The Medical Action Ontology: A tool for annotating and analyzing treatments and clinical management of human disease

Clinical management; Medical action ontology; OntologyGestión clínica; Ontología de la acción médica; OntologíaGestió clínica; Ontologia de l'acció mèdica; OntologiaBackground Navigating the clinical literature to determine the optimal clinical management for rare diseases presents significant challenges. We introduce the Medical Action Ontology (MAxO), an ontology specifically designed to organize medical procedures, therapies, and interventions. Methods MAxO incorporates logical structures that link MAxO terms to numerous other ontologies within the OBO Foundry. Term development involves a blend of manual and semi-automated processes. Additionally, we have generated annotations detailing diagnostic modalities for specific phenotypic abnormalities defined by the Human Phenotype Ontology (HPO). We introduce a web application, POET, that facilitates MAxO annotations for specific medical actions for diseases using the Mondo Disease Ontology. Findings MAxO encompasses 1,757 terms spanning a wide range of biomedical domains, from human anatomy and investigations to the chemical and protein entities involved in biological processes. These terms annotate phenotypic features associated with specific disease (using HPO and Mondo). Presently, there are over 16,000 MAxO diagnostic annotations that target HPO terms. Through POET, we have created 413 MAxO annotations specifying treatments for 189 rare diseases. Conclusions MAxO offers a computational representation of treatments and other actions taken for the clinical management of patients. Its development is closely coupled to Mondo and HPO, broadening the scope of our computational modeling of diseases and phenotypic features. We invite the community to contribute disease annotations using POET (https://poet.jax.org/). MAxO is available under the open-source CC-BY 4.0 license (https://github.com/monarch-initiative/MAxO).This study was supported by the National Institutes of Health (NIH): NHGRI 1U24HG011449-01A1 and NHGRI 5RM1HG010860-04. R.H. is a Wellcome Trust Investigator (109915/Z/15/Z), who receives support from the Medical Research Council (UK) (MR/V009346/1), the Addenbrookes Charitable Trust (G100142), the Evelyn Trust, the Stoneygate Trust, the Lily Foundation, Action for AT and an MRC strategic award to establish an International Centre for Genomic Medicine in Neuromuscular Diseases (ICGNMD) MR/S005021/1. This research was supported by the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care

Gleam: the GLAST Large Area Telescope Simulation Framework

This paper presents the simulation of the GLAST high energy gamma-ray telescope. The simulation package, written in C++, is based on the Geant4 toolkit, and it is integrated into a general framework used to process events. A detailed simulation of the electronic signals inside Silicon detectors has been provided and it is used for the particle tracking, which is handled by a dedicated software. A unique repository for the geometrical description of the detector has been realized using the XML language and a C++ library to access this information has been designed and implemented.Comment: 10 pages, Late

GA4GH Phenopackets: A Practical Introduction.

The Global Alliance for Genomics and Health (GA4GH) is developing a suite of coordinated standards for genomics for healthcare. The Phenopacket is a new GA4GH standard for sharing disease and phenotype information that characterizes an individual person, linking that individual to detailed phenotypic descriptions, genetic information, diagnoses, and treatments. A detailed example is presented that illustrates how to use the schema to represent the clinical course of a patient with retinoblastoma, including demographic information, the clinical diagnosis, phenotypic features and clinical measurements, an examination of the extirpated tumor, therapies, and the results of genomic analysis. The Phenopacket Schema, together with other GA4GH data and technical standards, will enable data exchange and provide a foundation for the computational analysis of disease and phenotype information to improve our ability to diagnose and conduct research on all types of disorders, including cancer and rare diseases

GA4GH Phenopackets: A Practical Introduction

The Global Alliance for Genomics and Health (GA4GH) is developing a suite of coordinated standards for genomics for healthcare. The Phenopacket is a new GA4GH standard for sharing disease and phenotype information that characterizes an individual person, linking that individual to detailed phenotypic descriptions, genetic information, diagnoses, and treatments. A detailed example is presented that illustrates how to use the schema to represent the clinical course of a patient with retinoblastoma, including demographic information, the clinical diagnosis, phenotypic features and clinical measurements, an examination of the extirpated tumor, therapies, and the results of genomic analysis. The Phenopacket Schema, together with other GA4GH data and technical standards, will enable data exchange and provide a foundation for the computational analysis of disease and phenotype information to improve our ability to diagnose and conduct research on all types of disorders, including cancer and rare diseases

Improved Adaptive Group Testing Algorithms with Applications to Multiple Access Channels and Dead Sensor Diagnosis

We study group-testing algorithms for resolving broadcast conflicts on a multiple access channel (MAC) and for identifying the dead sensors in a mobile ad hoc wireless network. In group-testing algorithms, we are asked to identify all the defective items in a set of items when we can test arbitrary subsets of items. In the standard group-testing problem, the result of a test is binary--the tested subset either contains defective items or not. In the more generalized versions we study in this paper, the result of each test is non-binary. For example, it may indicate whether the number of defective items contained in the tested subset is zero, one, or at least two. We give adaptive algorithms that are provably more efficient than previous group testing algorithms. We also show how our algorithms can be applied to solve conflict resolution on a MAC and dead sensor diagnosis. Dead sensor diagnosis poses an interesting challenge compared to MAC resolution, because dead sensors are not locally detectable, nor are they themselves active participants.Comment: Expanded version of a paper appearing in ACM Symposium on Parallelism in Algorithms and Architectures (SPAA), and preliminary version of paper appearing in Journal of Combinatorial Optimizatio

The Human Phenotype Ontology in 2024: phenotypes around the world

The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs

The Medical Action Ontology: A tool for annotating and analyzing treatments and clinical management of human disease.

BACKGROUND: Navigating the clinical literature to determine the optimal clinical management for rare diseases presents significant challenges. We introduce the Medical Action Ontology (MAxO), an ontology specifically designed to organize medical procedures, therapies, and interventions. METHODS: MAxO incorporates logical structures that link MAxO terms to numerous other ontologies within the OBO Foundry. Term development involves a blend of manual and semi-automated processes. Additionally, we have generated annotations detailing diagnostic modalities for specific phenotypic abnormalities defined by the Human Phenotype Ontology (HPO). We introduce a web application, POET, that facilitates MAxO annotations for specific medical actions for diseases using the Mondo Disease Ontology. FINDINGS: MAxO encompasses 1,757 terms spanning a wide range of biomedical domains, from human anatomy and investigations to the chemical and protein entities involved in biological processes. These terms annotate phenotypic features associated with specific disease (using HPO and Mondo). Presently, there are over 16,000 MAxO diagnostic annotations that target HPO terms. Through POET, we have created 413 MAxO annotations specifying treatments for 189 rare diseases. CONCLUSIONS: MAxO offers a computational representation of treatments and other actions taken for the clinical management of patients. Its development is closely coupled to Mondo and HPO, broadening the scope of our computational modeling of diseases and phenotypic features. We invite the community to contribute disease annotations using POET (https://poet.jax.org/). MAxO is available under the open-source CC-BY 4.0 license (https://github.com/monarch-initiative/MAxO). FUNDING: NHGRI 1U24HG011449-01A1 and NHGRI 5RM1HG010860-04

Fermi Large Area Telescope Constraints on the Gamma-ray Opacity of the Universe

The Extragalactic Background Light (EBL) includes photons with wavelengths from ultraviolet to infrared, which are effective at attenuating gamma rays with energy above ~10 GeV during propagation from sources at cosmological distances. This results in a redshift- and energy-dependent attenuation of the gamma-ray flux of extragalactic sources such as blazars and Gamma-Ray Bursts (GRBs). The Large Area Telescope onboard Fermi detects a sample of gamma-ray blazars with redshift up to z~3, and GRBs with redshift up to z~4.3. Using photons above 10 GeV collected by Fermi over more than one year of observations for these sources, we investigate the effect of gamma-ray flux attenuation by the EBL. We place upper limits on the gamma-ray opacity of the Universe at various energies and redshifts, and compare this with predictions from well-known EBL models. We find that an EBL intensity in the optical-ultraviolet wavelengths as great as predicted by the "baseline" model of Stecker et al. (2006) can be ruled out with high confidence.Comment: 42 pages, 12 figures, accepted version (24 Aug.2010) for publication in ApJ; Contact authors: A. Bouvier, A. Chen, S. Raino, S. Razzaque, A. Reimer, L.C. Reye

A population of gamma-ray emitting globular clusters seen with the Fermi Large Area Telescope

Globular clusters with their large populations of millisecond pulsars (MSPs) are believed to be potential emitters of high-energy gamma-ray emission. Our goal is to constrain the millisecond pulsar populations in globular clusters from analysis of gamma-ray observations. We use 546 days of continuous sky-survey observations obtained with the Large Area Telescope aboard the Fermi Gamma-ray Space Telescope to study the gamma-ray emission towards 13 globular clusters. Steady point-like high-energy gamma-ray emission has been significantly detected towards 8 globular clusters. Five of them (47 Tucanae, Omega Cen, NGC 6388, Terzan 5, and M 28) show hard spectral power indices $(0.7 < \Gamma <1.4)$ and clear evidence for an exponential cut-off in the range 1.0-2.6 GeV, which is the characteristic signature of magnetospheric emission from MSPs. Three of them (M 62, NGC 6440 and NGC 6652) also show hard spectral indices $(1.0 < \Gamma < 1.7)$, however the presence of an exponential cut-off can not be unambiguously established. Three of them (Omega Cen, NGC 6388, NGC 6652) have no known radio or X-ray MSPs yet still exhibit MSP spectral properties. From the observed gamma-ray luminosities, we estimate the total number of MSPs that is expected to be present in these globular clusters. We show that our estimates of the MSP population correlate with the stellar encounter rate and we estimate 2600-4700 MSPs in Galactic globular clusters, commensurate with previous estimates. The observation of high-energy gamma-ray emission from a globular cluster thus provides a reliable independent method to assess their millisecond pulsar populations that can be used to make constraints on the original neutron star X-ray binary population, essential for understanding the importance of binary systems in slowing the inevitable core collapse of globular clusters.Comment: Accepted for publication in A&A. Corresponding authors: J. Kn\"odlseder, N. Webb, B. Pancraz

Gammaherpesvirus Latency Accentuates EAE Pathogenesis: Relevance to Epstein-Barr Virus and Multiple Sclerosis

Epstein-Barr virus (EBV) has been identified as a putative environmental trigger of multiple sclerosis (MS), yet EBV's role in MS remains elusive. We utilized murine gamma herpesvirus 68 (γHV-68), the murine homolog to EBV, to examine how infection by a virus like EBV could enhance CNS autoimmunity. Mice latently infected with γHV-68 developed more severe EAE including heightened paralysis and mortality. Similar to MS, γHV-68EAE mice developed lesions composed of CD4 and CD8 T cells, macrophages and loss of myelin in the brain and spinal cord. Further, T cells from the CNS of γHV-68 EAE mice were primarily Th1, producing heightened levels of IFN-γ and T-bet accompanied by IL-17 suppression, whereas a Th17 response was observed in uninfected EAE mice. Clearly, γHV-68 latency polarizes the adaptive immune response, directs a heightened CNS pathology following EAE induction reminiscent of human MS and portrays a novel mechanism by which EBV likely influences MS and other autoimmune diseases