Posttransplant Lymphoproliferative Disease Following Pancreas Transplantation: A 40 Year Single-Center Experience.

BACKGROUND: Chronic immunosuppression following pancreas transplantation carries significant risk, including posttransplant lymphoproliferative disease (PTLD). We sought to define the incidence, risk factors, and long-term outcomes of PTLD following pancreas transplantation at a single center. METHODS: All adult pancreas transplants between February 1, 1983 and December 31, 2023 at the University of Minnesota were reviewed, including pancreas transplant alone (PTA), simultaneous pancreas-kidney transplants (SPK), and pancreas after kidney transplants (PAK). RESULTS: Among 2353 transplants, 110 cases of PTLD were identified, with an overall incidence of 4.8%. 17.3% were diagnosed within 1 year of transplant, 32.7% were diagnosed within 5 years, and 74 (67.3%) were diagnosed after 5 years. The overall 30-year incidence of PTLD did not differ by transplant type-7.4% for PTA, 14.2% for SPK, and 19.4% for PAK (p = 0.3). In multivariable analyses, older age and Epstein-Barr virus seronegativity were risk factors for PTLD, and PTLD was a risk factor for patient death. PTLD-specific mortality was 32.7%, although recipients with PTLD had similar median posttransplant survival compared to those without PTLD (14.9 year vs. 15.6 year, p = 0.9). CONCLUSIONS: PTLD following pancreas transplantation is associated with significant mortality. Although the incidence of PTLD has decreased over time, a high index of suspicion for PTLD following PTx should remain in EBV-negative recipients.

Utilization of a Novel Scoring System in Predicting 30-day Mortality in Acute Pulmonary Embolism, the CLOT-5 Pilot Study.

OBJECTIVES: To construct a new scoring system utilizing biomarkers, vitals, and imaging data to predict 30-day mortality in acute pulmonary embolism (PE). BACKGROUND: Acute PE, a well-known manifestation of venous thromboembolic disease, is responsible for over 100,000 deaths worldwide yearly. Contemporary management algorithms rely on a multidisciplinary approach to care via PE response teams (PERT) in the identification of low, intermediate, and high-risk patients. The PESI and sPESI scores have been used as cornerstones of the triage process in assigning risk of 30-day mortality for patients presenting with acute PE; however, the specificity of these scoring systems has often come into question. METHODS: This study retrospectively analyzed 488 patients with acute PE who were managed at a tertiary care institution with either conservative therapy consisting of low molecular weight or unfractionated heparin, advanced therapies consisting of catheter directed therapies, aspiration thrombectomy, or a combination of these therapies, or surgical embolectomy. The CLOT-5 score was designed to include vital signs, biomarkers, and imaging data to predict 30-day mortality in patients presenting with acute PE. RESULTS: The CLOT-5 score had an area under the curve (AUC) of 0.901 with a standard error of 0.29, while the PESI and sPESI scores had an AUC and standard errors of 0.793 ±- 0.43 and 0.728 ± 0.55, respectively. CONCLUSIONS: When incorporated into the management algorithms of national PERT programs, the CLOT-5 score may allow for rapid and comprehensive assessment of patients with acute PE at high risk for clinical decompensation, leading to early escalation of care where appropriate.

Evolution of Pancreas Transplantation At A Single Institution-50+ Years and 2,500 Transplants.

OBJECTIVE: To describe the evolution of pancreas transplantation, including improved outcomes and factors associated with improved outcomes over the past five decades. BACKGROUND: The world's first successful pancreas transplant was performed in December 1966 at the University of Minnesota. As new modalities for diabetes treatment mature, we must carefully assess the current state of pancreas transplantation to determine its ongoing role in patient care. METHODS: A single-center retrospective review of 2,500 pancreas transplants performed over >50 years in bivariate and multivariable models. Transplants were divided into six eras; outcomes are presented for the entire cohort and by era. RESULTS: All measures of patient and graft survival improved progressively through the six transplant eras. The overall death censored (DC) pancreas graft half-lives were >35 years for simultaneous pancreas and kidney (SPK), 7.1 years for pancreas after kidney (PAK), and 3.3 years for pancreas transplants alone (PTA). The 10-year DC pancreas graft survival rate in the most recent era was 86.9% for SPK recipients, 58.2% for PAK recipients, and 47.6% for PTA. Overall graft loss was most influenced by patient survival in SPK transplants, whereas graft loss in PAK and PTA recipients was more often due to graft failures. Predictors of improved pancreas graft survival were primary transplants, bladder drainage of exocrine secretions, younger donor age, and shorter preservation time. CONCLUSIONS: Pancreas outcomes have significantly improved over time via sequential, but overlapping, advances in surgical technique, immunosuppressive protocols, reduced preservation time, and the more recent reduction of immune-mediated graft loss.

Towards a standard benchmark for variant and gene prioritisation algorithms: PhEval - Phenotypic inference Evaluation framework.

Background: Computational approaches to support rare disease diagnosis are challenging to build, requiring the integration of complex data types such as ontologies, gene-to-phenotype associations, and cross-species data into variant and gene prioritisation algorithms (VGPAs). However, the performance of VGPAs has been difficult to measure and is impacted by many factors, for example, ontology structure, annotation completeness or changes to the underlying algorithm. Assertions of the capabilities of VGPAs are often not reproducible, in part because there is no standardised, empirical framework and openly available patient data to assess the efficacy of VGPAs - ultimately hindering the development of effective prioritisation tools. Results: In this paper, we present our benchmarking tool, PhEval, which aims to provide a standardised and empirical framework to evaluate phenotype-driven VGPAs. The inclusion of standardised test corpora and test corpus generation tools in the PhEval suite of tools allows open benchmarking and comparison of methods on standardised data sets. Conclusions: PhEval and the standardised test corpora solve the issues of patient data availability and experimental tooling configuration when benchmarking and comparing rare disease VGPAs. By providing standardised data on patient cohorts from real-world case-reports and controlling the configuration of evaluated VGPAs, PhEval enables transparent, portable, comparable and reproducible benchmarking of VGPAs. As these tools are often a key component of many rare disease diagnostic pipelines, a thorough and standardised method of assessment is essential for improving patient diagnosis and care.

Platelet hyperactivity: a comparison of water-soluble, bioactive compound levels in commercial tomato products and water-soluble tomato concentrate, a supplement with an approved EFSA antiplatelet health effect.

The aim of this study was to evaluate and compare the concentration of water-soluble bioactive compounds in tomato products (polyphenols profile, water-soluble vitamins and nucleophilic substances) with the concentration of the same bioactive molecules existing in a water-soluble patented tomato extract, water-soluble tomato extract (WSTC), commercially available as FruitFlow®. This patented tomato extract has been recognised by EFSA (European Food Safety Authority) in a specific Health Claim declaration as having an "Antiplatelet health effect". More than 100 commercial tomato samples, coming from 18 different processing tomato companies worldwide, were analysed and compared with the FruitFlow® supplement. According to the multivariate statistical analyses applied to the data matrix, it is possible to conclude that the commercial tomato products measured (pastes, purees, others) show a significantly higher concentration of water-soluble bioactive molecules (nucleosides/nucleotides and polyphenols) responsible for an anti-platelet aggregation effect than the FruitFlow® dietary supplement.

The Monarch Initiative in 2024: an analytic platform integrating phenotypes, genes and diseases across species.

Bridging the gap between genetic variations, environmental determinants, and phenotypic outcomes is critical for supporting clinical diagnosis and understanding mechanisms of diseases. It requires integrating open data at a global scale. The Monarch Initiative advances these goals by developing open ontologies, semantic data models, and knowledge graphs for translational research. The Monarch App is an integrated platform combining data about genes, phenotypes, and diseases across species. Monarch's APIs enable access to carefully curated datasets and advanced analysis tools that support the understanding and diagnosis of disease for diverse applications such as variant prioritization, deep phenotyping, and patient profile-matching. We have migrated our system into a scalable, cloud-based infrastructure; simplified Monarch's data ingestion and knowledge graph integration systems; enhanced data mapping and integration standards; and developed a new user interface with novel search and graph navigation features. Furthermore, we advanced Monarch's analytic tools by developing a customized plugin for OpenAI's ChatGPT to increase the reliability of its responses about phenotypic data, allowing us to interrogate the knowledge in the Monarch graph using state-of-the-art Large Language Models. The resources of the Monarch Initiative can be found at monarchinitiative.org and its corresponding code repository at github.com/monarch-initiative/monarch-app.

Expression Atlas update: insights from sequencing data at both bulk and single cell level.

Expression Atlas (www.ebi.ac.uk/gxa) and its newest counterpart the Single Cell Expression Atlas (www.ebi.ac.uk/gxa/sc) are EMBL-EBI's knowledgebases for gene and protein expression and localisation in bulk and at single cell level. These resources aim to allow users to investigate their expression in normal tissue (baseline) or in response to perturbations such as disease or changes to genotype (differential) across multiple species. Users are invited to search for genes or metadata terms across species or biological conditions in a standardised consistent interface. Alongside these data, new features in Single Cell Expression Atlas allow users to query metadata through our new cell type wheel search. At the experiment level data can be explored through two types of dimensionality reduction plots, t-distributed Stochastic Neighbor Embedding (tSNE) and Uniform Manifold Approximation and Projection (UMAP), overlaid with either clustering or metadata information to assist users' understanding. Data are also visualised as marker gene heatmaps identifying genes that help confer cluster identity. For some data, additional visualisations are available as interactive cell level anatomograms and cell type gene expression heatmaps.

A guide to the BRAIN Initiative Cell Census Network data ecosystem.

Characterizing cellular diversity at different levels of biological organization and across data modalities is a prerequisite to understanding the function of cell types in the brain. Classification of neurons is also essential to manipulate cell types in controlled ways and to understand their variation and vulnerability in brain disorders. The BRAIN Initiative Cell Census Network (BICCN) is an integrated network of data-generating centers, data archives, and data standards developers, with the goal of systematic multimodal brain cell type profiling and characterization. Emphasis of the BICCN is on the whole mouse brain with demonstration of prototype feasibility for human and nonhuman primate (NHP) brains. Here, we provide a guide to the cellular and spatial approaches employed by the BICCN, and to accessing and using these data and extensive resources, including the BRAIN Cell Data Center (BCDC), which serves to manage and integrate data across the ecosystem. We illustrate the power of the BICCN data ecosystem through vignettes highlighting several BICCN analysis and visualization tools. Finally, we present emerging standards that have been developed or adopted toward Findable, Accessible, Interoperable, and Reusable (FAIR) neuroscience. The combined BICCN ecosystem provides a comprehensive resource for the exploration and analysis of cell types in the brain.

Toward microfluidic integration of respiratory and renal organ support in a single cartridge.

BACKGROUND: Extracorporeal organ assist devices provide lifesaving functions for acutely and chronically ill patients suffering from respiratory and renal failure, but their availability and use is severely limited by an extremely high level of operational complexity. While current hollow fiber-based devices provide high-efficiency blood gas transfer and waste removal in extracorporeal membrane oxygenation (ECMO) and hemodialysis, respectively, their impact on blood health is often highly deleterious and difficult to control. Further challenges are encountered when integrating multiple organ support functions, as is often required when ECMO and ultrafiltration (UF) are combined to deal with fluid overload in critically ill patients, necessitating an unwieldy circuit containing two separate cartridges. METHODS: We report the first laboratory demonstration of simultaneous blood gas oxygenation and fluid removal in single microfluidic circuit, an achievement enabled by the microchannel-based blood flow configuration of the device. Porcine blood is flowed through a stack of two microfluidic layers, one with a non-porous, gas-permeable silicone membrane separating blood and oxygen chambers, and the other containing a porous dialysis membrane separating blood and filtrate compartments. RESULTS: High levels of oxygen transfer are measured across the oxygenator, while tunable rates of fluid removal, governed by the transmembrane pressure (TMP), are achieved across the UF layer. Key parameters including the blood flow rate, TMP and hematocrit are monitored and compared with computationally predicted performance metrics. CONCLUSIONS: These results represent a model demonstration of a potential future clinical therapy where respiratory support and fluid removal are both realized through a single monolithic cartridge.

A Clinical-Scale Microfluidic Respiratory Assist Device with 3D Branching Vascular Networks.

Recent global events such as COVID-19 pandemic amid rising rates of chronic lung diseases highlight the need for safer, simpler, and more available treatments for respiratory failure, with increasing interest in extracorporeal membrane oxygenation (ECMO). A key factor limiting use of this technology is the complexity of the blood circuit, resulting in clotting and bleeding and necessitating treatment in specialized care centers. Microfluidic oxygenators represent a promising potential solution, but have not reached the scale or performance required for comparison with conventional hollow fiber membrane oxygenators (HFMOs). Here the development and demonstration of the first microfluidic respiratory assist device at a clinical scale is reported, demonstrating efficient oxygen transfer at blood flow rates of 750 mL min⁻1 , the highest ever reported for a microfluidic device. The central innovation of this technology is a fully 3D branching network of blood channels mimicking key features of the physiological microcirculation by avoiding anomalous blood flows that lead to thrombus formation and blood damage in conventional oxygenators. Low, stable blood pressure drop, low hemolysis, and consistent oxygen transfer, in 24-hour pilot large animal experiments are demonstrated - a key step toward translation of this technology to the clinic for treatment of a range of lung diseases.

The Ontology of Biological Attributes (OBA)-computational traits for the life sciences.

Existing phenotype ontologies were originally developed to represent phenotypes that manifest as a character state in relation to a wild-type or other reference. However, these do not include the phenotypic trait or attribute categories required for the annotation of genome-wide association studies (GWAS), Quantitative Trait Loci (QTL) mappings or any population-focussed measurable trait data. The integration of trait and biological attribute information with an ever increasing body of chemical, environmental and biological data greatly facilitates computational analyses and it is also highly relevant to biomedical and clinical applications. The Ontology of Biological Attributes (OBA) is a formalised, species-independent collection of interoperable phenotypic trait categories that is intended to fulfil a data integration role. OBA is a standardised representational framework for observable attributes that are characteristics of biological entities, organisms, or parts of organisms. OBA has a modular design which provides several benefits for users and data integrators, including an automated and meaningful classification of trait terms computed on the basis of logical inferences drawn from domain-specific ontologies for cells, anatomical and other relevant entities. The logical axioms in OBA also provide a previously missing bridge that can computationally link Mendelian phenotypes with GWAS and quantitative traits. The term components in OBA provide semantic links and enable knowledge and data integration across specialised research community boundaries, thereby breaking silos.

Formalizing Invertebrate Morphological Data: A Descriptive Model for Cuticle-Based Skeleto-Muscular Systems, an Ontology for Insect Anatomy, and their Potential Applications in Biodiversity Research and Informatics.

The spectacular radiation of insects has produced a stunning diversity of phenotypes. During the past 250 years, research on insect systematics has generated hundreds of terms for naming and comparing them. In its current form, this terminological diversity is presented in natural language and lacks formalization, which prohibits computer-assisted comparison using semantic web technologies. Here we propose a Model for Describing Cuticular Anatomical Structures (MoDCAS) which incorporates structural properties and positional relationships for standardized, consistent, and reproducible descriptions of arthropod phenotypes. We applied the MoDCAS framework in creating the ontology for the Anatomy of the Insect Skeleto-Muscular system (AISM). The AISM is the first general insect ontology that aims to cover all taxa by providing generalized, fully logical, and queryable, definitions for each term. It was built using the Ontology Development Kit (ODK), which maximizes interoperability with Uberon (Uberon multispecies anatomy ontology) and other basic ontologies, enhancing the integration of insect anatomy into the broader biological sciences. A template system for adding new terms, extending, and linking the AISM to additional anatomical, phenotypic, genetic, and chemical ontologies is also introduced. The AISM is proposed as the backbone for taxon-specific insect ontologies and has potential applications spanning systematic biology and biodiversity informatics, allowing users to: 1) use controlled vocabularies and create semiautomated computer-parsable insect morphological descriptions; 2) integrate insect morphology into broader fields of research, including ontology-informed phylogenetic methods, logical homology hypothesis testing, evo-devo studies, and genotype to phenotype mapping; and 3) automate the extraction of morphological data from the literature, enabling the generation of large-scale phenomic data, by facilitating the production and testing of informatic tools able to extract, link, annotate, and process morphological data. This descriptive model and its ontological applications will allow for clear and semantically interoperable integration of arthropod phenotypes in biodiversity studies.

Specimen, biological structure, and spatial ontologies in support of a Human Reference Atlas.

The Human Reference Atlas (HRA) is defined as a comprehensive, three-dimensional (3D) atlas of all the cells in the healthy human body. It is compiled by an international team of experts who develop standard terminologies that they link to 3D reference objects, describing anatomical structures. The third HRA release (v1.2) covers spatial reference data and ontology annotations for 26 organs. Experts access the HRA annotations via spreadsheets and view reference object models in 3D editing tools. This paper introduces the Common Coordinate Framework (CCF) Ontology v2.0.1 that interlinks specimen, biological structure, and spatial data, together with the CCF API that makes the HRA programmatically accessible and interoperable with Linked Open Data (LOD). We detail how real-world user needs and experimental data guide CCF Ontology design and implementation, present CCF Ontology classes and properties together with exemplary usage, and report on validation methods. The CCF Ontology graph database and API are used in the HuBMAP portal, HRA Organ Gallery, and other applications that support data queries across multiple, heterogeneous sources.

Virtual Fly Brain-An interactive atlas of the Drosophila nervous system.

As a model organism, Drosophila is uniquely placed to contribute to our understanding of how brains control complex behavior. Not only does it have complex adaptive behaviors, but also a uniquely powerful genetic toolkit, increasingly complete dense connectomic maps of the central nervous system and a rapidly growing set of transcriptomic profiles of cell types. But this also poses a challenge: Given the massive amounts of available data, how are researchers to Find, Access, Integrate and Reuse (FAIR) relevant data in order to develop an integrated anatomical and molecular picture of circuits, inform hypothesis generation, and find reagents for experiments to test these hypotheses? The Virtual Fly Brain (virtualflybrain.org) web application & API provide a solution to this problem, using FAIR principles to integrate 3D images of neurons and brain regions, connectomics, transcriptomics and reagent expression data covering the whole CNS in both larva and adult. Users can search for neurons, neuroanatomy and reagents by name, location, or connectivity, via text search, clicking on 3D images, search-by-image, and queries by type (e.g., dopaminergic neuron) or properties (e.g., synaptic input in the antennal lobe). Returned results include cross-registered 3D images that can be explored in linked 2D and 3D browsers or downloaded under open licenses, and extensive descriptions of cell types and regions curated from the literature. These solutions are potentially extensible to cover similar atlasing and data integration challenges in vertebrates.

The Ontology of Biological Attributes (OBA) - Computational Traits for the Life Sciences.

Existing phenotype ontologies were originally developed to represent phenotypes that manifest as a character state in relation to a wild-type or other reference. However, these do not include the phenotypic trait or attribute categories required for the annotation of genome-wide association studies (GWAS), Quantitative Trait Loci (QTL) mappings or any population-focused measurable trait data. Moreover, variations in gene expression in response to environmental disturbances even without any genetic alterations can also be associated with particular biological attributes. The integration of trait and biological attribute information with an ever increasing body of chemical, environmental and biological data greatly facilitates computational analyses and it is also highly relevant to biomedical and clinical applications. The Ontology of Biological Attributes (OBA) is a formalised, species-independent collection of interoperable phenotypic trait categories that is intended to fulfil a data integration role. OBA is a standardised representational framework for observable attributes that are characteristics of biological entities, organisms, or parts of organisms. OBA has a modular design which provides several benefits for users and data integrators, including an automated and meaningful classification of trait terms computed on the basis of logical inferences drawn from domain-specific ontologies for cells, anatomical and other relevant entities. The logical axioms in OBA also provide a previously missing bridge that can computationally link Mendelian phenotypes with GWAS and quantitative traits. The term components in OBA provide semantic links and enable knowledge and data integration across specialised research community boundaries, thereby breaking silos.

Brain Data Standards - A method for building data-driven cell-type ontologies.

Large-scale single-cell 'omics profiling is being used to define a complete catalogue of brain cell types, something that traditional methods struggle with due to the diversity and complexity of the brain. But this poses a problem: How do we organise such a catalogue - providing a standard way to refer to the cell types discovered, linking their classification and properties to supporting data? Cell ontologies provide a partial solution to these problems, but no existing ontology schemas support the definition of cell types by direct reference to supporting data, classification of cell types using classifications derived directly from data, or links from cell types to marker sets along with confidence scores. Here we describe a generally applicable schema that solves these problems and its application in a semi-automated pipeline to build a data-linked extension to the Cell Ontology representing cell types in the Primary Motor Cortex of humans, mice and marmosets. The methods and resulting ontology are designed to be scalable and applicable to similar whole-brain atlases currently in preparation.

COVoc and COVTriage: novel resources to support literature triage.

MOTIVATION: Since early 2020, the coronavirus disease 2019 (COVID-19) pandemic has confronted the biomedical community with an unprecedented challenge. The rapid spread of COVID-19 and ease of transmission seen worldwide is due to increased population flow and international trade. Front-line medical care, treatment research and vaccine development also require rapid and informative interpretation of the literature and COVID-19 data produced around the world, with 177 500 papers published between January 2020 and November 2021, i.e. almost 8500 papers per month. To extract knowledge and enable interoperability across resources, we developed the COVID-19 Vocabulary (COVoc), an application ontology related to the research on this pandemic. The main objective of COVoc development was to enable seamless navigation from biomedical literature to core databases and tools of ELIXIR, a European-wide intergovernmental organization for life sciences. RESULTS: This collaborative work provided data integration into SIB Literature services, an application ontology (COVoc) and a triage service named COVTriage and based on annotation processing to search for COVID-related information across pre-defined aspects with daily updates. Thanks to its interoperability potential, COVoc lends itself to wider applications, hopefully through further connections with other novel COVID-19 ontologies as has been established with Coronavirus Infectious Disease Ontology. AVAILABILITY AND IMPLEMENTATION: The data at https://github.com/EBISPOT/covoc and the service at https://candy.hesge.ch/COVTriage.

The NHGRI-EBI GWAS Catalog: knowledgebase and deposition resource.

The NHGRI-EBI GWAS Catalog (www.ebi.ac.uk/gwas) is a FAIR knowledgebase providing detailed, structured, standardised and interoperable genome-wide association study (GWAS) data to >200 000 users per year from academic research, healthcare and industry. The Catalog contains variant-trait associations and supporting metadata for >45 000 published GWAS across >5000 human traits, and >40 000 full P-value summary statistics datasets. Content is curated from publications or acquired via author submission of prepublication summary statistics through a new submission portal and validation tool. GWAS data volume has vastly increased in recent years. We have updated our software to meet this scaling challenge and to enable rapid release of submitted summary statistics. The scope of the repository has expanded to include additional data types of high interest to the community, including sequencing-based GWAS, gene-based analyses and copy number variation analyses. Community outreach has increased the number of shared datasets from under-represented traits, e.g. cancer, and we continue to contribute to awareness of the lack of population diversity in GWAS. Interoperability of the Catalog has been enhanced through links to other resources including the Polygenic Score Catalog and the International Mouse Phenotyping Consortium, refinements to GWAS trait annotation, and the development of a standard format for GWAS data.