BackgroundExtracellular vesicles (EVs) are important mediators of cell-to-cell communication in healthy and pathological environments. Because EVs are present in a variety of biological fluids and contain molecular signatures of their cell or tissue of origin, they have great diagnostic and prognostic value. The ability of EVs to deliver biologically active proteins, RNAs and lipids to cells has generated interest in developing novel therapeutics. Despite their potential medical use, many of the mechanisms underlying EV biogenesis and secretion remain unknown.MethodsHere, we characterized vesicle secretion across the NCI-60 panel of human cancer cells by nanoparticle tracking analysis. Using CellMiner, the quantity of EVs secreted by each cell line was compared to reference transcriptomics data to identify gene products associated with vesicle secretion.ResultsGene products positively associated with the quantity of exosomal-sized vesicles included vesicular trafficking classes of proteins with Rab GTPase function and sphingolipid metabolism. Positive correlates of larger microvesicle-sized vesicle secretion included gene products involved in cytoskeletal dynamics and exocytosis, as well as Rab GTPase activation. One of the identified targets, CD63, was further evaluated for its role in vesicle secretion. Clustered regularly interspaced short palindromic repeat (CRISPR)/Cas9 knockout of the CD63 gene in HEK293 cells resulted in a decrease in small vesicle secretion, suggesting the importance of CD63 in exosome biogenesis.ConclusionThese observations reveal new insights into genes involved in exosome and microvesicle formation, and may provide a means to distinguish EV sub-populations. This study offers a foundation for further exploration of targets involved in EV biogenesis and secretion.

Clustering is an essential data mining tool that aims to discover inherent cluster structure in data. For most applications, applying clustering is only appropriate when cluster structure is present. As such, the study of clusterability, which evaluates whether data possesses such structure, is an integral part of cluster analysis. However, methods for evaluating clusterability vary radically, making it challenging to select a suitable measure. In this paper, we perform an extensive comparison of measures of clusterability and provide guidelines that clustering users can reference to select suitable measures for their applications.

When studying incidence of pain conditions such as temporomandibular disorders (TMDs), repeated monitoring is needed in prospective cohort studies. However, monitoring methods usually have limitations and, over a period of years, some loss to follow-up is inevitable. The OPPERA prospective cohort study of first-onset TMD screened for symptoms using quarterly questionnaires and examined symptomatic participants to definitively ascertain TMD incidence. During the median 2.8-year observation period, 16% of the 3,263 enrollees completed no follow-up questionnaires, others provided incomplete follow-up, and examinations were not conducted for one third of symptomatic episodes. Although screening methods and examinations were found to have excellent reliability and validity, they were not perfect. Loss to follow-up varied according to some putative TMD risk factors, although multiple imputation to correct the problem suggested that bias was minimal. A second method of multiple imputation that evaluated bias associated with omitted and dubious examinations revealed a slight underestimate of incidence and some small biases in hazard ratios used to quantify effects of risk factors. Although “bottom line” statistical conclusions were not affected, multiply-imputed estimates should be considered when evaluating the large number of risk factors under investigation in the OPPERA study. Perspective These findings support the validity of the OPPERA prospective cohort study for the purpose of investigating the etiology of first-onset TMD, providing the foundation for other papers investigating risk factors hypothesized in the OPPERA project.

Patient-generated health data (PGHD), or health-related data gathered from patients to help address a health concern, are used increasingly in oncology to make regulatory decisions and evaluate quality of care. PGHD include self-reported health and treatment histories, patient-reported outcomes (PROs), and biometric sensor data. Advances in wireless technology, smartphones, and the Internet of Things have facilitated new ways to collect PGHD during clinic visits and in daily life. The goal of the current review was to provide an overview of the current clinical, regulatory, technological, and analytic landscape as it relates to PGHD in oncology research and care. The review begins with a rationale for PGHD as described by the US Food and Drug Administration, the Institute of Medicine, and other regulatory and scientific organizations. The evidence base for clinic-based and remote symptom monitoring using PGHD is described, with an emphasis on PROs. An overview is presented of current approaches to digital phenotyping or device-based, real-time assessment of biometric, behavioral, self-report, and performance data. Analytic opportunities regarding PGHD are envisioned in the context of big data and artificial intelligence in medicine. Finally, challenges and solutions for the integration of PGHD into clinical care are presented. The challenges include electronic medical record integration of PROs and biometric data, analysis of large and complex biometric data sets, and potential clinic workflow redesign. In addition, there is currently more limited evidence for the use of biometric data relative to PROs. Despite these challenges, the potential benefits of PGHD make them increasingly likely to be integrated into oncology research and clinical care.

Birth defects are responsible for a large proportion of disability and infant mortality. Exposure to a variety of pesticides have been linked to increased risk of birth defects. We conducted a case-control study to estimate the associations between a residence-based metric of agricultural pesticide exposure and birth defects. We linked singleton live birth records for 2003-2005 from the North Carolina (NC) State Center for Health Statistics to data from the NC Birth Defects Monitoring Program. Included women had residence at delivery inside NC and infants with gestational ages from 20-44 weeks (n=304,906). Pesticide exposure was assigned using a previously constructed metric, estimating total chemical exposure (pounds of active ingredient) based on crops within 500 meters of maternal residence, specific dates of pregnancy, and chemical application dates based on the planting/harvesting dates of each crop. Logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (CI) for four categories of exposure (<10th, 10-50th, 50-90th, and >90th percentiles) compared to unexposed. Models were adjusted for maternal race, age at delivery, education, marital status, and smoking status. We observed elevated ORs for congenital heart defects and certain structural defects affecting the gastrointestinal, genitourinary and musculoskeletal systems (e.g., OR (95% CI) (highest exposure vs. unexposed) for tracheal esophageal fistula/esophageal atresia = 1.98 (0.69, 5.66), and OR for atrial septal defects: 1.70 (1.34, 2.14)). Our results provide some evidence of associations between residential exposure to agricultural pesticides and several birth defects phenotypes.