Modeling and integration of N-glycan biomarkers in a comprehensive biomarker data model.

Molecular biomarkers measure discrete components of biological processes that can contribute to disorders when impaired. Great interest exists in discovering early cancer biomarkers to improve outcomes. Biomarkers represented in a standardized data model, integrated with multi-omics data, may improve the understanding and use of novel biomarkers such as glycans and glycoconjugates. Among altered components in tumorigenesis, N-glycans exhibit substantial biomarker potential, when analyzed with their protein carriers. However, such data are distributed across publications and databases of diverse formats, which hamper their use in research and clinical application. Mass spectrometry measures of 50 N-glycans on 7 serum proteins in liver disease were integrated (as a panel) into a cancer biomarker data model, providing a unique identifier, standard nomenclature, links to glycan resources, and accession and ontology annotations to standard protein, gene, disease, and biomarker information. Data provenance was documented with a standardized United States Food and Drug Administration-supported BioCompute Object. Using the biomarker data model allows the capture of granular information, such as glycans with different levels of abundance in cirrhosis, hepatocellular carcinoma, and transplant groups. Such representation in a standardized data model harmonizes glycomics data in a unified framework, making glycan-protein biomarker data exploration more available to investigators and to other data resources. The biomarker data model we describe can be used by researchers to describe their novel glycan and glycoconjugate biomarkers; it can integrate N-glycan biomarker data with multi-source biomedical data and can foster discovery and insight within a unified data framework for glycan biomarker representation, thereby making the data FAIR (Findable, Accessible, Interoperable, Reusable) (https://www.go-fair.org/fair-principles/).

COVID-19 biomarkers and their overlap with comorbidities in a disease biomarker data model.

In response to the COVID-19 outbreak, scientists and medical researchers are capturing a wide range of host responses, symptoms and lingering postrecovery problems within the human population. These variable clinical manifestations suggest differences in influential factors, such as innate and adaptive host immunity, existing or underlying health conditions, comorbidities, genetics and other factors-compounding the complexity of COVID-19 pathobiology and potential biomarkers associated with the disease, as they become available. The heterogeneous data pose challenges for efficient extrapolation of information into clinical applications. We have curated 145 COVID-19 biomarkers by developing a novel cross-cutting disease biomarker data model that allows integration and evaluation of biomarkers in patients with comorbidities. Most biomarkers are related to the immune (SAA, TNF-∝ and IP-10) or coagulation (D-dimer, antithrombin and VWF) cascades, suggesting complex vascular pathobiology of the disease. Furthermore, we observe commonality with established cancer biomarkers (ACE2, IL-6, IL-4 and IL-2) as well as biomarkers for metabolic syndrome and diabetes (CRP, NLR and LDL). We explore these trends as we put forth a COVID-19 biomarker resource (https://data.oncomx.org/covid19) that will help researchers and diagnosticians alike.

Multi-layer framework of identifying placenta related research towards Placenta Curated Research Dataset (PCRD) development for the PAT project.

BACKGROUND: The placenta is a maternal-fetal organ that develops during pregnancy and provides nutrients, oxygen, and removal of waste products to the growing fetus. Better understanding of the placenta promises to help improve health of mothers and children, given its influence on health lasting a lifetime. However, the placenta is poorly understood due to its variability across different species and no live functions available after the baby is delivered. The Placenta Atlas Tool (PAT) project aims to leverage advanced computational approaches to meld numerous and diverse datasets into an integrated resource to encourage a "systems biology" approach for study of both normal and abnormal placental development and function throughout gestation. METHODS: In this study, we introduced a multi-layer framework to automatically identify PAT relevant research from PubMed and develop a Placenta Curated Research Dataset (PCRD) to ultimately support placenta research. This framework functions by multiple well-known Natural Language Processing (NLP) components; including Medical Subject Headings (MeSH) based Naïve Bayes classifier, abstract based text similarity comparison and MeSH based article prioritization to systematically filter out PAT relevant research publications for further data curation. In addition, we developed a user-friendly web application to incorporate human judgement at the final stage of publication identification. RESULTS: We obtained 22,047 articles from PubMed, and programmatically identified 6086 articles that are highly relevant to PAT via our framework. To assess performance of the framework, we manually reviewed a random set of articles by using our web tool. Based on our review, accuracy of article classification is greater than 90% and accuracy of prioritization is greater than 80%. CONCLUSIONS: We developed a multi-layer publication identification framework to systematically identify PAT relevant publications from PubMed. This framework not only demonstrates good performance in identifying placenta related research, but also can be easily extended to support research in other scientific fields.

A Near Fatal Sneeze Spontaneous Splenic Rupture: A Case Report and Review of the Literature.

A 79-year-old female called 911 for abdominal pain in her left upper quadrant with radiation through to her back and left shoulder for three hours. Upon arrival to the emergency department her physical exam was positive only for tenderness in the left upper quadrant of her abdomen. The patient denied any history of trauma but reported she "did sneeze three times" just prior to the onset of her pain. Computed tomography angiography of the abdomen and pelvis was obtained to evaluate for vascular pathology. The radiologist immediately called with concern for splenic laceration. The general surgeon took the patient directly to the operating room where she underwent a splenectomy and recovered without sequelae. This is the first case report of spontaneous splenic rupture that resulted after the act of sneezing. It is important to be aware of this rare clinical entity because early recognition can be life saving.