Pregnancy Outcomes among Pregnant Persons after COVID-19 Vaccination: Assessing Vaccine Safety in Retrospective Cohort Analysis of U.S. National COVID Cohort Collaborative (N3C).

COVID-19 vaccines have been shown to be effective in preventing severe illness, including among pregnant persons. The vaccines appear to be safe in pregnancy, supporting a continuously favorable overall risk/benefit profile, though supportive data for the U.S.

Who is pregnant? Defining real-world data-based pregnancy episodes in the National COVID Cohort Collaborative (N3C).

Objectives: To define pregnancy episodes and estimate gestational age within electronic health record (EHR) data from the National COVID Cohort Collaborative (N3C).

Materials And Methods: We developed a comprehensive approach, named Hierarchy and rule-based pregnancy episode Inference integrated with Pregnancy Progression Signatures (HIPPS), and applied it to EHR data in the N3C (January 1, 2018-April 7, 2022). HIPPS combines: (1) an extension of a previously published pregnancy episode algorithm, (2) a novel algorithm to detect gestational age-specific signatures of a progressing pregnancy for further episode support, and (3) pregnancy start date inference.

Who is pregnant? defining real-world data-based pregnancy episodes in the National COVID Cohort Collaborative (N3C).

Objective: To define pregnancy episodes and estimate gestational aging within electronic health record (EHR) data from the National COVID Cohort Collaborative (N3C).

Materials And Methods: We developed a comprehensive approach, named H ierarchy and rule-based pregnancy episode I nference integrated with P regnancy P rogression S ignatures (HIPPS) and applied it to EHR data in the N3C from 1 January 2018 to 7 April 2022. HIPPS combines: 1) an extension of a previously published pregnancy episode algorithm, 2) a novel algorithm to detect gestational aging-specific signatures of a progressing pregnancy for further episode support, and 3) pregnancy start date inference.

Shifting the Paradigm: The Dress-COV Telegram Bot as a Tool for Participatory Medicine.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic management is limited by great uncertainty, for both health systems and citizens. Facing this information gap requires a paradigm shift from traditional approaches to healthcare to the participatory model of improving health. This work describes the design and function of the Doing Risk sElf-assessment and Social health Support for COVID (Dress-COV) system.

Advancing nonclinical innovation and safety in pharmaceutical testing.

Nonclinical tests are considered crucial for understanding the safety of investigational medicines. However, the effective translation from nonclinical to human application is limited and must be improved. Drug development stakeholders are working to advance human-based in vitro and in silico methods that may be more predictive of human efficacy and safety in vivo because they enable scientists to model the direct interaction of drugs with human cells, tissues, and biological processes.

Bridging the gap between translational medicine and unmet clinical needs.

To date, the actual rate of successful translation has been extremely low although those few successes have been notable and provide for continued and expanding enthusiasm and support. This paper examines whether the fundamental premise may be flawed. Could the success rate be improved to further enhance quality of life and cost optimization for patients by changing the paradigm to "bedside to bench to bedside", and focusing the research on addressing unmet clinical needs? It examines all aspects of the healthcare ecosystem to understand issues that arise with real world patients and in real world clinical practice and how addressing these should be the focus of translational research.

Company Profile: Strategic Medicine, Inc. and Strategic Medicine, BV.

Strategic Medicine, BV (The Hague, The Netherlands) and Strategic Medicine, Inc. (PA, USA) deliver products and services to therapeutic and diagnostic companies, healthcare providers and payers and the investment community based on unique methodologies for modeling disease processes, from predisease through diagnosis, disease and patient stratification and outcome. Strategic Medicine, Inc.

The economics of biobanking and pharmacogenetics databasing: the case of an adaptive platform on breast cancer.

This paper aims to discuss the economics of biobanking. Among the critical issues in evaluating potential ROI for creation of a bio-bank are: scale (e.g.

The international effort: building the bridge for Translational Medicine: Report of the 1st International Conference of Translational Medicine (ICTM).

Background: Supported by the International Society for Translational Medicine (ISTM), Wenzhou Medical College and the First Affiliated Hospital of Wenzhou Medical College, the International Conference on Translational Medicine (ICTM) was held on October 22-23, 2011 in Wenzhou, China. Nearly 800 registrants attended the meeting, primarily representing institutes and hospitals in Europe, The United States of America, And Asia, and China. The meeting was chaired and organized by Dr.

A global resource to translational medicine: the International Park of Translational Medicine and BioMedicine (IPTBM).

Translational science consists of research and development that integrates multiple resources to expedite the successful treatment of disease. The International Park of Translational BioMedicine (IPTBM) is currently being developed within the interface between Zhejiang Province and Shanghai Municipality. IPTBM has been designed to pioneer comprehensive biomedical research that spans the continuum from the education of young scientists to providing the infrastructure necessary for clinical testing and direct observation to better understand human biology while promoting viable commercial results within a vibrant biotechnology community.

Computational modeling and epidemiologic approaches: a new section of the Journal of Translational Medicine.

A new section of the Journal of Translational Medicine is being introduced to encourage rapid communication of methods and results that utilize computational modeling and epidemiologic approaches in translational medicine. The focus will be on population-based studies that extend towards more molecular level analysis. Submission of studies involving methods development is encouraged where actual application and results can be shown in the healthcare and life sciences domains.

QAIT: a quality assurance issue tracking tool to facilitate the improvement of clinical data quality.

In clinical and translational research as well as clinical trial projects, clinical data collection is prone to errors such as missing data, and misinterpretation or inconsistency of the data. A good quality assurance (QA) program can resolve many such errors though this requires efficient communications between the QA staff and data collectors. Managing such communications is critical to resolving QA problems but imposes a major challenge for a project involving multiple clinical and data processing sites.

Expanding the perspective of translational medicine: the value of observational data.

In 2003, the Journal of Translational Medicine was launched to foster the publication of high quality research in both "bench-to-bedside" as well as ex vivo human observation. In spite of the success of several large-scale observational studies, e.g.

DW4TR: A Data Warehouse for Translational Research.

The linkage between the clinical and laboratory research domains is a key issue in translational research. Integration of clinicopathologic data alone is a major task given the number of data elements involved. For a translational research environment, it is critical to make these data usable at the point-of-need.

Synovial fluid proteins differentiate between the subtypes of juvenile idiopathic arthritis.

Objective: Juvenile idiopathic arthritis (JIA) is a heterogeneous group of inflammatory diseases, and no clinically useful prognostic markers to predict disease outcome in children with JIA are currently available. Synovial fluid likely reflects the proteins present in the inflamed synovium. The purpose of this study was to delineate the synovial fluid proteome and determine whether protein expression differs in the different subtypes of JIA.

An issue tracking system to facilitate the enhancement of clinical data quality in the clinical breast care project.

An online issue tracking (QAIT) system was developed to support the QA of questionnaire-based clinical data and tissue banking in the Clinical Breast Care Project (CBCP). The web-based system provides a centralized storage and management of QA issues and role-based access to related information and functions via internet. The QAIT system greatly improved the QA process for the CBCP clinical data and tissue banking and can be easily adapted to other applications.

A Bayesian derived network of breast pathology co-occurrence.

In this paper, we present the validation and verification of a machine-learning based Bayesian network of breast pathology co-occurrence. The present/not present occurrences of 29 common breast pathologies from 1631 pathology reports were used to build the network. All pathology reports were developed by a single pathologist.

Clinical prediction of antidepressant response in mood disorders: linear multivariate vs. neural network models.

Predicting the outcome of antidepressant treatment by pre-treatment features would be of great usefulness for clinicians as up to 50% of major depressives may not have a satisfactory response in spite of adequate trials of antidepressant drugs. In the present article we compared a linear multivariate model of predictors with a few artificial neural network (ANN) models differing from one another by outcome definition and validation procedure. The sample consisted of a reanalysis of 116 inpatients with a major depressive episode included in a 6-week open-label trial with fluvoxamine.

Detecting outlier microarray arrays by correlation and percentage of outliers spots.

We developed a quality assurance (QA) tool, namely microarray outlier filter (MOF), and have applied it to our microarray datasets for the identification of problematic arrays. Our approach is based on the comparison of the arrays using the correlation coefficient and the number of outlier spots generated on each array to reveal outlier arrays. For a human universal reference (HUR) dataset, which is used as a technical control in our standard hybridization procedure, 3 outlier arrays were identified out of 35 experiments.

Co-occurrence analysis for discovery of novel breast cancer pathology patterns.

To discover novel patterns in pathology co-occurrence, we have developed algorithms to analyze and visualize pathology co-occurrence. With access to a database of pathology reports, collected under a single protocol and reviewed by a single pathologist, we can conduct an analysis greater in its scope than previous studies looking at breast pathology co-occurrence. Because this data set is unique, specialized methods for pathology co-occurrence analysis and visualization are developed.

Analysis of metabolic and regulatory pathways through Gene Ontology-derived semantic similarity measures.

This study investigates the feasibility of applying Gene Ontology (GO)-derived semantic similarity methods to the biological pathway analysis. The results derived from the analysis of human metabolic and regulatory pathways are consistent with the network biology. It suggests that the semantic similarity measurement may be used to help the pathway modeling.

Assessing semantic similarity measures for the characterization of human regulatory pathways.

Motivation: Pathway modeling requires the integration of multiple data including prior knowledge. In this study, we quantitatively assess the application of Gene Ontology (GO)-derived similarity measures for the characterization of direct and indirect interactions within human regulatory pathways. The characterization would help the integration of prior pathway knowledge for the modeling.

Genomic instability in histologically normal breast tissues: implications for carcinogenesis.

Breast cancer is an important contributor to morbidity and mortality in society, but factors that affect the cause of the disease are poorly defined. Genomic instability drives tumorigenic processes in invasive carcinomas and premalignant breast lesions, and might promote the accumulation of genetic alterations in apparently normal tissues before histological abnormalities are detectable. Evidence suggests that genomic changes in breast parenchyma affect the behaviour of epithelial cells, and ultimately might affect tumour growth and progression.