Background The contribution of intimal hyperplasia (IH) to arteriovenous fistula (AVF) failure is uncertain. This observational study assessed the relationship between preexisting, postoperative, and change in IH over time and AVF outcomes. Study Design Prospective cohort study with longitudinal assessment of IH at the time of AVF creation (pre-existing) and transposition (postoperative). Patients were followed-up for up to 3.3 years. Setting & Participants 96 patients from a single center who underwent AVF surgery initially planned as a two-stage procedure. Veins and AVF samples were collected from 66 and 86 patients, respectively. Matched-pair tissues were available from 56 of these patients. Predictors Pre-existing, postoperative and change in IH over time. Outcomes Anatomic maturation failure was defined as an AVF that never reached a diameter greater than 6 mm. Primary unassisted patency was defined as the time elapsed from the second-stage surgery until first intervention. Measurements Maximal intimal thickness in veins and AVF and change in intimal thickness over time. Results Pre-existing IH (> 0.05 mm) was present in 98% of the patients. In this group, the median intimal thickness increased 4.40-fold (IQR, 2.17- to 4.94-fold) between the AVF creation and transposition. However, this change was not associated with the preexisting thickness (r2=0.002; p=0.7). Ten of 96 AVFs (10%) never achieved maturation, while 70% of the vascular accesses remained patent at the end of the observational period. Postoperative IH was not associated with anatomic maturation failure using a univariate logistic regression. Pre-existing, postoperative, and change in IH over time had no effects on primary unassisted patency. Limitations The low number of patients from whom longitudinal tissue samples were available and the low incidence of anatomic maturation failure, which decreased the statistical power to find associations between end points and IH. Conclusions Pre-existing, postoperative, and change in IH over time were not associated with two-stage AVF outcomes.

Improved procedures, in terms of smaller missed discovery rates (MDR), for performing multiple hypotheses testing with weak and strong control of the family-wise error rate (FWER) or the false discovery rate (FDR) are developed and studied. The improvement over existing procedures such as the Šidák procedure for FWER control and the Benjamini–Hochberg (BH) procedure for FDR control is achieved by exploiting possible differences in the powers of the individual tests. Results signal the need to take into account the powers of the individual tests and to have multiple hypotheses decision functions which are not limited to simply using the individual p-values, as is the case, for example, with the Šidák, Bonferroni, or BH procedures. They also enhance understanding of the role of the powers of individual tests, or more precisely the receiver operating characteristic (ROC) functions of decision processes, in the search for better multiple hypotheses testing procedures. A decision-theoretic framework is utilized, and through auxiliary randomizers the procedures could be used with discrete or mixed-type data or with rank-based nonparametric tests. This is in contrast to existing p-value based procedures whose theoretical validity is contingent on each of these p-value statistics being stochastically equal to or greater than a standard uniform variable under the null hypothesis. Proposed procedures are relevant in the analysis of high-dimensional “large M, small n” data sets arising in the natural, physical, medical, economic and social sciences, whose generation and creation is accelerated by advances in high-throughput technology, notably, but not limited to, microarray technology.

We study the popular benchmark dose (BMD) approach for estimation of low exposure levels in toxicological risk assessment, focusing on dose-response experiments with quantal data. In such settings, representations of the risk are traditionally based on a specified, parametric, dose-response model. It is a well-known concern, however, that uncertainty can exist in specification and selection of the model. If the chosen parametric form is in fact misspecified, this can lead to inaccurate, and possibly unsafe, lowdose inferences. We study the effects of model selection and possible misspecification on the BMD, on its corresponding lower confidence limit (BMDL), and on the associated extra risks achieved at these values, via large-scale Monte Carlo simulation. It is seen that an uncomfortably high percentage of instances can occur where the true extra risk at the BMDL under a misspecified or incorrectly selected model can surpass the target BMR, exposing potential dangers of traditional strategies for model selection when calculating BMDs and BMDLs.

We examined fentanyl and its six analogs using wB97XD/cc‐pVTZ density functional theoretical (DFT) calculations as well as Raman and Surface‐enhanced Raman spectroscopy (SERS). The in silico DFT calculations provided the vibrational frequencies, Raman activities, and normal mode assignment for each analog. Raman spectroscopy can detect crystalline fentanyl analogs but cannot obtain bands for samples in solution. Therefore, we utilized gold/silver nanospheres and gold/silver nanostars to examine them. The gold/silver nanostars provided stronger signals for the fentanyl analogs, and their SERS spectra can easily distinguish these fentanyl analogs from nonfentanyl opioids and other common drugs of abuse using principle component analysis and other statistical tests. Overall, our results demonstrate that SERS shows great potential to distinguish fentanyl analogs and detect trace quantities of these compounds in mixtures of seized drugs.

There is a dearth of research exploring the moderating role of the social environment on neighborhood structural disadvantage and depressive symptoms, particularly among adolescents. Therefore, we examined if adolescent perceptions of neighborhood social cohesion and safety moderated the association between neighborhood structural disadvantage and adolescent depressive symptoms. This cross-sectional study used data from the National Longitudinal Study of Adolescent to Adult Health (Add Health). The study sample consisted of 12,105 adolescents enrolled in 9th-12th grades during the 1994-1995 school year across the United States (U.S.). Mixed effects multilevel modeling was used to determine if adolescent perceptions of neighborhoods moderated the relationship between neighborhood structural disadvantage and adolescent depressive symptoms. Results showed that perceived neighborhood social cohesion moderated the relationship between neighborhood structural disadvantage and adolescent depressive symptoms (p≤0.001). At higher levels of perceived neighborhood social cohesion, neighborhood structural disadvantage was associated with decreased depressive symptoms. Findings suggest that improving perceived neighborhood social cohesion may decrease adolescent depressive symptoms, particularly in neighborhoods with high disadvantage. This aspect of the neighborhood social environment may serve as a target for structural and other interventions to address the growing burden of depression among adolescents.

An important objective in environmental risk assessment is estimation of minimum exposure levels, called Benchmark Doses (BMDs), that induce a pre-specified Benchmark Response (BMR) in a dose-response experiment. In such settings, representations of the risk are traditionally based on a specified parametric model. It is a well-known concern, however, that existing parametric estimation techniques are sensitive to the form employed for modeling the dose response. If the chosen parametric model is in fact misspecified, this can lead to inaccurate low-dose inferences. Indeed, avoiding the impact of model selection was one early motivating issue behind development of the BMD technology. Here, we apply a frequentist model averaging approach for estimating benchmark doses, based on information-theoretic weights. We explore how the strategy can be used to build one-sided lower confidence limits on the BMD, and we study the confidence limits’ small-sample properties via a simulation study. An example from environmental carcinogenicity testing illustrates the calculations. It is seen that application of this information-theoretic, model averaging methodology to benchmark analysis can improve environmental health planning and risk regulation when dealing with low-level exposures to hazardous agents.

We have recently shown that American ginseng (AG) prevents and treats mouse colitis. Because both mice and humans with chronic colitis have a high colon cancer risk, we tested the hypothesis that AG can be used to prevent colitis-driven colon cancer. Using the azoxymethane (AOM)/dextran sulfate sodium (DSS) mouse model of ulcerative colitis, we show that AG can suppress colon cancer associated with colitis. To explore the molecular mechanisms of the anticancer effects of AG, we also carried out antibody array experiments on colon cells isolated at a precancerous stage. We found there were 82 protein end points that were either significantly higher (41 proteins) or significantly lower (41 proteins) in the AOM + DSS group compared with the AOM-alone (control) group. In contrast, there were only 19 protein end points that were either significantly higher (10 proteins) or significantly lower (9 proteins) in the AOM + DSS + AG group compared with the AOM-alone (control) group. Overall, these results suggest that AG keeps the colon environment in metabolic equilibrium when mice are treated with AOM + DSS and gives insight into the mechanisms by which AG protects from colon cancer associated with colitis.

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