Predicting nutrition and environmental factors associated with female reproductive disorders using a knowledge graph and random forests.

OBJECTIVE: Female reproductive disorders (FRDs) are common health conditions that may present with significant symptoms. Diet and environment are potential areas for FRD interventions. We utilized a knowledge graph (KG) method to predict factors associated with common FRDs (for example, endometriosis, ovarian cyst, and uterine fibroids). MATERIALS AND METHODS: We harmonized survey data from the Personalized Environment and Genes Study (PEGS) on internal and external environmental exposures and health conditions with biomedical ontology content. We merged the harmonized data and ontologies with supplemental nutrient and agricultural chemical data to create a KG. We analyzed the KG by embedding edges and applying a random forest for edge prediction to identify variables potentially associated with FRDs. We also conducted logistic regression analysis for comparison. RESULTS: Across 9765 PEGS respondents, the KG analysis resulted in 8535 significant or suggestive predicted links between FRDs and chemicals, phenotypes, and diseases. Amongst these links, 32 were exact matches when compared with the logistic regression results, including comorbidities, medications, foods, and occupational exposures. DISCUSSION: Mechanistic underpinnings of predicted links documented in the literature may support some of our findings. Our KG methods are useful for predicting possible associations in large, survey-based datasets with added information on directionality and magnitude of effect from logistic regression. These results should not be construed as causal but can support hypothesis generation. CONCLUSION: This investigation enabled the generation of hypotheses on a variety of potential links between FRDs and exposures. Future investigations should prospectively evaluate the variables hypothesized to impact FRDs.

Effectiveness of mRNA Booster Vaccine Against Coronavirus Disease 2019 Infection and Severe Outcomes Among Persons With and Without Immune Dysfunction: A Retrospective Cohort Study of National Electronic Medical Record Data in the United States.

Background: Real-world evidence of coronavirus disease 2019 (COVID-19) messenger RNA (mRNA) booster effectiveness among patients with immune dysfunction are limited. Methods: We included data from patients in the United States National COVID Cohort Collaborative (N3C) who completed ≥2 doses of mRNA vaccination between 10 December 2020 and 27 May 2022. Immune dysfunction conditions included human immunodeficiency virus infection, solid organ or bone marrow transplant, autoimmune diseases, and cancer. We defined incident COVID-19 BTI as positive results from laboratory tests or diagnostic codes 14 days after at least 2 doses of mRNA vaccination; and severe COVID-19 BTI as hospitalization, invasive cardiopulmonary support, and/or death. We used propensity scores to match boosted versus nonboosted patients and evaluated hazards of incident and severe COVID-19 BTI using Cox regression after matching. Results: Among patients without immune dysfunction, the relative effectiveness of booster (3 doses) after 6 months from the primary (2 doses) vaccination against BTI ranged from 69% to 81% during the Delta-predominant period and from 33% to 39% during the Omicron-predominant period. Relative effectiveness against BTI was lower among patients with immune dysfunction but remained statistically significant in both periods. Boosted patients had lower risk of COVID-19-related hospitalization (hazard ratios [HR] ranged from 0.5 [95% confidence interval {CI}, .48-.53] to 0.63 [95% CI, .56-.70]), invasive cardiopulmonary support, or death (HRs ranged from 0.46 [95% CI, .41-.52] to 0.63 [95% CI, .50-.79]) during both periods. Conclusions: Booster vaccines remain effective against severe COVID-19 BTI throughout the Delta- and Omicron-predominant periods, regardless of patients' immune status.

Outcomes of SARS-CoV-2 infection among patients with orthopaedic fracture surgery in the National COVID Cohort Collaborative (N3C).

BACKGROUND: The objective of this study was to investigate the outcomes of COVID-19-positive patients undergoing orthopaedic fracture surgery using data from a national database of U.S. adults with a COVID-19 test for SARS-CoV-2. METHODS: This is a retrospective cohort study using data from a national database to compare orthopaedic fracture surgery outcomes between COVID-19-positive and COVID-19-negative patients in the United States. Participants aged 18-99 with orthopaedic fracture surgery between March and December 2020 were included. The main exposure was COVID-19 status. Outcomes included perioperative complications, 30-day all-cause mortality, and overall all-cause mortality. Multivariable adjusted models were fitted to determine the association of COVID-positivity with all-cause mortality. RESULTS: The total population of 6.5 million patient records was queried, identifying 76,697 participants with a fracture. There were 7,628 participants in the National COVID Cohort who had a fracture and operative management. The Charlson Comorbidity Index was higher in the COVID-19-positive group (n = 476, 6.2 %) than the COVID-19-negative group (n = 7,152, 93.8 %) (2.2 vs 1.4, p<0.001). The COVID-19-positive group had higher mortality (13.2 % vs 5.2 %, p<0.001) than the COVID-19-negative group with higher odds of death in the fully adjusted model (Odds Ratio=1.59; 95 % Confidence Interval: 1.16-2.18). CONCLUSION: COVID-19-positive participants with a fracture requiring surgery had higher mortality and perioperative complications than COVID-19-negative patients in this national cohort of U.S. adults tested for COVID-19. The risks associated with COVID-19 can guide potential treatment options and counseling of patients and their families. Future studies can be conducted as data accumulates. LEVEL OF EVIDENCE: Level III.

Predicting nutrition and environmental factors associated with female reproductive disorders using a knowledge graph and random forests.

Objective: Female reproductive disorders (FRDs) are common health conditions that may present with significant symptoms. Diet and environment are potential areas for FRD interventions. We utilized a knowledge graph (KG) method to predict factors associated with common FRDs (e.g., endometriosis, ovarian cyst, and uterine fibroids). Materials and Methods: We harmonized survey data from the Personalized Environment and Genes Study on internal and external environmental exposures and health conditions with biomedical ontology content. We merged the harmonized data and ontologies with supplemental nutrient and agricultural chemical data to create a KG. We analyzed the KG by embedding edges and applying a random forest for edge prediction to identify variables potentially associated with FRDs. We also conducted logistic regression analysis for comparison. Results: Across 9765 PEGS respondents, the KG analysis resulted in 8535 significant predicted links between FRDs and chemicals, phenotypes, and diseases. Amongst these links, 32 were exact matches when compared with the logistic regression results, including comorbidities, medications, foods, and occupational exposures. Discussion: Mechanistic underpinnings of predicted links documented in the literature may support some of our findings. Our KG methods are useful for predicting possible associations in large, survey-based datasets with added information on directionality and magnitude of effect from logistic regression. These results should not be construed as causal, but can support hypothesis generation. Conclusion: This investigation enabled the generation of hypotheses on a variety of potential links between FRDs and exposures. Future investigations should prospectively evaluate the variables hypothesized to impact FRDs.

Phenopacket-tools: Building and validating GA4GH Phenopackets.

The Global Alliance for Genomics and Health (GA4GH) is a standards-setting organization that is developing a suite of coordinated standards for genomics. The GA4GH Phenopacket Schema is a standard for sharing disease and phenotype information that characterizes an individual person or biosample. The Phenopacket Schema is flexible and can represent clinical data for any kind of human disease including rare disease, complex disease, and cancer. It also allows consortia or databases to apply additional constraints to ensure uniform data collection for specific goals. We present phenopacket-tools, an open-source Java library and command-line application for construction, conversion, and validation of phenopackets. Phenopacket-tools simplifies construction of phenopackets by providing concise builders, programmatic shortcuts, and predefined building blocks (ontology classes) for concepts such as anatomical organs, age of onset, biospecimen type, and clinical modifiers. Phenopacket-tools can be used to validate the syntax and semantics of phenopackets as well as to assess adherence to additional user-defined requirements. The documentation includes examples showing how to use the Java library and the command-line tool to create and validate phenopackets. We demonstrate how to create, convert, and validate phenopackets using the library or the command-line application. Source code, API documentation, comprehensive user guide and a tutorial can be found at https://github.com/phenopackets/phenopacket-tools. The library can be installed from the public Maven Central artifact repository and the application is available as a standalone archive. The phenopacket-tools library helps developers implement and standardize the collection and exchange of phenotypic and other clinical data for use in phenotype-driven genomic diagnostics, translational research, and precision medicine applications.

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.

Ankle Fracture and Length of Stay in US Adult Population Using Data From the National COVID Cohort Collaborative.

BACKGROUND: The National COVID Cohort Collaborative (N3C) is an innovative approach to integrate real-world clinical observations into a harmonized database during the time of the COVID-19 pandemic when clinical research on ankle fracture surgery is otherwise mostly limited to expert opinion and research letters. The purpose of this manuscript is to introduce the largest cohort of US ankle fracture surgery patients to date with a comparison between lab-confirmed COVID-19-positive and COVID-19-negative. METHODS: A retrospective cohort of adults with ankle fracture surgery using data from the N3C database with patients undergoing surgery between March 2020 and June 2021. The database is an NIH-funded platform through which the harmonized clinical data from 46 sites is stored. Patient characteristics included body mass index, Charlson Comorbidity Index, and smoking status. Outcomes included 30-day mortality, overall mortality, surgical site infection (SSI), deep SSI, acute kidney injury, pulmonary embolism, deep vein thrombosis, sepsis, time to surgery, and length of stay. COVID-19-positive patients were compared to COVID-19-negative controls to investigate perioperative outcomes during the pandemic. RESULTS: A total population of 8.4 million patient records was queried, identifying 4735 adults with ankle fracture surgery. The COVID-19-positive group (n=158, 3.3%) had significantly longer times to surgery (6.5 ± 6.6 vs 5.1 ± 5.5 days, P = .001) and longer lengths of stay (8.3 ± 23.5 vs 4.3 ± 7.4 days, P < .001), compared to the COVID-19-negative group. The COVID-19-positive group also had a higher rate of 30-day mortality. CONCLUSION: Patients with ankle fracture surgery had longer time to surgery and prolonged hospitalizations in COVID-19-positive patients compared to those who tested negative (average delay was about 1 day and increased length of hospitalization was about 4 days). Few perioperative events were observed in either group. Overall, the risks associated with COVID-19 were measurable but not substantial.Level of Evidence: Level III, retrospective cohort study.

Association Between COVID-19 and Mortality in Hip Fracture Surgery in the National COVID Cohort Collaborative (N3C): A Retrospective Cohort Study.

BACKGROUND: This study investigated the outcomes of coronavirus disease (COVID-19)-positive patients undergoing hip fracture surgery using a national database. METHODS: This is a retrospective cohort study comparing hip fracture surgery outcomes between COVID-19 positive and negative matched cohorts from 46 sites in the United States. Patients aged 65 and older with hip fracture surgery between March 15 and December 31, 2020, were included. The main outcomes were 30-day all-cause mortality and all-cause mortality. RESULTS: In this national study that included 3303 adults with hip fracture surgery, the 30-day mortality was 14.6% with COVID-19-positive versus 3.8% in COVID-19-negative, a notable difference. The all-cause mortality for hip fracture surgery was 27.0% in the COVID-19-positive group during the study period. DICUSSION: We found higher incidence of all-cause mortality in patients with versus without diagnosis of COVID-19 after undergoing hip fracture surgery. The mortality in hip fracture surgery in this national analysis was lower than other local and regional reports. The medical community can use this information to guide the management of hip fracture patients with a diagnosis of COVID-19.

PDX-MI: Minimal Information for Patient-Derived Tumor Xenograft Models.

Patient-derived tumor xenograft (PDX) mouse models have emerged as an important oncology research platform to study tumor evolution, mechanisms of drug response and resistance, and tailoring chemotherapeutic approaches for individual patients. The lack of robust standards for reporting on PDX models has hampered the ability of researchers to find relevant PDX models and associated data. Here we present the PDX models minimal information standard (PDX-MI) for reporting on the generation, quality assurance, and use of PDX models. PDX-MI defines the minimal information for describing the clinical attributes of a patient's tumor, the processes of implantation and passaging of tumors in a host mouse strain, quality assurance methods, and the use of PDX models in cancer research. Adherence to PDX-MI standards will facilitate accurate search results for oncology models and their associated data across distributed repository databases and promote reproducibility in research studies using these models. Cancer Res; 77(21); e62-66. ©2017 AACR.

The Cell Ontology 2016: enhanced content, modularization, and ontology interoperability.

BACKGROUND: The Cell Ontology (CL) is an OBO Foundry candidate ontology covering the domain of canonical, natural biological cell types. Since its inception in 2005, the CL has undergone multiple rounds of revision and expansion, most notably in its representation of hematopoietic cells. For in vivo cells, the CL focuses on vertebrates but provides general classes that can be used for other metazoans, which can be subtyped in species-specific ontologies. CONSTRUCTION AND CONTENT: Recent work on the CL has focused on extending the representation of various cell types, and developing new modules in the CL itself, and in related ontologies in coordination with the CL. For example, the Kidney and Urinary Pathway Ontology was used as a template to populate the CL with additional cell types. In addition, subtypes of the class 'cell in vitro' have received improved definitions and labels to provide for modularity with the representation of cells in the Cell Line Ontology and Reagent Ontology. Recent changes in the ontology development methodology for CL include a switch from OBO to OWL for the primary encoding of the ontology, and an increasing reliance on logical definitions for improved reasoning. UTILITY AND DISCUSSION: The CL is now mandated as a metadata standard for large functional genomics and transcriptomics projects, and is used extensively for annotation, querying, and analyses of cell type specific data in sequencing consortia such as FANTOM5 and ENCODE, as well as for the NIAID ImmPort database and the Cell Image Library. The CL is also a vital component used in the modular construction of other biomedical ontologies-for example, the Gene Ontology and the cross-species anatomy ontology, Uberon, use CL to support the consistent representation of cell types across different levels of anatomical granularity, such as tissues and organs. CONCLUSIONS: The ongoing improvements to the CL make it a valuable resource to both the OBO Foundry community and the wider scientific community, and we continue to experience increased interest in the CL both among developers and within the user community.

Leukaemia inhibitory factor (LIF) is functionally linked to axotrophin and both LIF and axotrophin are linked to regulatory immune tolerance.

Axotrophin (axot) is a newly characterised stem cell gene and mice that lack axotrophin are viable and fertile, but show premature neural degeneration and defective development of the corpus callosum. By comparing axot+/+, axot+/- and axot-/- littermates, we now show that axotrophin is also involved in immune regulation. Both T cell proliferation and T cell-derived leukaemia inhibitory factor (LIF) were suppressed by axotrophin in a gene-dose-dependent manner. Moreover, a role for axotrophin in the feedback regulation of LIF is implicated. This is the first evidence that fate determination mediated by LIF maybe qualified by axotrophin.