Publications

Conventional advice to reduce the risk of breast cancer-related lymphedema (BCLE) suggests avoidance of daily-living risks, and limited research has investigated these risks.

Artificial Intelligence (AI), encompassing Machine Learning and Deep Learning, has increasingly been applied to fracture detection using diverse imaging modalities and data types. This systematic review and meta-analysis aimed to assess the efficacy of AI in detecting fractures through various imaging modalities and data types (image, tabular, or both) and to synthesize the existing evidence related to AI-based fracture detection. Peer-reviewed studies developing and validating AI for fracture detection were identified through searches in multiple electronic databases without time limitations. A hierarchical meta-analysis model was used to calculate pooled sensitivity and specificity. A diagnostic accuracy quality assessment was performed to evaluate bias and applicability. Of the 66 eligible studies, 54 identified fractures using imaging-related data, nine using tabular data, and three using both. Vertebral fractures were the most common outcome (n = 20), followed by hip fractures (n = 18). Hip fractures exhibited the highest pooled sensitivity (92%; 95% CI: 87–96, p\textless 0.01) and specificity (90%; 95% CI: 85–93, p\textless 0.01). Pooled sensitivity and specificity using image data (92%; 95% CI: 90–94, p\textless 0.01; and 91%; 95% CI: 88–93, p \textless 0.01) were higher than those using tabular data (81%; 95% CI: 77–85, p\textless 0.01; and 83%; 95% CI: 76–88, p \textless 0.01), respectively. Radiographs demonstrated the highest pooled sensitivity (94%; 95% CI: 90–96, p \textless 0.01) and specificity (92%; 95% CI: 89–94, p\textless 0.01). Patient selection and reference standards were major concerns in assessing diagnostic accuracy for bias and applicability. AI displays high diagnostic accuracy for various fracture outcomes, indicating potential utility in healthcare systems for fracture diagnosis. However, enhanced transparency in reporting and adherence to standardized guidelines are necessary to improve the clinical applicability of AI. Review Registration: PROSPERO (CRD42021240359).

Exponentiated models have been widely used in modeling various types of data such as survival data and insurance claims data. However, the exponentiated composite distribution models have not been explored yet. In this paper, we introduce an improvement of the one-parameter Inverse Gamma-Pareto composite model by exponentiating the random variable associated with the one-parameter Inverse Gamma-Pareto composite distribution function. The goodness-of-fit of the exponentiated Inverse Gamma-Pareto was assessed using three different insurance data sets. The two-parameter exponentiated Inverse Gamma-Pareto model outperforms the one-parameter Inverse Gamma-Pareto model in terms of goodness-of-fit measures for all datasets. In addition, the proposed exponentiated composite Inverse Gamma-Pareto model provides a very good fit with some well-known insurance datasets.

As human complex diseases are influenced by the interplay of genes and environment, detecting gene-environment interactions (G×E) can shed light on biological mechanisms of diseases and play an important role in disease risk prediction. Development of powerful quantitative tools to incorporate G×E in complex diseases has potential to facilitate the accurate curation and analysis of large genetic epidemiological studies. However, most of existing methods that interrogate G×E focus on the interaction effects of an environmental factor and genetic variants, exclusively for common or rare variants. In this study, we proposed two tests, MAGEIT\_RAN and MAGEIT\_FIX, to detect interaction effects of an environmental factor and a set of genetic markers containing both rare and common variants, based on the MinQue for Summary statistics. The genetic main effects in MAGEIT\_RAN and MAGEIT\_FIX are modeled as random or fixed, respectively. Through simulation studies, we illustrated that both tests had type I error under control and MAGEIT\_RAN was overall the most powerful test. We applied MAGEIT to a genome-wide analysis of gene-alcohol interactions on hypertension in the Multi-Ethnic Study of Atherosclerosis. We detected two genes, CCNDBP1 and EPB42, that interact with alcohol usage to influence blood pressure. Pathway analysis identified sixteen significant pathways related to signal transduction and development that were associated with hypertension, and several of them were reported to have an interactive effect with alcohol intake. Our results demonstrated that MAGEIT can detect biologically relevant genes that interact with environmental factors to influence complex traits.

It is observed that, for some of the data in engineering and medical fields, the hazard rates increase to a high peak at the beginning and quickly decrease to a low level. In the context of survival analysis, such a hazard rate is called a upside-down bathtub hazard rate. In this paper, we investigated the properties of a model named exponentiated exponential-Pareto distribution. The model was recently proposed and applied to insurance data. We demonstrated that the model has upside-down bathtub-shaped hazard rates with specific choices of parameters. The theoretical properties such as moments, survival functions, and hazard functions were derived. The parameter estimation procedures were also introduced. We then briefly discussed the goodness-of-fit tests of the model with the simulations. Finally, we applied the model to a specific time-to-event data set along with a comparison of the performances with previous existing models. When compared to previous proposed models, the exponentiated exponential-Pareto model demonstrated good performance when fitting to such data sets.

In this paper, we propose a new family of distributions, by exponentiating the random variables associated with the probability density functions of composite distributions. We also derive some mathematical properties of this new family of distributions, including the moments and the limited moments. Specifically, two special models in this family are discussed. Three real datasets were chosen, to assess the performance of these two special exponentiated-composite models. When fitting to these three datasets, these three special exponentiated-composite distributions demonstrate significantly better performance, compared to the original composite distributions.

Objective This survey study is designed to understand the impact of the COVID-19 pandemic on stress among specific subpopulations of college students. Design, settings and participants An online questionnaire was sent to the students from University of Nevada, Las Vegas, between October 2020 and December to assess the psychological impact of COVID-19. A total of 2091 respondents signed the consent form online and their responses were collected. Main outcome measures Measures of psychological stress, as prescribed by the Perceived Stress Scale (PSS-10). An explanatory factor analysis was carried out on the PSS-10 results. We subsequently analysed each factor using stepwise linear regression that focused on various sociodemographic groups. Results A two-factor model was obtained using the explanatory factor analysis. After comparing with the past studies that investigated the factor structure of the PSS-10 scale, we identified these two factors as ‘anxiety’ and ‘irritability’. The subsequent stepwise linear regression analysis suggested that gender and age (p\textless0.01) are significantly associated with both factors. However, the ethnicities of students are not significantly associated with both factors. Conclusions To our knowledge, this is the first study that assessed the perceived stress of university students in the USA during the COVID-19 pandemic. Through exploratory factor analysis, we showed that the PSS-10 scale could be summarised as a two-factor structure. A stepwise regression approach was used, and we found both of the factors are significantly associated with the gender of the participants. However, we found no significant association between both factors and ethnicity. Our findings will help identify students with a higher risk for stress and mental health issues in pandemics and future crises.