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Annotation with widely used, well-structured ontologies, combined with the use of ontology-aware software tools, ensures data and analyses are Findable, Accessible, Interoperable and Reusable (FAIR). Standardized terms with synonyms support lexical search. Ontology structure supports biologically meaningful grouping of annotations (typically by location and type). However, there are significant barriers to the adoption and use of ontologies by researchers and resource developers. One barrier is complexity. Ontologies serving diverse communities are often more complex than needed for individual applications. It is common for atlases to attempt their own simplifications by manually constructing hierarchies of terms linked to ontologies, but these typically include relationship types that are not suitable for grouping annotations. Here, we present a suite of tools for validating user hierarchies against ontology structure, using them to generate graphical reports for discussion and ontology views tailored to the needs of the HuBMAP Human Reference Atlas, and the Human Developmental Cell Atlas. In both cases, validation is a source of corrections and content for both ontologies and user hierarchies.

Phenotypic data are critical for understanding biological mechanisms and consequences of genomic variation, and are pivotal for clinical use cases such as disease diagnostics and treatment development. For over a century, vast quantities of phenotype data have been collected in many different contexts covering a variety of organisms. The emerging field of phenomics focuses on integrating and interpreting these data to inform biological hypotheses. A major impediment in phenomics is the wide range of distinct and disconnected approaches to recording the observable characteristics of an organism. Phenotype data are collected and curated using free text, single terms or combinations of terms, using multiple vocabularies, terminologies, or ontologies. Integrating these heterogeneous and often siloed data enables the application of biological knowledge both within and across species. Existing integration efforts are typically limited to mappings between pairs of terminologies; a generic knowledge representation that captures the full range of cross-species phenomics data is much needed. We have developed the Unified Phenotype Ontology (uPheno) framework, a community effort to provide an integration layer over domain-specific phenotype ontologies, as a single, unified, logical representation. uPheno comprises (1) a system for consistent computational definition of phenotype terms using ontology design patterns, maintained as a community library; (2) a hierarchical vocabulary of species-neutral phenotype terms under which their species-specific counterparts are grouped; and (3) mapping tables between species-specific ontologies. This harmonized representation supports use cases such as cross-species integration of genotype-phenotype associations from different organisms and cross-species informed variant prioritization., Competing Interests: 7.Conflict of Interest None declared.

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.

Rationale: Preoperative patient education through 'joint class' has potential to improve quality of care for total joint replacement (TJR). However, no formal guidance exists regarding curriculum content, potentially resulting in inter-institutional variation., Objective: We aimed to (a) synthesize curriculum components of 'joint classes' across high-volume institutions and (b) develop a preliminary theory of change model for development and evaluation guided by the existing curricula and related literature., Methods: We reviewed 'joint class' curricula from the websites of the 10 highest-volume TJR centres (by average annual 2017-2019 volume) that publicly disclosed this information. Two reviewers qualitatively compared available content and noted common categories, which were synthesized into key domains across institutions. We then reviewed the PubMed database for literature on pre-TJR patient education and education needs in the past 10 years. Drawing on our curriculum synthesis and related literature, we proposed a theory of change model: hypothesized mechanisms through which 'joint class' confers benefits to patients and health systems., Results: We identified 30 categories in our review of existing class content, which we synthesized into seven key domains: (I) Practical Elements, (II) Logistics, (III) Medical Information, (IV) Modifiable Risk Factors, (V) Expected Outcomes, (VI) Patient Role in Recovery and (VII) Enhanced Education. Variation across institutions was noted. Our preliminary model based on the curriculum synthesis and related literature on the impact of 'joint class' includes three levels: (1) Practical Elements ('joint class' accessibility and information quality), (2) Class Goals (increased health literacy, increased adherence, risk mitigation, realistic expectations, and reduced anxiety) and (3) Target Outcomes (improved clinical outcomes, positive patient experience and increased patient satisfaction)., Conclusion: Our synthesis identified core common topics included in pre-TJR education but also highlighted variation across institutions, supporting opportunities for standardization. Clinicians and researchers can use our preliminary model to systematically develop and evaluate 'joint classes,' with the goal of establishing a standard of care for TJR preoperative education., (© 2023 John Wiley & Sons Ltd.)

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., (© The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Respiratory viruses' detection is vitally important in coping with pandemics such as COVID-19. Conventional methods typically require laboratory-based, high-cost equipment. An emerging alternative method is Near-Infrared (NIR) spectroscopy, especially a portable one of the type that has the benefits of low cost, portability, rapidity, ease of use, and mass deployability in both clinical and field settings. One obstacle to its effective application lies in its common limitations, which include relatively low specificity and general quality. Characteristically, the spectra curves show an interweaving feature for the virus-present and virus-absent samples. This then provokes the idea of using machine learning methods to overcome the difficulty. While a subsequent obstacle coincides with the fact that a direct deployment of the machine learning approaches leads to inadequate accuracy of the modelling results. This paper presents a data-driven study on the detection of two common respiratory viruses, the respiratory syncytial virus (RSV) and the Sendai virus (SEV), using a portable NIR spectrometer supported by a machine learning solution enhanced by an algorithm of variable selection via the Variable Importance in Projection (VIP) scores and its Quantile value, along with variable truncation processing, to overcome the obstacles to a certain extent. We conducted extensive experiments with the aid of the specifically developed algorithm of variable selection, using a total of four datasets, achieving classification accuracy of: (1) 0.88, 0.94, and 0.93 for RSV, SEV, and RSV + SEV, respectively, averaged over multiple runs, for the neural network modelling of taking in turn 3 sessions of data for training and the remaining one session of an 'unknown' dataset for testing. (2) the average accuracy of 0.94 (RSV), 0.97 (SEV), and 0.97 (RSV + SEV) for model validation and 0.90 (RSV), 0.93 (SEV), and 0.91 (RSV + SEV) for model testing, using two of the datasets for model training, one for model validation and the other for model testing. These results demonstrate the feasibility of using portable NIR spectroscopy coupled with machine learning to detect respiratory viruses with good accuracy, and the approach could be a viable solution for population screening.

Sepsis is the body's response to an infection. Existing diagnostic testing equipment is not available in primary care settings and requires long waiting times. Lateral flow devices (LFDs) could be employed in point-of-care (POC) settings for sepsis detection; however, they currently lack the required sensitivity. Herein, LFDs are constructed using 150-310 nm sized selenium nanoparticles (SeNPs) and are compared to commercial 40 nm gold nanoparticles (AuNPs) for the detection of the sepsis biomarker interleukin-6 (IL-6). Both 310 and 150 nm SeNPs reported a lower limit of detection (LOD) than 40 nm AuNPs (0.1 ng/mL compared to 1 ng/mL), although at the cost of test line visual intensity. This is to our knowledge the first use of larger SeNPs (>100 nm) in LFDs and the first comparison of the effect of the size of SeNPs on assay sensitivity in this context. The results herein demonstrate that large SeNPs are viable alternatives to existing commercial labels, with the potential for higher sensitivity than standard 40 nm AuNPs., Competing Interests: The authors declare no competing financial interest., (© 2023 The Authors. Published by American Chemical Society.)

Introduction: The AbC-19™ lateral flow immunoassay (LFIA) performance was evaluated on plasma samples from a SARS-CoV-2 vaccination cohort, WHO international standards for anti-SARS-CoV-2 IgG (human), individuals ≥2 weeks from infection of RT-PCR confirmed SARS-CoV-2 genetic variants, as well as microorganism serology., Methods: Pre-vaccination to three weeks post-booster samples were collected from a cohort of 111 patients (including clinically extremely vulnerable patients) from Northern Ireland. All patients received Oxford-AstraZeneca COVID-19 vaccination for the first and second dose, and Pfizer-BioNTech for the third (first booster). WHO international standards, 15 samples from 2 variants of concern (Delta and Omicron) and cross-reactivity with plasma samples from other microorganism infections were also assessed on AbC-19™., Results: All 80 (100%) participants sampled post-booster had high positive IgG responses, compared to 38/95 (40%) participants at 6 months post-first vaccination. WHO standard results correlated with information from corresponding biological data sheets, and antibodies to all genetic variants were detected by LFIA. No cross-reactivity was found with exception of one (of five) Dengue virus samples., Conclusion: These findings suggest BNT162b2 booster vaccination enhanced humoral immunity to SARS-CoV-2 from pre-booster levels, and that this antibody response was detectable by the LFIA. In combination with cross-reactivity, standards and genetic variant results would suggest LFIA may be a cost-effective measure to assess SARS-CoV-2 antibody status., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Professor Tara Moore acted as a consultant for Abingdon Health during the final period of sampling. At time of conception and commencement of this study, none of the authors received payment from Abingdon Health., (© 2022 The Authors. Published by Elsevier Inc.)

Laser-induced graphene (LIG) on paper substrates is a desirable material for single-use point-of-care sensing with its high-quality electrical properties, low fabrication cost, and ease of disposal. While a prior study has shown how the repeated lasing of substrates enables the synthesis of high-quality porous graphitic films, however, the process-property correlation of lasing process on the surface microstructure and electrochemical behavior, including charge-transfer kinetics, is missing. The current study presents a systematic in-depth study on LIG synthesis to elucidate the complex relationship between the surface microstructure and the resulting electroanalytical properties. The observed improvements were then applied to develop high-quality LIG-based electrochemical biosensors for uric acid detection. We show that the optimal paper LIG produced via a dual pass (defocused followed by focused lasing) produces high-quality graphene in terms of crystallinity, sp 2 content, and electrochemical surface area. The highest quality LIG electrodes achieved a high rate constant k 0 of 1.5 × 10 -2 cm s -1 and a significant reduction in charge-transfer resistance (818 Ω compared with 1320 Ω for a commercial glassy carbon electrode). By employing square wave anodic stripping voltammetry and chronoamperometry on a disposable two-electrode paper LIG-based device, the improved charge-transfer kinetics led to enhanced performance for sensing of uric acid with a sensitivity of 24.35 ± 1.55 μA μM -1 and a limit of detection of 41 nM. This study shows how high-quality, sensitive LIG electrodes can be integrated into electrochemical paper analytical devices.

Despite progress in the development of standards for describing and exchanging scientific information, the lack of easy-to-use standards for mapping between different representations of the same or similar objects in different databases poses a major impediment to data integration and interoperability. Mappings often lack the metadata needed to be correctly interpreted and applied. For example, are two terms equivalent or merely related? Are they narrow or broad matches? Or are they associated in some other way? Such relationships between the mapped terms are often not documented, which leads to incorrect assumptions and makes them hard to use in scenarios that require a high degree of precision (such as diagnostics or risk prediction). Furthermore, the lack of descriptions of how mappings were done makes it hard to combine and reconcile mappings, particularly curated and automated ones. We have developed the Simple Standard for Sharing Ontological Mappings (SSSOM) which addresses these problems by: (i) Introducing a machine-readable and extensible vocabulary to describe metadata that makes imprecision, inaccuracy and incompleteness in mappings explicit. (ii) Defining an easy-to-use simple table-based format that can be integrated into existing data science pipelines without the need to parse or query ontologies, and that integrates seamlessly with Linked Data principles. (iii) Implementing open and community-driven collaborative workflows that are designed to evolve the standard continuously to address changing requirements and mapping practices. (iv) Providing reference tools and software libraries for working with the standard. In this paper, we present the SSSOM standard, describe several use cases in detail and survey some of the existing work on standardizing the exchange of mappings, with the goal of making mappings Findable, Accessible, Interoperable and Reusable (FAIR). The SSSOM specification can be found at http://w3id.org/sssom/spec. Database URL: http://w3id.org/sssom/spec., (© The Author(s) 2022. Published by Oxford University Press.)