Because of their unique optical properties quantum dots (QDs) have become a preferred system for ultrasensitive detection and imaging. However, since QDs commonly contain Cd and other heavy metals, concerns have been raised regarding their toxicity. QDs are thus commonly synthesized with a ZnS cap structure, and/or coated with polymeric stabilizers. We recently synthesized amphiphilic polymer coated TOPO-PMAT QDs which are highly stable in aqueous environments. The effects of these QDs on viability and stress response in five cell lines of mouse and human origins are reported here. Human and mouse macrophages, and human kidney cells readily internalized these QDs, resulting in modest toxicity. TOPO-PMAT QD exposure was highly correlated with the induction of the stress response protein heme oxygenase-1 (HMOX1). Other stress biomarkers (glutamate cysteine ligase modifier subunit, NAD(P)H, necrosis) were only moderately affected. HMOX1 may thus be a useful biomarker of TOPO-QDOT QD exposure across cell types and species.

Quantum dots (QDs) are engineered semiconductor nanoparticles with unique physicochemical properties that make them potentially useful in clinical, research and industrial settings. However, a growing body of evidence indicates that like other engineered nanomaterials, QDs have the potential to be respiratory hazards, especially in the context of the manufacture of QDs and products containing them, as well as exposures to consumers using these products. The overall goal of this study was to investigate the role of mouse strain in determining susceptibility to QD-induced pulmonary inflammation and toxicity. Male mice from 8 genetically diverse inbred strains (the Collaborative Cross founder strains) were exposed to CdSe–ZnS core–shell QDs stabilized with an amphiphilic polymer. QD treatment resulted in significant increases in the percentage of neutrophils and levels of cytokines present in bronchoalveolar lavage fluid (BALF) obtained from NOD/ShiLtJ and NZO/HlLtJ mice relative to their saline (Sal) treated controls. Cadmium measurements in lung tissue indicated strain-dependent differences in disposition of QDs in the lung. Total glutathione levels in lung tissue were significantly correlated with percent neutrophils in BALF as well as with lung tissue Cd levels. Our findings indicate that QD-induced acute lung inflammation is mouse strain dependent, that it is heritable, and that the choice of mouse strain is an important consideration in planning QD toxicity studies. These data also suggest that formal genetic analyses using additional strains or recombinant inbred strains from these mice could be useful for discovering potential QD-induced inflammation susceptibility loci.

Objective Vascular endothelial growth factors (VEGFs) C and D are biologically rational markers of nodal disease that could improve the accuracy of lung cancer staging. We hypothesized that these biomarkers would improve the ability of positron emission tomography (PET) to predict nodal disease among patients with suspected or confirmed non–small cell lung cancer (NSCLC). Methods A cross-sectional study (2010–2013) was performed of patients prospectively enrolled in a lung nodule biorepository, staged by computed tomography (CT) and PET, and who underwent pathologic nodal evaluation. Enzyme-linked immunosorbent assay was used to measure biomarker levels in plasma from blood drawn before anesthesia. Likelihood ratio testing was used to compare the following logistic regression prediction models: ModelPET, ModelPET/VEGF-C, ModelPET/VEGF-D, and ModelPET/VEGF-C/VEGF-D. To account for 5 planned pairwise comparisons, P values<.01 were considered significant. Results Among 62 patients (median age, 67 years; 48% men; 87% white; and 84% NSCLC), 58% had fluorodeoxyglucose uptake in hilar and/or mediastinal lymph nodes. The prevalence of pathologically confirmed lymph node metastases was 40%. Comparisons of prediction models revealed the following: ModelPET/VEGF-C versus ModelPET (P = .0069), ModelPET/VEGF-D versus ModelPET (P = .1886), ModelPET/VEGF-C/VEGF-D versus ModelPET (P = .0146), ModelPET/VEGF-C/VEGF-D versus ModelPET/VEGF-C (P = .2818), and ModelPET/VEGF-C/VEGF-D versus ModelPET/VEGF-D (P = .0095). In ModelPET/VEGF-C, higher VEGF-C levels were associated with an increased risk of nodal disease (odds ratio, 2.96; 95% confidence interval, 1.26–6.90). Conclusions Plasma levels of VEGF-C complemented the ability of PET to predict nodal disease among patients with suspected or confirmed NSCLC. VEGF-D did not improve prediction.

Background:The availability of high fidelity electronic health record (EHR) data is a hallmark of the learning health care system. Washington State’s Surgical Care Outcomes and Assessment Program (SCOAP) is a network of hospitals participating in quality improvement (QI) registries wherein data are manually abstracted from EHRs. To create the Comparative Effectiveness Research and Translation Network (CERTAIN), we semi-automated SCOAP data abstraction using a centralized federated data model, created a central data repository (CDR), and assessed whether these data could be used as real world evidence for QI and research.Objectives:Describe the validation processes and complexities involved and lessons learned.Methods:Investigators installed a commercial CDR to retrieve and store data from disparate EHRs. Manual and automated abstraction systems were conducted in parallel (10/2012-7/2013) and validated in three phases using the EHR as the gold standard: 1) ingestion, 2) standardization, and 3) concordance of automated versus manually abstracted cases. Information retrieval statistics were calculated.Results:Four unaffiliated health systems provided data. Between 6 and 15 percent of data elements were abstracted: 51 to 86 percent from structured data; the remainder using natural language processing (NLP). In phase 1, data ingestion from 12 out of 20 feeds reached 95 percent accuracy. In phase 2, 55 percent of structured data elements performed with 96 to 100 percent accuracy; NLP with 89 to 91 percent accuracy. In phase 3, concordance ranged from 69 to 89 percent. Information retrieval statistics were consistently above 90 percent.Conclusions:Semi-automated data abstraction may be useful, although raw data collected as a byproduct of health care delivery is not immediately available for use as real world evidence. New approaches to gathering and analyzing extant data are required.

BackgroundProtein kinases and phosphatases regulate protein phosphorylation, a critical means of modulating protein function, stability and localization. The identification of functional networks for protein phosphatases has been slow due to their redundant nature and the lack of large-scale analyses. We hypothesized that a genome-scale analysis of genetic interactions using the Synthetic Genetic Array could reveal protein phosphatase functional networks. We apply this approach to the conserved type 1 protein phosphatase Glc7, which regulates numerous cellular processes in budding yeast.ResultsWe created a novel glc7 catalytic mutant (glc7-E101Q). Phenotypic analysis indicates that this novel allele exhibits slow growth and defects in glucose metabolism but normal cell cycle progression and chromosome segregation. This suggests that glc7-E101Q is a hypomorphic glc7 mutant. Synthetic Genetic Array analysis of glc7-E101Q revealed a broad network of 245 synthetic sick/lethal interactions reflecting that many processes are required when Glc7 function is compromised such as histone modification, chromosome segregation and cytokinesis, nutrient sensing and DNA damage. In addition, mitochondrial activity and inheritance and lipid metabolism were identified as new processes involved in buffering Glc7 function. An interaction network among 95 genes genetically interacting with GLC7 was constructed by integration of genetic and physical interaction data. The obtained network has a modular architecture, and the interconnection among the modules reflects the cooperation of the processes buffering Glc7 function.ConclusionWe found 245 genes required for the normal growth of the glc7-E101Q mutant. Functional grouping of these genes and analysis of their physical and genetic interaction patterns bring new information on Glc7-regulated processes.