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Clinical Infectious Diseases : An... Dec 2023Antimicrobial resistance (AMR) is a multifaceted global health problem disproportionately affecting low- and middle-income countries (LMICs). The Capturing data on...
Methodological Approach to Identify and Expand the Volume of Antimicrobial Resistance (AMR) Data in the Human Health Sector in Low- and Middle-Income Countries in Asia: Implications for Local and Regional AMR Surveillance Systems Strengthening.
Antimicrobial resistance (AMR) is a multifaceted global health problem disproportionately affecting low- and middle-income countries (LMICs). The Capturing data on Antimicrobial resistance Patterns and Trends in Use in Regions of Asia (CAPTURA) project was tasked to expand the volume of AMR and antimicrobial use data in Asia. The CAPTURA project used 2 data-collection streams: facility data and project metadata. Project metadata constituted information collected to map out data sources and assess data quality, while facility data referred to the retrospective data collected from healthcare facilities. A down-selection process, labelled "the funnel approach" by the project, was adopted to use the project metadata in prioritizing and selecting laboratories for retrospective AMR data collection. Moreover, the metadata served as a guide for understanding the AMR data once they were collected. The findings from CAPTURA's metadata add to the current discourse on the limitation of AMR data in LMICs. There is generally a low volume of AMR data generated as there is a lack of microbiology laboratories with sufficient antimicrobial susceptibility testing capacity. Many laboratories in Asia are still capturing data on paper, resulting in scattered or unused data not readily accessible or shareable for analyses. There is also a lack of clinical and epidemiological data captured, impeding interpretation and in-depth understanding of the AMR data. CAPTURA's experience in Asia suggests that there is a wide spectrum of capacity and capability of microbiology laboratories within a country and region. As local AMR surveillance is a crucial instrument to inform context-specific measures to combat AMR, it is important to understand and assess current capacity-building needs while implementing activities to enhance surveillance systems.
Topics: Humans; Anti-Bacterial Agents; Developing Countries; Retrospective Studies; Drug Resistance, Bacterial; Asia
PubMed: 38118007
DOI: 10.1093/cid/ciad634 -
Frontiers in Cellular and Infection... 2024Sharing microbiome data among researchers fosters new innovations and reduces cost for research. Practically, this means that the (meta)data will have to be...
INTRODUCTION
Sharing microbiome data among researchers fosters new innovations and reduces cost for research. Practically, this means that the (meta)data will have to be standardized, transparent and readily available for researchers. The microbiome data and associated metadata will then be described with regards to composition and origin, in order to maximize the possibilities for application in various contexts of research. Here, we propose a set of tools and protocols to develop a real-time FAIR (Findable. Accessible, Interoperable and Reusable) compliant database for the handling and storage of human microbiome and host-associated data.
METHODS
The conflicts arising from privacy laws with respect to metadata, possible human genome sequences in the metagenome shotgun data and FAIR implementations are discussed. Alternate pathways for achieving compliance in such conflicts are analyzed. Sample traceable and sensitive microbiome data, such as DNA sequences or geolocalized metadata are identified, and the role of the GDPR (General Data Protection Regulation) data regulations are considered. For the construction of the database, procedures have been realized to make data FAIR compliant, while preserving privacy of the participants providing the data.
RESULTS AND DISCUSSION
An open-source development platform, Supabase, was used to implement the microbiome database. Researchers can deploy this real-time database to access, upload, download and interact with human microbiome data in a FAIR complaint manner. In addition, a large language model (LLM) powered by ChatGPT is developed and deployed to enable knowledge dissemination and non-expert usage of the database.
Topics: Humans; Microbiota; Databases, Factual; Metadata; Metagenome; Information Dissemination; Computational Biology; Metagenomics; Databases, Genetic
PubMed: 38774631
DOI: 10.3389/fcimb.2024.1384809 -
Nucleic Acids Research Jan 2024Plasmids are mobile genetic elements found in many clades of Archaea and Bacteria. They drive horizontal gene transfer, impacting ecological and evolutionary processes...
Plasmids are mobile genetic elements found in many clades of Archaea and Bacteria. They drive horizontal gene transfer, impacting ecological and evolutionary processes within microbial communities, and hold substantial importance in human health and biotechnology. To support plasmid research and provide scientists with data of an unprecedented diversity of plasmid sequences, we introduce the IMG/PR database, a new resource encompassing 699 973 plasmid sequences derived from genomes, metagenomes and metatranscriptomes. IMG/PR is the first database to provide data of plasmid that were systematically identified from diverse microbiome samples. IMG/PR plasmids are associated with rich metadata that includes geographical and ecosystem information, host taxonomy, similarity to other plasmids, functional annotation, presence of genes involved in conjugation and antibiotic resistance. The database offers diverse methods for exploring its extensive plasmid collection, enabling users to navigate plasmids through metadata-centric queries, plasmid comparisons and BLAST searches. The web interface for IMG/PR is accessible at https://img.jgi.doe.gov/pr. Plasmid metadata and sequences can be downloaded from https://genome.jgi.doe.gov/portal/IMG_PR.
Topics: Humans; Metagenome; Metadata; Software; Databases, Genetic; Plasmids; Microbiota
PubMed: 37930866
DOI: 10.1093/nar/gkad964 -
Journal of Medical Internet Research Nov 2023There has been a surge in academic and business interest in software as a medical device (SaMD). SaMD enables medical professionals to streamline existing medical... (Review)
Review
BACKGROUND
There has been a surge in academic and business interest in software as a medical device (SaMD). SaMD enables medical professionals to streamline existing medical practices and make innovative medical processes such as digital therapeutics a reality. Furthermore, SaMD is a billion-dollar market. However, SaMD is not clearly understood as a technological change and emerging industry.
OBJECTIVE
This study aims to review the landscape of SaMD in response to increasing interest in SaMD within health systems and regulation. The objectives of the study are to (1) clarify the innovation process of SaMD, (2) identify the prevailing typology of such innovation, and (3) elucidate the underlying mechanisms driving the SaMD innovation process.
METHODS
We collected product information on 581 US Food and Drug Administration-approved SaMDs from the OpenFDA website and 268 company profiles of the corresponding manufacturers from Crunchbase, Bloomberg, PichBook.com, and other company websites. In addition to assessing the metadata of SaMD, we used correspondence and business process analysis to assess the distribution of intended use and how SaMDs interact with other devices in the medical process.
RESULTS
The current SaMD industry is highly concentrated in medical image processing and radiological analysis. Incumbents in the medical device industry currently lead the market and focus on incremental innovation, whereas new entrants, particularly startups, produce more disruptive innovation. We found that hardware medical device functions as a complementary asset for SaMD, whereas how SaMD interacts with the complementary asset differs according to its intended use. Based on these findings, we propose a regime map that illustrates the SaMD innovation process.
CONCLUSIONS
SaMD, as an industry, is nascent and dominated by incremental innovation. The innovation process of the present SaMD industry is shaped by data accessibility, which is key to building disruptive innovation.
Topics: Humans; Commerce; Software; United States; United States Food and Drug Administration
PubMed: 37999948
DOI: 10.2196/47505 -
Scientific Data Dec 2023The "MEG-MASC" dataset provides a curated set of raw magnetoencephalography (MEG) recordings of 27 English speakers who listened to two hours of naturalistic stories....
The "MEG-MASC" dataset provides a curated set of raw magnetoencephalography (MEG) recordings of 27 English speakers who listened to two hours of naturalistic stories. Each participant performed two identical sessions, involving listening to four fictional stories from the Manually Annotated Sub-Corpus (MASC) intermixed with random word lists and comprehension questions. We time-stamp the onset and offset of each word and phoneme in the metadata of the recording, and organize the dataset according to the 'Brain Imaging Data Structure' (BIDS). This data collection provides a suitable benchmark to large-scale encoding and decoding analyses of temporally-resolved brain responses to speech. We provide the Python code to replicate several validations analyses of the MEG evoked responses such as the temporal decoding of phonetic features and word frequency. All code and MEG, audio and text data are publicly available to keep with best practices in transparent and reproducible research.
Topics: Humans; Brain; Brain Mapping; Magnetoencephalography; Speech; Speech Perception
PubMed: 38049487
DOI: 10.1038/s41597-023-02752-5 -
Scientific Data Oct 2023Transparent and FAIR disclosure of meta-information about healthcare data and infrastructure is essential but has not been well publicized. In this paper, we provide a...
Transparent and FAIR disclosure of meta-information about healthcare data and infrastructure is essential but has not been well publicized. In this paper, we provide a transparent disclosure of the process of standardizing a common data model and developing a national data infrastructure using national claims data. We established an Observational Medical Outcome Partnership (OMOP) common data model database for national claims data of the Health Insurance Review and Assessment Service of South Korea. To introduce a data openness policy, we built a distributed data analysis environment and released metadata based on the FAIR principle. A total of 10,098,730,241 claims and 56,579,726 patients' data were converted as OMOP common data model. We also built an analytics environment for distributed research and made the metadata publicly available. Disclosure of this infrastructure to researchers will help to eliminate information inequality and contribute to the generation of high-quality medical evidence.
PubMed: 37794003
DOI: 10.1038/s41597-023-02580-7 -
IMA Fungus Feb 2024Rust fungi (Pucciniales, Basidiomycota) are a species-rich (ca. 8000 species), globally distributed order of obligate plant pathogens. Rust species are host-specific,...
Rust fungi (Pucciniales, Basidiomycota) are a species-rich (ca. 8000 species), globally distributed order of obligate plant pathogens. Rust species are host-specific, and as a group they cause disease on many of our most economically and/or ecologically significant plants. As such, the ability to accurately and rapidly identify these fungi is of particular interest to mycologists, botanists, agricultural scientists, farmers, quarantine officials, and associated stakeholders. However, the complexities of the rust life cycle, which may include production of up to five different spore types and alternation between two unrelated host species, have made standard identifications, especially of less-documented spore states or alternate hosts, extremely difficult. The Arthur Fungarium (PUR) at Purdue University is home to one of the most comprehensive collections of rust fungi in the world. Using material vouchered in PUR supplemented with fresh collections we generated DNA barcodes of the 28S ribosomal repeat from > 3700 rust fungal specimens. Barcoded material spans 120 genera and > 1100 species, most represented by several replicate sequences. Barcodes and associated metadata are hosted in a publicly accessible, BLAST searchable database called Rust HUBB (Herbarium-based Universal Barcode Blast) and will be continuously updated.
PubMed: 38402196
DOI: 10.1186/s43008-023-00132-7 -
ELife Jul 2023Nullius in verba ('trust no one'), chosen as the motto of the Royal Society in 1660, implies that independently verifiable observations-rather than authoritative...
Nullius in verba ('trust no one'), chosen as the motto of the Royal Society in 1660, implies that independently verifiable observations-rather than authoritative claims-are a defining feature of empirical science. As the complexity of modern scientific instrumentation has made exact replications prohibitive, sharing data is now essential for ensuring the trustworthiness of one's findings. While embraced in spirit by many, in practice open data sharing remains the exception in contemporary systems neuroscience. Here, we take stock of the Allen Brain Observatory, an effort to share data and metadata associated with surveys of neuronal activity in the visual system of laboratory mice. Data from these surveys have been used to produce new discoveries, to validate computational algorithms, and as a benchmark for comparison with other data, resulting in over 100 publications and preprints to date. We distill some of the lessons learned about open surveys and data reuse, including remaining barriers to data sharing and what might be done to address these.
Topics: Animals; Mice; Neurophysiology; Brain; Neurosciences; Algorithms; Benchmarking
PubMed: 37432073
DOI: 10.7554/eLife.85550 -
Scientific Data Oct 2023Computer-assisted systems are becoming broadly used in medicine. In endoscopy, most research focuses on the automatic detection of polyps or other pathologies, but...
Computer-assisted systems are becoming broadly used in medicine. In endoscopy, most research focuses on the automatic detection of polyps or other pathologies, but localization and navigation of the endoscope are completely performed manually by physicians. To broaden this research and bring spatial Artificial Intelligence to endoscopies, data from complete procedures is needed. This paper introduces the Endomapper dataset, the first collection of complete endoscopy sequences acquired during regular medical practice, making secondary use of medical data. Its main purpose is to facilitate the development and evaluation of Visual Simultaneous Localization and Mapping (VSLAM) methods in real endoscopy data. The dataset contains more than 24 hours of video. It is the first endoscopic dataset that includes endoscope calibration as well as the original calibration videos. Meta-data and annotations associated with the dataset vary from the anatomical landmarks, procedure labeling, segmentations, reconstructions, simulated sequences with ground truth and same patient procedures. The software used in this paper is publicly available.
PubMed: 37789003
DOI: 10.1038/s41597-023-02564-7 -
Microbiology Spectrum Aug 2023Staphylococcus aureus is an opportunistic pathogen and a leading cause of morbidity and mortality worldwide. Genomic-based surveillance has greatly improved our ability...
Staphylococcus aureus is an opportunistic pathogen and a leading cause of morbidity and mortality worldwide. Genomic-based surveillance has greatly improved our ability to track the emergence and spread of high-risk clones, but the full potential of genomic data is only reached when used in conjunction with detailed metadata. Here, we demonstrate the utility of an integrated approach by leveraging a curated collection of clinical and epidemiological metadata of S. aureus in the San Matteo Hospital (Italy) through a semisupervised clustering strategy. We sequenced 226 sepsis S. aureus samples, recovered over a period of 9 years. By using existing antibiotic profiling data, we selected strains that capture the full diversity of the population. Genome analysis revealed 49 sequence types, 16 of which are novel. Comparative genomic analyses of hospital- and community-acquired infection ruled out the existence of genomic features differentiating them, while evolutionary analyses of genes and traits of interest highlighted different dynamics of acquisition and loss between antibiotic resistance and virulence genes. Finally, highly resistant clones belonging to clonal complexes (CC) 8 and 22 were found to be responsible for abundant infections and deaths, while the highly virulent CC30 was responsible for rare but deadly episodes of infections. Genome sequencing is an important tool in clinical microbiology, as it allows in-depth characterization of isolates of interest and can propel genome-based surveillance studies. Such studies can benefit from methods of sample selection to capture the genomic diversity present in a data set. Here, we present an approach based on clustering of antibiotic resistance profiles that allows optimal sample selection for bacterial genomic surveillance. We apply the method to a 9-year collection of Staphylococcus aureus from a large hospital in northern Italy. Our method allows us to sequence the genomes of a large variety of strains of this important pathogen, which we then leverage to characterize the epidemiology in the hospital and to perform evolutionary analyses on genes and traits of interest. These analyses highlight different dynamics of acquisition and loss between antibiotic resistance and virulence genes.
Topics: Humans; Staphylococcus aureus; Metadata; Staphylococcal Infections; Genome, Bacterial; Anti-Bacterial Agents; Hospitals; Methicillin-Resistant Staphylococcus aureus; Microbial Sensitivity Tests
PubMed: 37458594
DOI: 10.1128/spectrum.01010-23