, occupational dust exposure and diet were amongst the risk factors found.
CONCLUSIONS
Many different factors for COPD and asthma have been found, highlighting the differences and similarities. The results of this systematic review can be used to target and identify people at high risk for COPD or AOA.
Topics: Adult; Humans; Child; Pulmonary Disease, Chronic Obstructive; Asthma; Risk Factors; Air Pollution; Dust; Environmental Exposure
PubMed: 37137510
DOI: 10.1183/16000617.0009-2023 -
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 -
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 -
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 -
Research Square Jul 2023The Encyclopedia of DNA elements (ENCODE) project is a collaborative effort to create a comprehensive catalog of functional elements in the human genome. The current...
The Encyclopedia of DNA elements (ENCODE) project is a collaborative effort to create a comprehensive catalog of functional elements in the human genome. The current database comprises more than 19000 functional genomics experiments across more than 1000 cell lines and tissues using a wide array of experimental techniques to study the chromatin structure, regulatory and transcriptional landscape of the and genomes. All experimental data, metadata, and associated computational analyses created by the ENCODE consortium are submitted to the Data Coordination Center (DCC) for validation, tracking, storage, and distribution to community resources and the scientific community. The ENCODE project has engineered and distributed uniform processing pipelines in order to promote data provenance and reproducibility as well as allow interoperability between genomic resources and other consortia. All data files, reference genome versions, software versions, and parameters used by the pipelines are captured and available the ENCODE Portal. The pipeline code, developed using Docker and Workflow Description Language (WDL; https://openwdl.org/) is publicly available in GitHub, with images available on Dockerhub (https://hub.docker.com), enabling access to a diverse range of biomedical researchers. ENCODE pipelines maintained and used by the DCC can be installed to run on personal computers, local HPC clusters, or in cloud computing environments Cromwell. Access to the pipelines and data the cloud allows small labs the ability to use the data or software without access to institutional compute clusters. Standardization of the computational methodologies for analysis and quality control leads to comparable results from different ENCODE collections - a prerequisite for successful integrative analyses.
PubMed: 37503119
DOI: 10.21203/rs.3.rs-3111932/v1 -
NPJ Precision Oncology Aug 2023This study evaluates the quality of published research using artificial intelligence (AI) for ovarian cancer diagnosis or prognosis using histopathology data. A... (Review)
Review
This study evaluates the quality of published research using artificial intelligence (AI) for ovarian cancer diagnosis or prognosis using histopathology data. A systematic search of PubMed, Scopus, Web of Science, Cochrane CENTRAL, and WHO-ICTRP was conducted up to May 19, 2023. Inclusion criteria required that AI was used for prognostic or diagnostic inferences in human ovarian cancer histopathology images. Risk of bias was assessed using PROBAST. Information about each model was tabulated and summary statistics were reported. The study was registered on PROSPERO (CRD42022334730) and PRISMA 2020 reporting guidelines were followed. Searches identified 1573 records, of which 45 were eligible for inclusion. These studies contained 80 models of interest, including 37 diagnostic models, 22 prognostic models, and 21 other diagnostically relevant models. Common tasks included treatment response prediction (11/80), malignancy status classification (10/80), stain quantification (9/80), and histological subtyping (7/80). Models were developed using 1-1375 histopathology slides from 1-776 ovarian cancer patients. A high or unclear risk of bias was found in all studies, most frequently due to limited analysis and incomplete reporting regarding participant recruitment. Limited research has been conducted on the application of AI to histopathology images for diagnostic or prognostic purposes in ovarian cancer, and none of the models have been demonstrated to be ready for real-world implementation. Key aspects to accelerate clinical translation include transparent and comprehensive reporting of data provenance and modelling approaches, and improved quantitative evaluation using cross-validation and external validations. This work was funded by the Engineering and Physical Sciences Research Council.
PubMed: 37653025
DOI: 10.1038/s41698-023-00432-6 -
Cell Reports Sep 2023In ribosome-associated quality control (RQC), nascent polypeptides produced by interrupted translation are modified with C-terminal polyalanine tails ("Ala-tails") that...
In ribosome-associated quality control (RQC), nascent polypeptides produced by interrupted translation are modified with C-terminal polyalanine tails ("Ala-tails") that function outside ribosomes to induce ubiquitylation by E3 ligases Pirh2 (p53-induced RING-H2 domain-containing) or CRL2 (Cullin-2 RING ligase2)-KLHDC10. Here, we investigate the molecular basis of Ala-tail function using biochemical and in silico approaches. We show that Pirh2 and KLHDC10 directly bind to Ala-tails and that structural predictions identify candidate Ala-tail-binding sites, which we experimentally validate. The degron-binding pockets and specific pocket residues implicated in Ala-tail recognition are conserved among Pirh2 and KLHDC10 homologs, suggesting that an important function of these ligases across eukaryotes is in targeting Ala-tailed substrates. Moreover, we establish that the two Ala-tail-binding pockets have convergently evolved, either from an ancient module of bacterial provenance (Pirh2) or via tinkering of a widespread C-degron-recognition element (KLHDC10). These results shed light on the recognition of a simple degron sequence and the evolution of Ala-tail proteolytic signaling.
Topics: Humans; Alanine; Binding Sites; Proteolysis; Ubiquitin-Protein Ligases; Ubiquitination; Carrier Proteins
PubMed: 37676773
DOI: 10.1016/j.celrep.2023.113100 -
ArXiv Nov 2023Technological advances in high-throughput microscopy have facilitated the acquisition of cell images at a rapid pace, and data pipelines can now extract and process...
Technological advances in high-throughput microscopy have facilitated the acquisition of cell images at a rapid pace, and data pipelines can now extract and process thousands of image-based features from microscopy images. These features represent valuable single-cell phenotypes that contain information about cell state and biological processes. The use of these features for biological discovery is known as image-based or morphological profiling. However, these raw features need processing before use and image-based profiling lacks scalable and reproducible open-source software. Inconsistent processing across studies makes it difficult to compare datasets and processing steps, further delaying the development of optimal pipelines, methods, and analyses. To address these issues, we present Pycytominer, an open-source software package with a vibrant community that establishes an image-based profiling standard. Pycytominer has a simple, user-friendly Application Programming Interface (API) that implements image-based profiling functions for processing high-dimensional morphological features extracted from microscopy images of cells. Establishing Pycytominer as a standard image-based profiling toolkit ensures consistent data processing pipelines with data provenance, therefore minimizing potential inconsistencies and enabling researchers to confidently derive accurate conclusions and discover novel insights from their data, thus driving progress in our field.
PubMed: 38045474
DOI: No ID Found -
Foods (Basel, Switzerland) Aug 2023With the rise of globalization and technological competition, the food supply chain has grown more complex due to the multiple players and factors involved in the chain.... (Review)
Review
With the rise of globalization and technological competition, the food supply chain has grown more complex due to the multiple players and factors involved in the chain. Traditional systems fail to offer effective and reliable traceability solutions considering the increasing requirement for accountability and transparency in the food supply chain. Blockchain technology has been claimed to offer the food industry a transformative future. The inherent features of blockchain, including immutability and transparency, create a dependable and secure system for tracking food products across the whole supply chain, ensuring total control over their traceability from the origin to the final consumer. This research offers a comprehensive overview of multiple models to understand how the integration of blockchain and other digital technologies has transformed the food supply chain. This comprehensive systematic review of blockchain-based food-supply-chain frameworks aimed to uncover the capability of blockchain technology to revolutionize the industry and examined the current landscape of blockchain-based food traceability solutions to identify areas for improvement. Furthermore, the research investigates recent advancements and investigates how blockchain aligns with other emerging technologies of Industry 4.0 and Web 3.0. Blockchain technology plays an important role in improving food traceability and supply-chain operations. Potential synergies between blockchain and other emerging technologies of Industry 4.0 and Web 3.0 are digitizing food supply chains, which results in better management, automation, efficiencies, sustainability, verifiability, auditability, accountability, traceability, transparency, tracking, monitoring, response times and provenance across food supply chains.
PubMed: 37628025
DOI: 10.3390/foods12163026