A video-based educational intervention for providers regarding colorectal cancer screening

Methods: Email sent to providers asking them to complete a 7 question survey regarding knowledge and self-reported comfort in screening for colorectal cancer using a shared decision-making approach.https://jdc.jefferson.edu/patientsafetyposters/1045/thumbnail.jp

Estimation of contrast agent bolus arrival delays for improved reproducibility of liver DCE MRI

Delays between contrast agent (CA) arrival at the site of vascular input function (VIF) sampling and the tissue of interest affect dynamic contrast enhanced (DCE) MRI pharmacokinetic modelling. We investigate effects of altering VIF CA bolus arrival delays on liver DCE MRI perfusion parameters, propose an alternative approach to estimating delays and evaluate reproducibility. Thirteen healthy volunteers (28.7  ±  1.9 years, seven males) underwent liver DCE MRI using dual-input single compartment modelling, with reproducibility (n  =  9) measured at 7 days. Effects of VIF CA bolus arrival delays were assessed for arterial and portal venous input functions. Delays were pre-estimated using linear regression, with restricted free modelling around the pre-estimated delay. Perfusion parameters and 7 days reproducibility were compared using this method, freely modelled delays and no delays using one-way ANOVA. Reproducibility was assessed using Bland–Altman analysis of agreement. Maximum percent change relative to parameters obtained using zero delays, were  −31% for portal venous (PV) perfusion, +43% for total liver blood flow (TLBF), +3247% for hepatic arterial (HA) fraction, +150% for mean transit time and  −10% for distribution volume. Differences were demonstrated between the 3 methods for PV perfusion (p  =  0.0085) and HA fraction (p  <  0.0001), but not other parameters. Improved mean differences and Bland–Altman 95% Limits-of-Agreement for reproducibility of PV perfusion (9.3 ml/min/100 g, ±506.1 ml/min/100 g) and TLBF (43.8 ml/min/100 g, ±586.7 ml/min/100 g) were demonstrated using pre-estimated delays with constrained free modelling. CA bolus arrival delays cause profound differences in liver DCE MRI quantification. Pre-estimation of delays with constrained free modelling improved 7 days reproducibility of perfusion parameters in volunteers

Improved hepatic arterial fraction estimation using cardiac output correction of arterial input functions for liver DCE MRI

Liver dynamic contrast enhanced (DCE) MRI pharmacokinetic modelling could be useful in the assessment of diffuse liver disease and focal liver lesions, but is compromised by errors in arterial input function (AIF) sampling. In this study, we apply cardiac output correction to arterial input functions (AIFs) for liver dynamic contrast enhanced (DCE) MRI and investigate the effect on dual-input single compartment hepatic perfusion parameter estimation and reproducibility. Thirteen healthy volunteers (28.7±1.94 years, seven males) underwent liver DCE MRI and cardiac output measurement using aortic root phase contrast MRI (PCMRI), with reproducibility (n=9) measured at seven days. Cardiac output AIF correction was undertaken by constraining the first pass AIF enhancement curve using the indicator-dilution principle. Hepatic perfusion parameters with and without cardiac output AIF correction were compared and seven-day reproducibility assessed. Differences between cardiac output corrected and uncorrected liver DCE MRI portal venous (PV) perfusion (p=0.066), total liver blood flow (TLBF)(p=0.101), hepatic arterial (HA) fraction (p=0.895), mean transit time (MTT)(p=0.646), distribution volume (DV)(p=0.890) were not significantly different. Seven-day corrected HA fraction reproducibility was improved (mean difference 0.3%, Bland-Altman 95% Limits-of-Agreement (BA95%LoA) ±27.9%, Coefficient of Variation (CoV) 61.4% vs 9.3%, ±35.5%, 81.7% respectively without correction). Seven-day uncorrected PV perfusion was also improved (mean difference 9.3 ml/min/100g, BA95%LoA ±506.1 ml/min/100g, CoV 64.1% vs 0.9 ml/min/100g, ±562.8 ml/min/100g, 65.1% respectively with correction) as was uncorrected TLBF(mean difference 43.8 ml/min/100g, BA95%LoA ±586.7 ml/min/100g, CoV 58.3% vs 13.3 ml/min/100g, ±661.5 ml/min/100g, 60.9% respectively with correction). Reproducibility of uncorrected MTT was similar (uncorrected mean difference 2.4s, BA95%LoA ±26.7s, CoV 60.8% uncorrected vs 3.7s, ±27.8s, 62.0% respectively with correction), as was and DV (uncorrected mean difference 14.1%, BA95%LoA ±48.2%, CoV 24.7% vs 10.3%, ±46.0%, 23.9% respectively with correction). Cardiac output AIF correction does not significantly affect the estimation of hepatic perfusion parameters but demonstrates improvements in normal volunteer seven-day HA fraction reproducibility, but deterioration in PV perfusion and TLBF reproducibility. Improved HA fraction reproducibility maybe important as arterialisation of liver perfusion is increased in chronic liver disease and within malignant liver lesions

Molecular footprints of the Holocene retreat of dwarf birch in Britain

© 2014 The Authors. Molecular Ecology Published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited

Radiomics-Based Texture Analysis of Ga-68-DOTATATE Positron Emission Tomography and Computed Tomography Images as a Prognostic Biomarker in Adults With Neuroendocrine Cancers Treated With Lu-177-DOTATATE

Purpose: Neuroendocrine tumors (NET) are rare cancers with variable behavior. A better understanding of prognosis would aid individualized management. The aim of this hypothesis-generating pilot study was to investigate the prognostic potential of tumor heterogeneity and tracer avidity in NET using texture analysis (TA) of 68Ga-DOTATATE positron emission tomography (PET) and non-enhanced computed tomography (CT) performed at baseline in patients treated with 177Lu-DOTATATE. It aims to justify a larger-scale study to evaluate its clinical value. Methods: The pretherapy 68Ga-DOTATATE PET-CT scans of 44 patients with metastatic NET (carcinoid, pancreatic, thyroid, head and neck, catecholamine-secreting, and unknown primary NET) treated with 177Lu-DOTATATE were analyzed retrospectively using commercially available texture analysis research software. Image filtration extracted and enhanced objects of different sizes (fine, medium, coarse), then quantified heterogeneity by statistical and histogram-based parameters (mean intensity, standard deviation, entropy, mean of positive pixels, skewness, and kurtosis). Regions of interest were manually drawn around up to five of the most 68Ga-DOTATATE avid lesions for each patient. 68Gallium uptake on PET was quantified as SUVmax and SUVmean. Associations between imaging and clinical markers with progression-free (PFS) and overall survival (OS) were assessed using univariate Kaplan-Meier analysis. Independence of the significant univariate markers of survival was tested using multivariate Cox regression analysis. Results: Measures of heterogeneity (higher kurtosis, higher entropy, and lower skewness) on coarse-texture scale CT and unfiltered PET images predicted shorter PFS (CT coarse kurtosis: p=0.05, PET entropy: p=0.01, PET skewness: p=0.03) and shorter OS (CT coarse kurtosis: p=0.05, PET entropy: p=0.01, PET skewness p=0.02). Conventional PET parameters such as SUVmax and SUVmean showed trends towards predicting outcome but were not statistically significant. Multivariate analysis identified that CT-TA (coarse kurtosis: HR=2.57, 95% CI=1.22–5.38, p=0.013) independently predicted PFS, and PET-TA (unfiltered skewness: HR=9.05, 95% CI=1.19–68.91, p=0.033) independently predicted OS. Conclusion: These preliminary data generate a hypothesis that radiomic analysis of neuroendocrine cancer on 68Ga-DOTATATE PET-CT may be of prognostic value and a valuable addition to the assessment of patients

Pattern Avoidance in Poset Permutations

We extend the concept of pattern avoidance in permutations on a totally ordered set to pattern avoidance in permutations on partially ordered sets. The number of permutations on $P$ that avoid the pattern $\pi$ is denoted $Av_P(\pi)$. We extend a proof of Simion and Schmidt to show that $Av_P(132) \leq Av_P(123)$ for any poset $P$, and we exactly classify the posets for which equality holds.Comment: 13 pages, 1 figure; v2: corrected typos; v3: corrected typos and improved formatting; v4: to appear in Order; v5: corrected typos; v6: updated author email addresse

The Turbulent Structure of the Arctic Summer Boundary Layer During The Arctic Summer Cloud‐Ocean Study

The mostly ice covered Arctic Ocean is dominated by low‐level liquid‐ or mixed‐phase clouds. Turbulence within stratocumulus is primarily driven by cloud top cooling that induces convective instability. Using a suite of in situ and remote sensing instruments we characterize turbulent mixing in Arctic stratocumulus, and for the first time we estimate profiles of the gradient Richardson number at relatively high resolution in both time (10 min) and altitude (10 m). It is found that the mixing occurs both within the cloud, as expected, and by wind shear instability near the surface. About 75% of the time these two layers are separated by a stably stratified inversion at 100–200 m altitude. Exceptions are associated with low cloud bases that allow the cloud‐driven turbulence to reach the surface. The results imply that turbulent coupling between the surface and the cloud is sporadic or intermittent

Cardiac-induced liver deformation as a measure of liver stiffness using dynamic imaging without magnetization tagging-preclinical proof-of-concept, clinical translation, reproducibility and feasibility in patients with cirrhosis

Purpose: MR elastography and magnetization-tagging use liver stiffness (LS) measurements to diagnose fibrosis but require physical drivers, specialist sequences and post-processing. Here we evaluate non-rigid registration of dynamic two-dimensional cine MRI images to measure cardiac-induced liver deformation (LD) as a measure of LS by (i) assessing preclinical proof-of-concept, (ii) clinical reproducibility and inter-reader variability, (iii) the effects of hepatic hemodynamic changes and (iv) feasibility in patients with cirrhosis. / Methods: Sprague–Dawley rats (n = 21 bile duct ligated (BDL), n = 17 sham-operated controls) and fasted patients with liver cirrhosis (n = 11) and healthy volunteers (HVs, n = 10) underwent spoiled gradient-echo short-axis cardiac cine MRI studies at 9.4 T (rodents) and 3.0 T (humans). LD measurements were obtained from intrahepatic sub-cardiac regions-of-interest close to the diaphragmatic margin. One-week reproducibility and prandial stress induced hemodynamic changes were assessed in healthy volunteers. / Results: Normalized LD was higher in BDL (1.304 ± 0.062) compared with sham-operated rats (1.058 ± 0.045, P = 0.0031). HV seven-day reproducibility Bland–Altman (BA) limits-of-agreement (LoAs) were ± 0.028 a.u. and inter-reader variability BA LoAs were ± 0.030 a.u. Post-prandial LD increases were non-significant (+ 0.0083 ± 0.0076 a.u., P = 0.3028) and uncorrelated with PV flow changes (r = 0.42, p = 0.2219). LD measurements successfully obtained from all patients were not significantly higher in cirrhotics (0.102 ± 0.0099 a.u.) compared with HVs (0.080 ± 0.0063 a.u., P = 0.0847). / Conclusion: Cardiac-induced LD is a conceptually reasonable approach from preclinical studies, measurements demonstrate good reproducibility and inter-reader variability, are less likely to be affected by hepatic hemodynamic changes and are feasible in patients with cirrhosis

The impact of measurement error in modelled ambient particles exposures on health effect estimates in multi-level analysis: a simulation study.

Background: Various spatiotemporal models have been proposed for predicting ambient particulate exposure for inclusion in epidemiological analyses. We investigated the effect of measurement error in the prediction of particulate matter with diameter <10 µm (PM10) and <2.5 µm (PM2.5) concentrations on the estimation of health effects. Methods: We sampled 1,000 small administrative areas in London, United Kingdom, and simulated the “true” underlying daily exposure surfaces for PM10 and PM2.5 for 2009–2013 incorporating temporal variation and spatial covariance informed by the extensive London monitoring network. We added measurement error assessed by comparing measurements at fixed sites and predictions from spatiotemporal land-use regression (LUR) models; dispersion models; models using satellite data and applying machine learning algorithms; and combinations of these methods through generalized additive models. Two health outcomes were simulated to assess whether the bias varies with the effect size. We applied multilevel Poisson regression to simultaneously model the effect of long- and short-term pollutant exposure. For each scenario, we ran 1,000 simulations to assess measurement error impact on health effect estimation. Results: For long-term exposure to particles, we observed bias toward the null, except for traffic PM2.5 for which only LUR underestimated the effect. For short-term exposure, results were variable between exposure models and bias ranged from −11% (underestimate) to 20% (overestimate) for PM10 and of −20% to 17% for PM2.5. Integration of models performed best in almost all cases. Conclusions: No single exposure model performed optimally across scenarios. In most cases, measurement error resulted in attenuation of the effect estimate

Application of a genetic risk score to racially diverse type 1 diabetes populations demonstrates the need for diversity in risk-modeling

This is the final version of the article. Available from the publisher via the DOI in this record.Prior studies identified HLA class-II and 57 additional loci as contributors to genetic susceptibility for type 1 diabetes (T1D). We hypothesized that race and/or ethnicity would be contextually important for evaluating genetic risk markers previously identified from Caucasian/European cohorts. We determined the capacity for a combined genetic risk score (GRS) to discriminate disease-risk subgroups in a racially and ethnically diverse cohort from the southeastern U.S. including 637 T1D patients, 46 at-risk relatives having two or more T1D-related autoantibodies (≥2AAb+), 790 first-degree relatives (≤1AAb+), 68 second-degree relatives (≤1 AAb+), and 405 controls. GRS was higher among Caucasian T1D and at-risk subjects versus ≤ 1AAb+ relatives or controls (P < 0.001). GRS receiver operating characteristic AUC (AUROC) for T1D versus controls was 0.86 (P < 0.001, specificity = 73.9%, sensitivity = 83.3%) among all Caucasian subjects and 0.90 for Hispanic Caucasians (P < 0.001, specificity = 86.5%, sensitivity = 84.4%). Age-at-diagnosis negatively correlated with GRS (P < 0.001) and associated with HLA-DR3/DR4 diplotype. Conversely, GRS was less robust (AUROC = 0.75) and did not correlate with age-of-diagnosis for African Americans. Our findings confirm GRS should be further used in Caucasian populations to assign T1D risk for clinical trials designed for biomarker identification and development of personalized treatment strategies. We also highlight the need to develop a GRS model that accommodates racial diversity.Supported by grants from the National Institutes of Health P01 AI42288 (MAA), R01 DK106191 (TMB), UC4 DK104194 (CEM), and from the JDRF Career Development Award (2–2012–280 to TMB). RAO is supported by a Diabetes UK Harry Keen Fellowship. DJP is supported by the JDRF Postdoctoral Fellowship Award (2-PDF-2016-207-A-N)