Search

Purpose: Immunosuppressed (IS) patients are at increased risk for developing Merkel cell carcinoma (MCC) with worsened outcomes compared to immunocompetent (IC) patients. We sought to determine the effects of immune status on the efficacy of adjuvant RT regarding OS for patients with stage I, II or III (localized) MCC of the head and neck. Methods/patients: The National Cancer Database was queried for patients with resected, localized MCC of the head and neck with known immune status. Kaplan–Meier methods were used to describe OS. Log-rank tests, multivariable Cox regression models and interaction effect testing were used to compare OS by subgroup categorized by patient and treatment factors including immune status and adjuvant RT receipt. Results: A total of 892 (89.6%) IC and 104 (10.4%) IS patients with MCC of the head and neck were included. Adjuvant RT was associated with improved 3-year OS rate for both IS patients (49.4% vs. 35.5%, p = 0.0467) and stage I/II IC patients (72.4% vs. 62.9%, p = 0.0092). Adjuvant RT was associated with decreased hazard of death (HR 0.77, 95% CI 0.62–0.95). Interaction effect testing did not demonstrate a difference in the efficacy of adjuvant RT on OS between IC and IS status (p = 0.157). Conclusions: In this NCDB analysis, adjuvant RT was associated with decreased hazard of death for patients with localized MCC of the head and neck regardless of immune status and should be considered for both IS and IC patients. [ABSTRACT FROM AUTHOR]

In phase change memory cells, the majority of heat is lost through the electrodes during the programming process, which leads to significant drops in the performance of the memory device. In this Letter, we report on the thermal properties of thin film carbon nitride with a modest electrical resistivity of 5–10 mΩ cm, a low thermal conductivity of 1.47 ± 0.09 W m−1 K–1, and a low interfacial thermal conductance between carbon nitride and phase change material for length scales below 40 nm. The thermally insulating property of carbon nitride makes it a suitable thermal barrier, allowing for less heat loss during Joule heating within the memory unit. We compare the thermal properties of carbon nitride against the commonly used electrodes and insulators such as tungsten and silicon nitride, respectively, to demonstrate the promise of carbon nitride as a potential material candidate for electrode applications in phase change memory devices. [ABSTRACT FROM AUTHOR]

The article presents a case study of 15-year-old green moray eel (Gymnothorax funebris) with inapetence and abnormal buoyancy. Topics discussed include the use of dexamethasone sodium phosphate and adequan for reducing inflammation; spondylosis with swim bladder hyperinflation and photobiomodulation for wound healing.

Inference on data with missingness can be challenging, particularly if the knowledge that a measurement was unobserved provides information about its distribution. Our work is motivated by the Commit to Quit II study, a smoking cessation trial that measured smoking status and weight change as weekly outcomes. It is expected that dropout in this study was informative and that patients with missed measurements are more likely to be smoking, even after conditioning on their observed smoking and weight history. We jointly model the categorical smoking status and continuous weight change outcomes by assuming normal latent variables for cessation and by extending the usual pattern mixture model to the bivariate case. The model includes a novel approach to sharing information across patterns through a Bayesian shrinkage framework to improve estimation stability for sparsely observed patterns. To accommodate the presumed informativeness of the missing data in a parsimonious manner, we model the unidentified components of the model under a nonfuture dependence assumption and specify departures from missing at random through sensitivity parameters, whose distributions are elicited from a subject-matter expert. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

The estimation of the covariance matrix is a key concern in the analysis of longitudinal data. When data consist of multiple groups, it is often assumed the covariance matrices are either equal across groups or are completely distinct. We seek methodology to allow borrowing of strength across potentially similar groups to improve estimation. To that end, we introduce a covariance partition prior that proposes a partition of the groups at each measurement time. Groups in the same set of the partition share dependence parameters for the distribution of the current measurement given the preceding ones, and the sequence of partitions is modeled as a Markov chain to encourage similar structure at nearby measurement times. This approach additionally encourages a lower-dimensional structure of the covariance matrices by shrinking the parameters of the Cholesky decomposition toward zero. We demonstrate the performance of our model through two simulation studies and the analysis of data from a depression study. This article includes Supplementary Materials available online. [ABSTRACT FROM AUTHOR]

For longitudinal data, the modeling of a correlation matrix Rcan be a difficult statistical task due to both the positive definite and the unit diagonal constraints. Because the number of parameters increases quadratically in the dimension, it is often useful to consider a sparse parameterization. We introduce a pair of prior distributions on the set of correlation matrices for longitudinal data through the partial autocorrelations (PACs), which vary independently over (−1,1). The first prior shrinks each of the PACs toward zero with increasingly aggressive shrinkage in lag. The second prior (a selection prior) is a mixture of a zero point mass and a continuous component for each PAC, allowing for a sparse representation. The structure implied under our priors is readily interpretable for time-ordered responses because each zero PAC implies a conditional independence relationship in the distribution of the data. Selection priors on the PACs provide a computationally attractive alternative to selection on the elements of Ror R− 1for ordered data. These priors allow for data-dependent shrinkage/selection under an intuitive parameterization in an unconstrained setting. The proposed priors are compared to standard methods through a simulation study and illustrated using a multivariate probit data example. Supplemental materials for this article (appendix, data, and R code) are available online. [ABSTRACT FROM PUBLISHER]

Exophiala pisciphila is a dematiaceous fungus that belongs to a group of fungi known as the 'black yeasts'. It was isolated from the skin lesions of a smooth dogfish, Mustelus canis Mitchill, that had been born in the shark exhibit tank of the New York Aquarium. The different stages of development of this fungus were studied by light microscopy and scanning electron microscopy to illustrate the morphology and surface structures of conidia and mycelium. The list of marine and fresh water fish, which have been infected by Exophiala spp. and Exophiala-like fungi has been up-dated. Potato Dextrose Agar and Malt Agar proved to be the best growth media, while Corn Meal Agar proved to be the best medium for studying the morphological features of the conidia and mycelial development of E. pisciphila, which exhibited polymorphic conidiogenesis. [ABSTRACT FROM AUTHOR]