Towards an Ontology-Based Phenotypic Query Model

Clinical research based on data from patient or study data management systems plays an important role in transferring basic findings into the daily practices of physicians. To support study recruitment, diagnostic processes, and risk factor evaluation, search queries for such management systems can be used. Typically, the query syntax as well as the underlying data structure vary greatly between different data management systems. This makes it difficult for domain experts (e.g., clinicians) to build and execute search queries. In this work, the Core Ontology of Phenotypes is used as a general model for phenotypic knowledge. This knowledge is required to create search queries that determine and classify individuals (e.g., patients or study participants) whose morphology, function, behaviour, or biochemical and physiological properties meet specific phenotype classes. A specific model describing a set of particular phenotype classes is called a Phenotype Specification Ontology. Such an ontology can be automatically converted to search queries on data management systems. The methods described have already been used successfully in several projects. Using ontologies to model phenotypic knowledge on patient or study data management systems is a viable approach. It allows clinicians to model from a domain perspective without knowing the actual data structure or query language

Rheological and biological properties of a hydrogel support for cells intended for intervertebral disc repair

<p>Abstract</p> <p>Background</p> <p>Cell-based approaches towards restoration of prolapsed or degenerated intervertebral discs are hampered by a lack of measures for safe administration and placement of cell suspensions within a treated disc. In order to overcome these risks, a serum albumin-based hydrogel has been developed that polymerizes after injection and anchors the administered cell suspension within the tissue.</p> <p>Methods</p> <p>A hydrogel composed of chemically activated albumin crosslinked by polyethylene glycol spacers was produced. The visco-elastic gel properties were determined by rheological measurement. Human intervertebral disc cells were cultured <it>in vitro </it>and <it>in vivo </it>in the hydrogel and their phenotype was tested by reverse-transcriptase polymerase chain reaction. Matrix production and deposition was monitored by immuno-histology and by biochemical analysis of collagen and glycosaminoglycan deposition. Species specific <it>in situ </it>hybridization was performed to discriminate between cells of human and murine origin in xenotransplants.</p> <p>Results</p> <p>The reproducibility of the gel formation process could be demonstrated. The visco-elastic properties were not influenced by storage of gel components. <it>In vitro </it>and <it>in vivo </it>(subcutaneous implants in mice) evidence is presented for cellular differentiation and matrix deposition within the hydrogel for human intervertebral disc cells even for donor cells that have been expanded in primary monolayer culture, stored in liquid nitrogen and re-activated in secondary monolayer culture. Upon injection into the animals, gels formed spheres that lasted for the duration of the experiments (14 days). The expression of cartilage- and disc-specific mRNAs was maintained in hydrogels <it>in vitro </it>and <it>in vivo</it>, demonstrating the maintenance of a stable specific cellular phenotype, compared to monolayer cells. Significantly higher levels of hyaluronan synthase isozymes-2 and -3 mRNA suggest cell functionalities towards those needed for the support of the regeneration of the intervertebral disc. Moreover, mouse implanted hydrogels accumulated 5 times more glycosaminoglycans and 50 times more collagen than the <it>in vitro </it>cultured gels, the latter instead releasing equivalent quantities of glycosaminoglycans and collagen into the culture medium. Matrix deposition could be specified by immunohistology for collagen types I and II, and aggrecan and was found only in areas where predominantly cells of human origin were detected by species specific <it>in situ </it>hybridization.</p> <p>Conclusions</p> <p>The data demonstrate that the hydrogels form stable implants capable to contain a specifically functional cell population within a physiological environment.</p

Ontological representation, classification and data-driven computing of phenotypes

Background: The successful determination and analysis of phenotypes plays a key role in the diagnostic process, the evaluation of risk factors and the recruitment of participants for clinical and epidemiological studies. The development of computable phenotype algorithms to solve these tasks is a challenging problem, caused by various reasons. Firstly, the term 'phenotype' has no generally agreed definition and its meaning depends on context. Secondly, the phenotypes are most commonly specified as non-computable descriptive documents. Recent attempts have shown that ontologies are a suitable way to handle phenotypes and that they can support clinical research and decision making. The SMITH Consortium is dedicated to rapidly establish an integrative medical informatics framework to provide physicians with the best available data and knowledge and enable innovative use of healthcare data for research and treatment optimisation. In the context of a methodological use case 'phenotype pipeline' (PheP), a technology to automatically generate phenotype classifications and annotations based on electronic health records (EHR) is developed. A large series of phenotype algorithms will be implemented. This implies that for each algorithm a classification scheme and its input variables have to be defined. Furthermore, a phenotype engine is required to evaluate and execute developed algorithms. Results: In this article, we present a Core Ontology of Phenotypes (COP) and the software Phenotype Manager (PhenoMan), which implements a novel ontology-based method to model, classify and compute phenotypes from already available data. Our solution includes an enhanced iterative reasoning process combining classification tasks with mathematical calculations at runtime. The ontology as well as the reasoning method were successfully evaluated with selected phenotypes including SOFA score, socio-economic status, body surface area and WHO BMI classification based on available medical data. Conclusions: We developed a novel ontology-based method to model phenotypes of living beings with the aim of automated phenotype reasoning based on available data. This new approach can be used in clinical context, e.g., for supporting the diagnostic process, evaluating risk factors, and recruiting appropriate participants for clinical and epidemiological studies

Ontological representation, classification and data-driven computing of phenotypes

Background: The successful determination and analysis of phenotypes plays a key role in the diagnostic process, the evaluation of risk factors and the recruitment of participants for clinical and epidemiological studies. The development of computable phenotype algorithms to solve these tasks is a challenging problem, caused by various reasons. Firstly, the term 'phenotype' has no generally agreed definition and its meaning depends on context. Secondly, the phenotypes are most commonly specified as non-computable descriptive documents. Recent attempts have shown that ontologies are a suitable way to handle phenotypes and that they can support clinical research and decision making. The SMITH Consortium is dedicated to rapidly establish an integrative medical informatics framework to provide physicians with the best available data and knowledge and enable innovative use of healthcare data for research and treatment optimisation. In the context of a methodological use case 'phenotype pipeline' (PheP), a technology to automatically generate phenotype classifications and annotations based on electronic health records (EHR) is developed. A large series of phenotype algorithms will be implemented. This implies that for each algorithm a classification scheme and its input variables have to be defined. Furthermore, a phenotype engine is required to evaluate and execute developed algorithms. Results: In this article, we present a Core Ontology of Phenotypes (COP) and the software Phenotype Manager (PhenoMan), which implements a novel ontology-based method to model, classify and compute phenotypes from already available data. Our solution includes an enhanced iterative reasoning process combining classification tasks with mathematical calculations at runtime. The ontology as well as the reasoning method were successfully evaluated with selected phenotypes including SOFA score, socio-economic status, body surface area and WHO BMI classification based on available medical data. Conclusions: We developed a novel ontology-based method to model phenotypes of living beings with the aim of automated phenotype reasoning based on available data. This new approach can be used in clinical context, e.g., for supporting the diagnostic process, evaluating risk factors, and recruiting appropriate participants for clinical and epidemiological studies

Ontological representation, classification and data-driven computing of phenotypes

Background: The successful determination and analysis of phenotypes plays a key role in the diagnostic process, the evaluation of risk factors and the recruitment of participants for clinical and epidemiological studies. The development of computable phenotype algorithms to solve these tasks is a challenging problem, caused by various reasons. Firstly, the term 'phenotype' has no generally agreed definition and its meaning depends on context. Secondly, the phenotypes are most commonly specified as non-computable descriptive documents. Recent attempts have shown that ontologies are a suitable way to handle phenotypes and that they can support clinical research and decision making. The SMITH Consortium is dedicated to rapidly establish an integrative medical informatics framework to provide physicians with the best available data and knowledge and enable innovative use of healthcare data for research and treatment optimisation. In the context of a methodological use case 'phenotype pipeline' (PheP), a technology to automatically generate phenotype classifications and annotations based on electronic health records (EHR) is developed. A large series of phenotype algorithms will be implemented. This implies that for each algorithm a classification scheme and its input variables have to be defined. Furthermore, a phenotype engine is required to evaluate and execute developed algorithms. Results: In this article, we present a Core Ontology of Phenotypes (COP) and the software Phenotype Manager (PhenoMan), which implements a novel ontology-based method to model, classify and compute phenotypes from already available data. Our solution includes an enhanced iterative reasoning process combining classification tasks with mathematical calculations at runtime. The ontology as well as the reasoning method were successfully evaluated with selected phenotypes including SOFA score, socio-economic status, body surface area and WHO BMI classification based on available medical data. Conclusions: We developed a novel ontology-based method to model phenotypes of living beings with the aim of automated phenotype reasoning based on available data. This new approach can be used in clinical context, e.g., for supporting the diagnostic process, evaluating risk factors, and recruiting appropriate participants for clinical and epidemiological studies

Association between Chronic Gingivitis and Cancer: A Retrospective Cohort Study of 19,782 Outpatients from the United Kingdom

Purpose: Recent data argue for the involvement of inflammatory and infectious diseases in cancer development. However, clinical data on the association between chronic gingivitis and cancer have been less conclusive. Here, we systematically evaluated the cancer incidence in a population-based cohort of outpatients with chronic gingivitis from the United Kingdom. Methods: 9891 patients with chronic gingivitis and an identical number of people without gingivitis matched by age, gender, index year, and the Charlson Comorbidity Index were identified from the Disease Analyzer database (IQVIA) between January 2000 and December 2016. Cox regression models were used to study the association between gingivitis and cancer. Results: The probability of cancer was significantly higher among patients with diagnosed chronic gingivitis compared to non-gingivitis individuals (HR = 1.36, 95% CI = 1.15–1.62). In cancer site-stratified analyses, we observed a trend towards higher rates of cancer in almost all cancers (breast cancer, lymphoid system cancer, digestive tract cancers, skin cancer); however, a significant association was only observed for prostate cancer (HR: 3.38; 95% CI: 1.57–7.27). Notably, the largest increase in cancer rates was observed in male patients (HR: 1.46, 95% CI: 1.13–1.89) between 41 and 60 years old (HR: 1.74, 95% CI: 1.30–2.32). Conclusions: Our data suggest that chronic gingivitis represents an important risk factor for the development of cancer. Therefore, in the context of patient dental care, awareness should be raised to refer gingivitis patients to existing screening programs, especially for prostate cancer. Moreover, the consistent treatment of gingivitis could potentially have a positive impact on the morbidity of certain cancers

Towards an Ontology-Based Phenotypic Query Model

Clinical research based on data from patient or study data management systems plays an important role in transferring basic findings into the daily practices of physicians. To support study recruitment, diagnostic processes, and risk factor evaluation, search queries for such management systems can be used. Typically, the query syntax as well as the underlying data structure vary greatly between different data management systems. This makes it difficult for domain experts (e.g., clinicians) to build and execute search queries. In this work, the Core Ontology of Phenotypes is used as a general model for phenotypic knowledge. This knowledge is required to create search queries that determine and classify individuals (e.g., patients or study participants) whose morphology, function, behaviour, or biochemical and physiological properties meet specific phenotype classes. A specific model describing a set of particular phenotype classes is called a Phenotype Specification Ontology. Such an ontology can be automatically converted to search queries on data management systems. The methods described have already been used successfully in several projects. Using ontologies to model phenotypic knowledge on patient or study data management systems is a viable approach. It allows clinicians to model from a domain perspective without knowing the actual data structure or query language

COVID-19 pandemic and families' utilization of well-child clinics and pediatric practices attendance in Germany

Objective!#!The COVID-19 pandemic and the measures implemented to stop the pandemic had a broad impact on our daily lives. Besides work and social life, health care is affected on many levels. In particular, there is concern that attendance in health care programs will drop or hospital admissions will be delayed due to COVID-19-related anxieties, especially in children. Therefore, we compared the number of weekly visits to 78 German pediatric institutions between 2019 and 2020.!##!Results!#!We found no significant differences during the first 10 weeks of the year. However, and importantly, from April, the weekly number of visits was more than 35% lower in 2020 than in 2019 (p = 0.005). In conclusion, the COVID-19 pandemic seems to relate to families´ utilization of outpatient well-child clinics and pediatric practice attendance in Germany