Latent pathway-based Bayesian models to identify intervenable factors of racial disparities in breast cancer stage at diagnosis.

PURPOSE: We built Bayesian Network (BN) models to explain roles of different patient-specific factors affecting racial differences in breast cancer stage at diagnosis, and to identify healthcare related factors that can be intervened to reduce racial health disparities. METHODS: We studied women age 67-74 with initial diagnosis of breast cancer during 2006-2014 in the National Cancer Institute's SEER-Medicare dataset. Our models included four measured variables (tumor grade, hormone receptor status, screening utilization and biopsy delay) expressed through two latent pathways-a tumor biology path, and health-care access/utilization path. We used various Bayesian model assessment tools to evaluate these two latent pathways as well as each of the four measured variables in explaining racial disparities in stage-at-diagnosis. RESULTS: Among 3,010 Black non-Hispanic (NH) and 30,310 White NH breast cancer patients, respectively 70.2% vs 76.9% were initially diagnosed at local stage, 25.3% vs 20.3% with regional stage, and 4.56% vs 2.80% with distant stage-at-diagnosis. Overall, BN performed approximately 4.7 times better than Classification And Regression Tree (CART) (Breiman L, Friedman JH, Stone CJ, Olshen RA. Classification and regression trees. CRC press; 1984) in predicting stage-at-diagnosis. The utilization of screening mammography is the most prominent contributor to the accuracy of the BN model. Hormone receptor (HR) status and tumor grade are useful for explaining racial disparity in stage-at diagnosis, while log-delay in biopsy impeded good prediction. CONCLUSIONS: Mammography utilization had a significant effect on racial differences in breast cancer stage-at-diagnosis, while tumor biology factors had less impact. Biopsy delay also aided in predicting local and regional stages-at-diagnosis for Black NH women but not for white NH women.

Bayesian regression analysis of skewed tensor responses.

Tensor regression analysis is finding vast emerging applications in a variety of clinical settings, including neuroimaging, genomics, and dental medicine. The motivation for this paper is a study of periodontal disease (PD) with an order-3 tensor response: multiple biomarkers measured at prespecified tooth-sites within each tooth, for each participant. A careful investigation would reveal considerable skewness in the responses, in addition to response missingness. To mitigate the shortcomings of existing analysis tools, we propose a new Bayesian tensor response regression method that facilitates interpretation of covariate effects on both marginal and joint distributions of highly skewed tensor responses, and accommodates missing-at-random responses under a closure property of our tensor model. Furthermore, we present a prudent evaluation of the overall covariate effects while identifying their possible variations on only a sparse subset of the tensor components. Our method promises Markov chain Monte Carlo (MCMC) tools that are readily implementable. We illustrate substantial advantages of our proposal over existing methods via simulation studies and application to a real data set derived from a clinical study of PD. The R package BSTN available in GitHub implements our model.

Activation of nitrogen species mixed with Ar and H2S plasma for directly N-doped TMD films synthesis.

Among the transition metal dichalcogenides (TMD), tungsten disulfide (WS2) and molybdenum disulfide (MoS2) are promising sulfides for replacing noble metals in the hydrogen evolution reaction (HER) owing to their abundance and good catalytic activity. However, the catalytic activity is derived from the edge sites of WS2 and MoS2, while their basal planes are inert. We propose a novel process for N-doped TMD synthesis for advanced HER using N2 + Ar + H2S plasma. The high ionization energy of Ar gas enabled nitrogen species activation results in efficient N-doping of TMD (named In situ-MoS2 and In situ-WS2). In situ-MoS2 and WS2 were characterized by various techniques (Raman spectroscopy, XPS, HR-TEM, TOF-SIMS, and OES), confirming nanocrystalline and N-doping. The N-doped TMD were used as electrocatalysts for the HER, with overpotentials of 294 mV (In situ-MoS2) and 298 mV (In situ-WS2) at a current density of 10 mA cm-2, which are lower than those of pristine MoS2 and WS2, respectively. Density functional theory (DFT) calculations were conducted for the hydrogen Gibbs energy (∆GH) to investigate the effect of N doping on the HER activity. Mixed gas plasma proposes a facile and novel fabrication process for direct N doping on TMD as a suitable HER electrocatalyst.

Rural-Urban Differences in Breast Cancer Stage at Diagnosis.

Purpose: To analyze the extent to which rural-urban differences in breast cancer stage at diagnosis are explained by factors including age, race, tumor grade, receptor status, and insurance status. Methods: Using the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) 18 database, analysis was performed using data from women aged 50-74 diagnosed with breast cancer between the years 2013 and 2016. Patient rurality of residence was coded according to SEER's Rural-Urban Continuum Code 2013: Large Urban (RUCC 1), Small Urban (RUCC 2,3), and Rural (RUCC 4,5,6,7,8,9). Stage at diagnosis was coded according to SEER's Combined Summary Stage 2000 (2004+) criteria: Localized (0,1), Regional (2,3,4,5), and Distant (7). Descriptive statistics were analyzed, and variations were tested for across rural-urban categories using Kruskall-Wallis and Kendall's tau-b tests. Additionally, odds ratios (ORs) and 95% confidence intervals for the three ordinal levels of rural-urban residence were calculated while adjusting for other independent variables using ordinal logistic regression. Results: The rural residence category showed the largest proportion of women diagnosed with distant stage breast cancer. Additionally, we determined that patients with residence in both large and small urban areas had statistically significantly lower odds of higher stage diagnosis compared to rural patients even after controlling for age, race, tumor grade, receptor status, and insurance status. Conclusions: Rural women with breast cancer show small but statistically significant disparities in stage-at-diagnosis. Further research is needed to understand local area variation in these disparities across a wide range of rural communities, and to identify the most effective interventions to eliminate these disparities.

Naïve Bayesian network-based contribution analysis of tumor biology and healthcare factors to racial disparity in breast cancer stage-at-diagnosis.

BACKGROUND: Variation in breast cancer stage at initial diagnosis (including racial disparities) is driven both by tumor biology and healthcare factors. METHODS: We studied women age 67-74 with initial diagnosis of breast cancer from 2006 through 2014 in the SEER-Medicare database. We extracted variables related to tumor biology (histologic grade and hormone receptor status) and healthcare factors (screening mammography [SM] utilization and time delay from mammography to diagnostic biopsy). We used naïve Bayesian networks (NBNs) to illustrate the relationships among patient-specific factors and stage-at-diagnosis for African American (AA) and white patients separately. After identifying and controlling confounders, we conducted counterfactual inference through the NBN, resulting in an unbiased evaluation of the causal effects of individual factors on the expected utility of stage-at-diagnosis. An NBN-based decomposition mechanism was developed to evaluate the contributions of each patient-specific factor to an actual racial disparity in stage-at-diagnosis. 2000 bootstrap samples from our training patients were used to compute the 95% confidence intervals (CIs) of these contributions. RESULTS: Using a causal-effect contribution analysis, the relative contributions of each patient-specific factor to the actual racial disparity in stage-at-diagnosis were as follows: tumor grade, 45.1% (95% CI: 44.5%, 45.8%); hormone receptor status, 5.0% (4.5%, 5.4%); mammography utilization, 23.1% (22.4%, 24.0%); and biopsy delay 26.8% (26.1%, 27.3%). CONCLUSION: The modifiable mechanisms of mammography utilization and biopsy delay drive about 49.9% of racial difference in stage-at-diagnosis, potentially guiding more targeted interventions to eliminate cancer outcome disparities. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13755-021-00165-5.

Synthesis of vertically aligned wafer-scale tantalum disulfide using high-Ar/H2S ratio plasma.

Nanostructural modification of two-dimensional (2D) materials has attracted significant attention for enhancing hydrogen evolution reaction (HER) activity. In this study, the nanostructure of TaS2films was controlled by controlling the Ar/H2S gas ratio used in plasma-enhanced chemical vapor deposition (PECVD). At a high Ar/H2S gas ratio, vertically aligned TaS2(V-TaS2) films were formed over a large-area (4 in) at a temperature of 250 °C, which, to the best of our knowledge, is the lowest temperature reported for PECVD. Furthermore, the plasma species formed in the injected gas at various Ar/H2S gas ratios were analyzed using optical emission spectroscopy to determine the synthesis mechanism. In addition, the 4 in wafer-scale V-TaS2was analyzed by x-ray photoelectron spectroscopy, transmission electron microscopy, and atomic force microscopy, and the HER performance of the as-synthesized TaS2fabricated with various Ar/H2S ratios was measured. The results revealed that, depending on the film structure of TaS2, the HER performance can be enhanced owing to its structural advantage. Furthermore, the excellent stability and robustness of V-TaS2was confirmed by conducting 1000 HER cycles and post-HER material characterization. This study provides important insights into the plasma-assisted nanostructural modification of 2D materials for application as enhanced electrocatalysts.

Low-Temperature Synthesis of Wafer-Scale MoS2-WS2 Vertical Heterostructures by Single-Step Penetrative Plasma Sulfurization.

Two-dimensional (2D) transition metal dichalcogenides (TMDs) have attracted considerable attention owing to their synergetic effects with other 2D materials, such as graphene and hexagonal boron nitride, in TMD-based heterostructures. Therefore, it is important to understand the physical properties of TMD-TMD vertical heterostructures for their applications in next-generation electronic devices. However, the conventional synthesis process of TMD-TMD heterostructures has some critical limitations, such as nonreproducibility and low yield. In this paper, we synthesize wafer-scale MoS2-WS2 vertical heterostructures (MWVHs) using plasma-enhanced chemical vapor deposition (PE-CVD) via penetrative single-step sulfurization discovered by time-dependent analysis. This method is available for fabricating uniform large-area vertical heterostructures (4 in.) at a low temperature (300 °C). MWVHs were characterized using various spectroscopic and microscopic techniques, which revealed their uniform nanoscale polycrystallinity and the presence of vertical layers of MoS2 and WS2. In addition, wafer-scale MWVHs diodes were fabricated and demonstrated uniform performance by current mapping. Furthermore, mode I fracture tests were performed using large double cantilever beam specimens to confirm the separation of the MWVHs from the SiO2/Si substrate. Therefore, this study proposes a synthesis mechanism for TMD-TMD heterostructures and provides a fundamental understanding of the interfacial properties of TMD-TMD vertical heterostructures.