FAIR assessment of nanosafety data reusability with community standards.

Nanomaterials hold great promise for improving our society, and it is crucial to understand their effects on biological systems in order to enhance their properties and ensure their safety. However, the lack of consistency in experimental reporting, the absence of universally accepted machine-readable metadata standards, and the challenge of combining such standards hamper the reusability of previously produced data for risk assessment. Fortunately, the research community has responded to these challenges by developing minimum reporting standards that address several of these issues. By converting twelve published minimum reporting standards into a machine-readable representation using FAIR maturity indicators, we have created a machine-friendly approach to annotate and assess datasets' reusability according to those standards. Furthermore, our NanoSafety Data Reusability Assessment (NSDRA) framework includes a metadata generator web application that can be integrated into experimental data management, and a new web application that can summarize the reusability of nanosafety datasets for one or more subsets of maturity indicators, tailored to specific computational risk assessment use cases. This approach enhances the transparency, communication, and reusability of experimental data and metadata. With this improved FAIR approach, we can facilitate the reuse of nanosafety research for exploration, toxicity prediction, and regulation, thereby advancing the field and benefiting society as a whole.

A template wizard for the cocreation of machine-readable data-reporting to harmonize the evaluation of (nano)materials.

Making research data findable, accessible, interoperable and reusable (FAIR) is typically hampered by a lack of skills in technical aspects of data management by data generators and a lack of resources. We developed a Template Wizard for researchers to easily create templates suitable for consistently capturing data and metadata from their experiments. The templates are easy to use and enable the compilation of machine-readable metadata to accompany data generation and align them to existing community standards and databases, such as eNanoMapper, streamlining the adoption of the FAIR principles. These templates are citable objects and are available as online tools. The Template Wizard is designed to be user friendly and facilitates using and reusing existing templates for new projects or project extensions. The wizard is accompanied by an online template validator, which allows self-evaluation of the template (to ensure mapping to the data schema and machine readability of the captured data) and transformation by an open-source parser into machine-readable formats, compliant with the FAIR principles. The templates are based on extensive collective experience in nanosafety data collection and include over 60 harmonized data entry templates for physicochemical characterization and hazard assessment (cell viability, genotoxicity, environmental organism dose-response tests, omics), as well as exposure and release studies. The templates are generalizable across fields and have already been extended and adapted for microplastics and advanced materials research. The harmonized templates improve the reliability of interlaboratory comparisons, data reuse and meta-analyses and can facilitate the safety evaluation and regulation process for (nano) materials.

From papers to RDF-based integration of physicochemical data and adverse outcome pathways for nanomaterials.

Adverse Outcome Pathways (AOPs) have been proposed to facilitate mechanistic understanding of interactions of chemicals/materials with biological systems. Each AOP starts with a molecular initiating event (MIE) and possibly ends with adverse outcome(s) (AOs) via a series of key events (KEs). So far, the interaction of engineered nanomaterials (ENMs) with biomolecules, biomembranes, cells, and biological structures, in general, is not yet fully elucidated. There is also a huge lack of information on which AOPs are ENMs-relevant or -specific, despite numerous published data on toxicological endpoints they trigger, such as oxidative stress and inflammation. We propose to integrate related data and knowledge recently collected. Our approach combines the annotation of nanomaterials and their MIEs with ontology annotation to demonstrate how we can then query AOPs and biological pathway information for these materials. We conclude that a FAIR (Findable, Accessible, Interoperable, Reusable) representation of the ENM-MIE knowledge simplifies integration with other knowledge. SCIENTIFIC CONTRIBUTION: This study introduces a new database linking nanomaterial stressors to the first known MIE or KE. Second, it presents a reproducible workflow to analyze and summarize this knowledge. Third, this work extends the use of semantic web technologies to the field of nanoinformatics and nanosafety.

A data reusability assessment in the nanosafety domain based on the NSDRA framework followed by an exploratory quantitative structure activity relationships (QSAR) modeling targeting cellular viability.

INTRODUCTION: The current effort towards the digital transformation across multiple scientific domains requires data that is Findable, Accessible, Interoperable and Reusable (FAIR). In addition to the FAIR data, what is required for the application of computational tools, such as Quantitative Structure Activity Relationships (QSARs), is a sufficient data volume and the ability to merge sources into homogeneous digital assets. In the nanosafety domain there is a lack of FAIR available metadata. METHODOLOGY: To address this challenge, we utilized 34 datasets from the nanosafety domain by exploiting the NanoSafety Data Reusability Assessment (NSDRA) framework, which allowed the annotation and assessment of dataset's reusability. From the framework's application results, eight datasets targeting the same endpoint (i.e. numerical cellular viability) were selected, processed and merged to test several hypothesis including universal versus nanogroup-specific QSAR models (metal oxide and nanotubes), and regression versus classification Machine Learning (ML) algorithms. RESULTS: Universal regression and classification QSARs reached an 0.86 R2 and 0.92 accuracy, respectively, for the test set. Nanogroup-specific regression models reached 0.88 R2 for nanotubes test set followed by metal oxide (0.78). Nanogroup-specific classification models reached 0.99 accuracy for nanotubes test set, followed by metal oxide (0.91). Feature importance revealed different patterns depending on the dataset with common influential features including core size, exposure conditions and toxicological assay. Even in the case where the available experimental knowledge was merged, the models still failed to correctly predict the outputs of an unseen dataset, revealing the cumbersome conundrum of scientific reproducibility in realistic applications of QSAR for nanosafety. To harness the full potential of computational tools and ensure their long-term applications, embracing FAIR data practices is imperative in driving the development of responsible QSAR models. CONCLUSIONS: This study reveals that the digitalization of nanosafety knowledge in a reproducible manner has a long way towards its successful pragmatic implementation. The workflow carried out in the study shows a promising approach to increase the FAIRness across all the elements of computational studies, from dataset's annotation, selection, merging to FAIR modeling reporting. This has significant implications for future research as it provides an example of how to utilize and report different tools available in the nanosafety knowledge system, while increasing the transparency of the results. One of the main benefits of this workflow is that it promotes data sharing and reuse, which is essential for advancing scientific knowledge by making data and metadata FAIR compliant. In addition, the increased transparency and reproducibility of the results can enhance the trustworthiness of the computational findings.

PSnpBind-ML: predicting the effect of binding site mutations on protein-ligand binding affinity.

Protein mutations, especially those which occur in the binding site, play an important role in inter-individual drug response and may alter binding affinity and thus impact the drug's efficacy and side effects. Unfortunately, large-scale experimental screening of ligand-binding against protein variants is still time-consuming and expensive. Alternatively, in silico approaches can play a role in guiding those experiments. Methods ranging from computationally cheaper machine learning (ML) to the more expensive molecular dynamics have been applied to accurately predict the mutation effects. However, these effects have been mostly studied on limited and small datasets, while ideally a large dataset of binding affinity changes due to binding site mutations is needed. In this work, we used the PSnpBind database with six hundred thousand docking experiments to train a machine learning model predicting protein-ligand binding affinity for both wild-type proteins and their variants with a single-point mutation in the binding site. A numerical representation of the protein, binding site, mutation, and ligand information was encoded using 256 features, half of them were manually selected based on domain knowledge. A machine learning approach composed of two regression models is proposed, the first predicting wild-type protein-ligand binding affinity while the second predicting the mutated protein-ligand binding affinity. The best performing models reported an RMSE value within 0.5 [Formula: see text] 0.6 kcal/mol-1 on an independent test set with an R2 value of 0.87 [Formula: see text] 0.90. We report an improvement in the prediction performance compared to several reported models developed for protein-ligand binding affinity prediction. The obtained models can be used as a complementary method in early-stage drug discovery. They can be applied to rapidly obtain a better overview of the ligand binding affinity changes across protein variants carried by people in the population and narrow down the search space where more time-demanding methods can be used to identify potential leads that achieve a better affinity for all protein variants.

PSnpBind: a database of mutated binding site protein-ligand complexes constructed using a multithreaded virtual screening workflow.

A key concept in drug design is how natural variants, especially the ones occurring in the binding site of drug targets, affect the inter-individual drug response and efficacy by altering binding affinity. These effects have been studied on very limited and small datasets while, ideally, a large dataset of binding affinity changes due to binding site single-nucleotide polymorphisms (SNPs) is needed for evaluation. However, to the best of our knowledge, such a dataset does not exist. Thus, a reference dataset of ligands binding affinities to proteins with all their reported binding sites' variants was constructed using a molecular docking approach. Having a large database of protein-ligand complexes covering a wide range of binding pocket mutations and a large small molecules' landscape is of great importance for several types of studies. For example, developing machine learning algorithms to predict protein-ligand affinity or a SNP effect on it requires an extensive amount of data. In this work, we present PSnpBind: A large database of 0.6 million mutated binding site protein-ligand complexes constructed using a multithreaded virtual screening workflow. It provides a web interface to explore and visualize the protein-ligand complexes and a REST API to programmatically access the different aspects of the database contents. PSnpBind is open source and freely available at https://psnpbind.org .

WikiPathways: connecting communities.

WikiPathways (https://www.wikipathways.org) is a biological pathway database known for its collaborative nature and open science approaches. With the core idea of the scientific community developing and curating biological knowledge in pathway models, WikiPathways lowers all barriers for accessing and using its content. Increasingly more content creators, initiatives, projects and tools have started using WikiPathways. Central in this growth and increased use of WikiPathways are the various communities that focus on particular subsets of molecular pathways such as for rare diseases and lipid metabolism. Knowledge from published pathway figures helps prioritize pathway development, using optical character and named entity recognition. We show the growth of WikiPathways over the last three years, highlight the new communities and collaborations of pathway authors and curators, and describe various technologies to connect to external resources and initiatives. The road toward a sustainable, community-driven pathway database goes through integration with other resources such as Wikidata and allowing more use, curation and redistribution of WikiPathways content.

A Semi-Automated Workflow for FAIR Maturity Indicators in the Life Sciences.

Data sharing and reuse are crucial to enhance scientific progress and maximize return of investments in science. Although attitudes are increasingly favorable, data reuse remains difficult due to lack of infrastructures, standards, and policies. The FAIR (findable, accessible, interoperable, reusable) principles aim to provide recommendations to increase data reuse. Because of the broad interpretation of the FAIR principles, maturity indicators are necessary to determine the FAIRness of a dataset. In this work, we propose a reproducible computational workflow to assess data FAIRness in the life sciences. Our implementation follows principles and guidelines recommended by the maturity indicator authoring group and integrates concepts from the literature. In addition, we propose a FAIR balloon plot to summarize and compare dataset FAIRness. We evaluated the feasibility of our method on three real use cases where researchers looked for six datasets to answer their scientific questions. We retrieved information from repositories (ArrayExpress, Gene Expression Omnibus, eNanoMapper, caNanoLab, NanoCommons and ChEMBL), a registry of repositories, and a searchable resource (Google Dataset Search) via application program interfaces (API) wherever possible. With our analysis, we found that the six datasets met the majority of the criteria defined by the maturity indicators, and we showed areas where improvements can easily be reached. We suggest that use of standard schema for metadata and the presence of specific attributes in registries of repositories could increase FAIRness of datasets.

Prediction of symptomatic embolism in infective endocarditis: construction and validation of a risk calculator in a multicenter cohort.

OBJECTIVES: The aim of this study was to develop and validate a simple calculator to quantify the embolic risk (ER) at admission of patients with infective endocarditis. BACKGROUND: Early valve surgery reduces the incidence of embolism in high-risk patients with endocarditis, but the quantification of ER remains challenging. METHODS: From 1,022 consecutive patients presenting with definite diagnoses of infective endocarditis in a multicenter observational cohort study, 847 were randomized into derivation (n = 565) and validation (n = 282) samples. Clinical, microbiological, and echocardiographic data were collected at admission. The primary endpoint was symptomatic embolism that occurred during the 6-month period after the initiation of treatment. The prediction model was developed and validated accounting for competing risks. RESULTS: The 6-month incidence of embolism was similar in the development and validation samples (8.5% in the 2 samples). Six variables were associated with ER and were used to create the calculator: age, diabetes, atrial fibrillation, embolism before antibiotics, vegetation length, and Staphylococcus aureus infection. There was an excellent correlation between the predicted and observed ER in both the development and validation samples. The C-statistics for the development and validation samples were 0.72 and 0.65, respectively. Finally, a significantly higher cumulative incidence of embolic events was observed in patients with high predicted ER in both the development (p < 0.0001) and validation (p < 0.05) samples. CONCLUSIONS: The risk for embolism during infective endocarditis can be quantified at admission using a simple and accurate calculator. It might be useful for facilitating therapeutic decisions.

Respiratory variations of R-wave amplitude in lead II are correlated with stroke volume variations evaluated by transesophageal Doppler echocardiography.

OBJECTIVE: The authors hypothesized that variations in electrocardiographically derived R-wave amplitude might be correlated with mechanical ventilation-induced variations in stroke volume as determined by transesophageal echocardiography. DESIGN: Observational prospective study. SETTING: Single university hospital. PARTICIPANTS: Thirty-four patients undergoing coronary artery bypass surgery. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Respiratory R-wave variations in lead II (ΔRII) were correlated with aortic velocity time integral variations (r = 0.82, p < 0.0001). Respiratory R-wave variations in leads III and aVF and pulse pressure variation also were correlated with aortic velocity time integral variations (r = 0.49, p = 0.015; r = 0.61, p = 0.0016; and r = 0.72, p < 0.0001, respectively). R-wave respiratory variations in lead V(5) were not correlated with aortic velocity time integral variations. ΔRII was correlated with pulse pressure variation (r = 0.71, p < 0.0001). A ΔRII cutoff value of 15% accurately predicted stroke volume variations >15%, with a specificity of 92%, a sensitivity of 86%, a positive likelihood ratio of 11.1, a negative likelihood ratio of 0.15, a positive predictive value of 95%, and a negative predictive value of 80%. CONCLUSIONS: ΔRII is correlated with stroke volume variations as determined by transesophageal echocardiography in mechanically ventilated patients and can identify the stroke volume variation cutoff of 15%, previously determined to be the cutoff for volume responsiveness.

Predicting left ventricular dysfunction after valve repair for mitral regurgitation due to leaflet prolapse: additive value of left ventricular end-systolic dimension to ejection fraction.

AIMS: Left ventricular (LV) dysfunction is the first cause of late mortality after mitral valve surgery. In this retrospective analysis, we studied the association between preoperative echocardiographic LV measures and occurrence of LV dysfunction after mitral valve repair (MVR). METHODS AND RESULTS: Between 1991 and 2009, 335 consecutive patients underwent MVR for severe mitral regurgitation due to leaflet prolapse in our institution. Echocardiography was performed preoperatively and at 10.8 (9.1-12.0) months after surgery in 303 patients who represented the study population. Cardiac events were recorded during follow-up. LV ejection fraction (EF) decreased from 68 ± 9% before surgery to 59 ± 9% post-operatively (P < 0.001). Preoperative EF <64% and LV end-systolic diameter (ESD) ≥ 37 mm were the best cut-off values for the prediction of post-operative LV dysfunction (EF < 50%). On the basis of a combined analysis, the occurrence of post-operative LV dysfunction was 9% when EF was ≥ 64% and LVESD < 37 mm, 21% with EF < 64% or LVESD ≥ 37 mm, and 33% with EF < 64% and LVESD ≥ 37 mm (P for trend < 0.001). The combined variable EF < 64% and LVESD ≥ 37 mm added incremental prognostic value to the multivariable regression model (P = 0.001). CONCLUSION: Simple preoperative echocardiography measures allow the prediction of LV dysfunction after MVR in patients with leaflet prolapse. Patients with preoperative EF ≥ 64% and LVESD < 37 mm incur relatively low risk of post-operative LV dysfunction.