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. over different periods of variant predominance are lacking. We sought to analyze the association of adverse pregnancy outcomes with COVID-19 vaccinations in the pre-Delta, Delta, and Omicron SARS-CoV-2 variants' dominant periods (constituting 50% or more of each pregnancy) for pregnant persons in a large, nationally sampled electronic health record repository in the U.S. Our overall analysis included 311,057 pregnant persons from December 2020 to October 2023 at a time when there were approximately 3.6 million births per year. We compared rates of preterm births and stillbirths among pregnant persons who were vaccinated before or during pregnancy to persons vaccinated after pregnancy or those who were not vaccinated. We performed a multivariable Poisson regression with generalized estimated equations to address data site heterogeneity for preterm births and unadjusted exact models for stillbirths, stratified by the dominant variant period. We found lower rates of preterm birth in the majority of modeled periods (adjusted incidence rate ratio [aIRR] range: 0.42 to 0.85; p-value range: <0.001 to 0.06) and lower rates of stillbirth (IRR range: 0.53 to 1.82; p-value range: <0.001 to 0.976) in most periods among those who were vaccinated before or during pregnancy compared to those who were vaccinated after pregnancy or not vaccinated. We largely found no adverse associations between COVID-19 vaccination and preterm birth or stillbirth; these findings reinforce the safety of COVID-19 vaccination during pregnancy and bolster confidence for pregnant persons, providers, and policymakers in the importance of COVID-19 vaccination for this group despite the end of the public health emergency.

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. IPTBM's goal is to attract global partners organized around five fundamental pillars: 1) Institutional Development, 2) Project Implementation, 3) Development and Production, 4) Investment and 5) Regulatory Clusters to address the needs of an international platform of scientists, institutes, universities, commercial enterprises, investors, politicians, and other stakeholders. The IPTBM differs from existing models including CTSA's (US, NIH) technology because of its comprehensive approach to merge education, research, innovation, and development to translate clinical and public health needs into target-oriented and cost-efficient projects.

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. Clinicians performed validation of HIPPS on a subset of episodes. We then generated pregnancy cohorts based on gestational age precision and pregnancy outcomes for assessment of accuracy and comparison of COVID-19 and other characteristics. Results: We identified 628 165 pregnant persons with 816 471 pregnancy episodes, of which 52.3% were live births, 24.4% were other outcomes (stillbirth, ectopic pregnancy, abortions), and 23.3% had unknown outcomes. Clinician validation agreed 98.8% with HIPPS-identified episodes. We were able to estimate start dates within 1 week of precision for 475 433 (58.2%) episodes. 62 540 (7.7%) episodes had incident COVID-19 during pregnancy. Discussion: HIPPS provides measures of support for pregnancy-related variables such as gestational age and pregnancy outcomes based on N3C data. Gestational age precision allows researchers to find time to events with reasonable confidence. Conclusion: We have developed a novel and robust approach for inferring pregnancy episodes and gestational age that addresses data inconsistency and missingness in EHR data.

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.

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. Framingham Heart Study, the opportunity to expand upon the ex vivo human observation has remained limited within the field of translational medicine. We believe that this presents a significant opportunity that merits consideration in both the planning and analysis of large scale observational studies and can contribute greatly to expanding our approaches in translational medicine.

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. Clinicians performed validation of HIPPS on a subset of episodes. We then generated three types of pregnancy cohorts based on the level of precision for gestational aging and pregnancy outcomes for comparison of COVID-19 and other characteristics. Results: We identified 628,165 pregnant persons with 816,471 pregnancy episodes, of which 52.3% were live births, 24.4% were other outcomes (stillbirth, ectopic pregnancy, spontaneous abortions), and 23.3% had unknown outcomes. We were able to estimate start dates within one week of precision for 431,173 (52.8%) episodes. 66,019 (8.1%) episodes had incident COVID-19 during pregnancy. Across varying COVID-19 cohorts, patient characteristics were generally similar though pregnancy outcomes differed. Discussion: HIPPS provides support for pregnancy-related variables based on EHR data for researchers to define pregnancy cohorts. Our approach performed well based on clinician validation. Conclusion: We have developed a novel and robust approach for inferring pregnancy episodes and gestational aging that addresses data inconsistency and missingness in EHR data.

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. METHODS: Synovial fluid samples obtained from children with oligoarticular JIA, polyarticular JIA, or systemic JIA were compared. Two-dimensional gel electrophoresis for protein separation and matrix-assisted laser desorption ionization-time-of-flight mass spectrometry and quadripole time-of-flight mass spectrometry for protein identification were used for this study. Synovial fluid cells were analyzed by polymerase chain reaction (PCR) for the presence of haptoglobin messenger RNA (mRNA). RESULTS: The synovial fluid proteome of the samples was delineated. The majority of proteins showed overexpression in JIA synovial fluid as compared with noninflammatory control samples. There were 24 statistically significantly differentially expressed spots (>2-fold change; P < 0.05) between the subtypes of JIA. PCR analysis revealed haptoglobin mRNA, suggesting that haptoglobin is locally produced in an inflamed joint in JIA. CONCLUSION: Despite the similar histologic appearance of inflamed joints in patients with different subtypes of JIA, there are differences in protein expression according to the subtype of JIA. Haptoglobin is differentially expressed between the subtypes of JIA and is locally produced in an inflamed joint in JIA. Haptoglobin and other differentially expressed proteins may be potential biomarkers in JIA.

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. Individual systems have been developed to meet the needs of particular projects though the need for a generalizable system has been recognized. Increased use of Electronic Medical Record data in translational research will demand generalizing the system for integrating clinical data to support the study of a broad range of human diseases. To ultimately satisfy these needs, we have developed a system to support multiple translational research projects. This system, the Data Warehouse for Translational Research (DW4TR), is based on a light-weight, patient-centric modularly-structured clinical data model and a specimen-centric molecular data model. The temporal relationships of the data are also part of the model. The data are accessed through an interface composed of an Aggregated Biomedical-Information Browser (ABB) and an Individual Subject Information Viewer (ISIV) which target general users. The system was developed to support a breast cancer translational research program and has been extended to support a gynecological disease program. Further extensions of the DW4TR are underway. We believe that the DW4TR will play an important role in translational research across multiple disease types.

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. Here, we recommend test-neutral regulations; increased funding for development and integration of human-based approaches; support for existing initiatives that advance human-based approaches; evaluation of new approaches using human data; establishment of guidelines for procuring human cells and tissues for research; and additional training and educational opportunities in human-based approaches.

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. The resulting network has 25 diagnosis nodes interconnected by 40 arcs. Each arc represents a predicted co-occurrence or null co-occurrence. Model verification involved assessing the robustness of the original network structure after random exclusion of 25%, 50%, and 75% of the pathology report dataset. The structure of the network appears stable as random removal of 75% of the records in the original dataset leaves 81% of the original network intact. Model validation was primarily assessed by review of the breast pathology literature for each arc in the network. Almost all network identified co-occurrences (95%) have been published in the breast pathology literature or were verified by expert opinion. In conclusion, the Bayesian network of breast pathology co-occurrence presented here is both robust with respect to incomplete data and validated by consistency with the breast pathology literature and by expert opinion. Further, the ability to utilize a specific pathology observation to predict multiple co-current pathologies enables exploration of pathology co-occurrence patterns in an intuitive manner that may have broader application in both the breast pathologist clinical community and the breast cancer research community.

Intratumoral T cells, recurrence, and survival in epithelial ovarian cancer.

BACKGROUND: Although tumor-infiltrating T cells have been documented in ovarian carcinoma, a clear association with clinical outcome has not been established. METHODS: We performed immunohistochemical analysis of 186 frozen specimens from advanced-stage ovarian carcinomas to assess the distribution of tumor-infiltrating T cells and conducted outcome analyses. Molecular analyses were performed in some tumors by real-time polymerase chain reaction. RESULTS: CD3+ tumor-infiltrating T cells were detected within tumor-cell islets (intratumoral T cells) in 102 of the 186 tumors (54.8 percent); they were undetectable in 72 tumors (38.7 percent); the remaining 12 tumors (6.5 percent) could not be evaluated. There were significant differences in the distributions of progression-free survival and overall survival according to the presence or absence of intratumoral T cells (P<0.001 for both comparisons). The five-year overall survival rate was 38.0 percent among patients whose tumors contained T cells and 4.5 percent among patients whose tumors contained no T cells in islets. Significant differences in the distributions of progression-free survival and overall survival according to the presence or absence of intratumoral T cells (P<0.001 for both comparisons) were also seen among 74 patients with a complete clinical response after debulking and platinum-based chemotherapy: the five-year overall survival rate was 73.9 percent among patients whose tumors contained T cells and 11.9 percent among patients whose tumors contained no T cells in islets. The presence of intratumoral T cells independently correlated with delayed recurrence or delayed death in multivariate analysis and was associated with increased expression of interferon-gamma, interleukin-2, and lymphocyte-attracting chemokines within the tumor. The absence of intratumoral T cells was associated with increased levels of vascular endothelial growth factor. CONCLUSIONS: The presence of intratumoral T cells correlates with improved clinical outcome in advanced ovarian carcinoma.

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. With the original outcome definition (responders/non-responders), ANN performed better than logistic regression (90% of correct classifications in the training sample vs. 77%). However only 62% of new patients were correctly predicted by ANN for their outcome class. Length of the index episode, psychotic features and suicidal behavior emerged as outcome predictors in both models, while demographic characteristics, personality disorders and concomitant somatic morbidity were pointed to only by ANN analysis. Increase of classes in the outcome field resulted in a more elevated error: 46.4% for three classes, 60.4% for four classes and 70.3% for five classes. Overall, our findings suggest that antidepressant outcome prediction based on clinical variables is poor. The ANN approach is as valid as traditional multivariate techniques for the analysis of psychopharmacology studies. The complex interactions modelled through ANN may eventually be applied at the clinical level for individualized therapy. However, the accuracy of prediction is still far from satisfactory from a clinical point of view.

Systems biology for organotypic cell cultures.

Translating in vitro biological data into actionable information related to human health holds the potential to improve disease treatment and risk assessment of chemical exposures. While genomics has identified regulatory pathways at the cellular level, translation to the organism level requires a multiscale approach accounting for intra-cellular regulation, inter-cellular interaction, and tissue/organ-level effects. Tissue-level effects can now be probed in vitro thanks to recently developed systems of three-dimensional (3D), multicellular, "organotypic" cell cultures, which mimic functional responses of living tissue. However, there remains a knowledge gap regarding interactions across different biological scales, complicating accurate prediction of health outcomes from molecular/genomic data and tissue responses. Systems biology aims at mathematical modeling of complex, non-linear biological systems. We propose to apply a systems biology approach to achieve a computational representation of tissue-level physiological responses by integrating empirical data derived from organotypic culture systems with computational models of intracellular pathways to better predict human responses. Successful implementation of this integrated approach will provide a powerful tool for faster, more accurate and cost-effective screening of potential toxicants and therapeutics. On September 11, 2015, an interdisciplinary group of scientists, engineers, and clinicians gathered for a workshop in Research Triangle Park, North Carolina, to discuss this ambitious goal. Participants represented laboratory-based and computational modeling approaches to pharmacology and toxicology, as well as the pharmaceutical industry, government, non-profits, and academia. Discussions focused on identifying critical system perturbations to model, the computational tools required, and the experimental approaches best suited to generating key data.

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. Primary analysis is through a co-occurrence score based on the Jaccard coefficient. Density maps are used to visualize global co-occurrence. When our co-occurrence analysis is applied to a population stratified by menopausal status, we can successfully identify statistically significant differences in pathology co-occurrence patterns between premenopausal and postmenopausal women. Genomic and proteomic experiments are planned to discover biological mechanisms that may underpin differences seen in pathology patterns between populations.

Opening Pandora's box: clinical data and the study of complex diseases.

Complex diseases have complex phenotypes, and proper diagnosis requires that the analysis take into account the patient's history and exposure to environmental factors, as well as genetic information. Signaling information is one aspect of a grander "biomedical informatics" approach advocated for a better understanding of a patient's medically relevant disease phenotype.

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.

Biomedical informatics: the future for drug development.

The problems that exist in drug development are well documented: the limited number of new chemical entities, increased cost of drug development, problems in clinical trials (Phase III), product launches that result in withdrawal, and pressure to reduce the cost of pharmaceuticals from the government. It appears that the promise of genomics has not yet reached its full potential to impact the process. This review identifies the need to develop and implement the area of biomedical informatics for increased success in drug development and healthcare in general.

Microarray data simulator for improved selection of differentially expressed genes.

The development of microarray technology has allowed researchers to measure expression levels of thousands of genes simultaneously. Analysis of these data requires the best normalization and statistical approaches to account for the biological and technical variability inherent in the technique. To approach this problem we have developed a publicly available simulator of microarray hybridization experiments that can be used to help assess the accuracy of bioinformatic tools in discovering significant genes. After analyzing microarray hybridization experiments from over 50 samples, an estimate of various degrees of technical and biological variability was obtained. This information was used to develop a simulator of microarray hybridization data which modeled "normal tissue samples" and "diseased tissue samples" with known, defined, changes in gene expression (a "gold standard"). The data derived from the simulator were then used to evaluate the true positive and false negative rates of several normalization procedures and gene selection techniques. We found that the type of normalization approach used was an important aspect of data analysis. Global normalization was the least accurate approach. Evaluation of gene selection techniques showed that "Significance analysis of microarrays" (SAM) and "Patterns of Gene Expression" (PaGE) were more accurate than simple t-test analysis. We provide access to the microarray hybridization simulator as a public resource for biologists to further test new emerging genomic bioinfomatic tools.

Mycophenolate mofetil versus azathioprine therapy is associated with a significant protection against long-term renal allograft function deterioration.

BACKGROUND: To evaluate the association of long-term continuous mycophenolate mofetil (MMF) versus azathioprine (AZA) therapy and renal allograft function, as measured by the slope of reciprocal creatinine, we analyzed 49,666 primary renal allograft recipients reported to the United States Renal Data System between October 31, 1988 and June 30, 1998. METHODS: The primary study endpoint was defined as a greater than 20% decrease below a 6-month baseline of 1/serum creatinine (SCr) (slope of reciprocal creatinine) at or beyond 1 year after transplantation. A secondary endpoint was defined as reaching an SCr value greater than 1.6 mg/dL. Univariate Kaplan-Meier analysis and multivariate Cox proportional hazard models were used to investigate the risk of reaching the study endpoints. Multivariate analyses were corrected for potential confounding covariates. RESULTS: According to the Cox proportional hazard model, 12-month continued therapy of MMF versus AZA was associated with a protective effect against declining renal function, as measured by the slope of reciprocal creatinine (relative risk [RR]=0.84, confidence interval 0.78-0.91, P<0.001). For 24-month continued therapy of MMF versus AZA, MMF was associated with a further decreased risk for a decline in renal function (RR=0.66, confidence interval=0.57-0.77, P<0.001). Furthermore, MMF was associated with a protective effect against reaching the SCr threshold of 1.6 mg/dL (RR=0.80, P<0.001) beyond 12 months posttransplantation. CONCLUSIONS: Continuous use of MMF versus AZA was associated with a protective effect against declining renal function beyond 1 year after transplantation. Further study is needed to confirm that continued MMF therapy is protective against long-term deterioration in renal function.