Explore the vast range of titles available.

Previous research demonstrated Non-Hispanic Black populations experience higher COVID-19 mortality rates than Non-Hispanic White individuals. Additionally, cancer status is a known risk factor for COVID-19 death. While prior studies investigated comorbidities as exploratory variables in differences in COVID-19 hospitalization, none have explored their role in COVID-19-related deaths. This study aimed to evaluate whether Charlson Comorbidity Index (CCI) and subsequently, individual diseases are potential explanatory variables for this relationship. The analysis focused on Non-Hispanic Black and Non-Hispanic White cancer patients aged 20 or older, diagnosed between 2011 and 2019, who tested positive for COVID-19 from the start of pandemic through June 30, 2021 from Louisiana Tumor Registry. Two separate mediation analyses were conducted. First checked whether overall comorbidity, measured by CCI, could explain the difference in COVID-19 mortality. If so, further checked which individual comorbidities contributed to this difference. The hazard rate for Non-Hispanic Black cancer patients dying from COVID-19 was 6.46 times than that of Non-Hispanic White patients. The CCI accounted for 12.7% of the differences observed in COVID-19 mortality, with renal disease as the top contributor, explaining 4.9%. These findings could help develop interventions to reduce COVID-19 mortality and address the disproportionate impact, especially by managing chronic conditions like renal disease.

Background/Objectives: Triple negative breast cancer (TNBC) is an aggressive, molecularly heterogeneous subtype of breast cancer, accounting for approximately 10–15% of all cases. While reproductive and metabolic factors contribute to breast cancer development, growing concerns about environmental exposures, alongside biological and socio-cultural influences, underscore the need for targeted prevention strategies across diverse populations. Despite increasing evidence linking biological, socioeconomic, and environmental factors to TNBC outcomes, the molecular mechanisms underlying these relationships remain poorly understood. Micro-RNAs (miRNAs), which regulate gene expression and play critical roles in cancer development, have emerged as potential mediators between environmental exposures and TNBC progression. The goal of this research is to identify environmental risk factors that directly relate to TNBC stages and enhance understanding of the mechanisms underlying how miRNAs link environmental exposures to TNBC stages. Methods: In this study, we analyzed 434 Formalin-Fixed, Paraffin-Embedded (FFPE) tumor samples from 434 women diagnosed with TNBC between 2009 and 2019, encompassing diverse cancer stages (184 cases from early stage and 250 cases from advanced stage), racial backgrounds, and socioeconomic statuses. The sequencing data were linked with the Louisiana Tumor Registry data and the Environmental Justice index. Results: A total of 348 unique miRNAs were identified as differentially expressed across environmental risk factors statistically associated with TNBC stage, adjusting for plate effects. An UpSet plot revealed 44 miRNAs commonly differentially expressed across TNBC stages and multiple environmental exposures. At least one differentially expressed (DE) miRNA was shared between the TNBC stage and each environmental factor, with many associated with receptor-negative and aggressive breast cancer subtypes. Conclusions: These findings highlight potential biological pathways through which exposures may drive the TNBC progression and contribute to disparities in outcomes.

Objectives: To assess the utility of Microsoft Copilot, a generative AI tool, in providing meaningful differential diagnosis and management strategies comparable with those generated by a board-certified radiologist using cone beam computed tomography (CBCT) studies in maxillofacial disease and thus assess its potential utility to help with the initial provisional diagnostic process. Study Design: A pilot project designed as a single-center, retrospective study using a convenient sample was conducted. De-identified data collected from patient charts in a consistent format was fed to Microsoft 365 Copilot (MCP) to generate a list of meaningful differential diagnosis (DD) and management protocols. Scores ranging of 0–3 were given for 0–3 matches in DD and management protocols, respectively. Results: Proportional analysis showed that the radiologist and Copilot agreed on the DD in 75.2% of cases and 94.6% of cases in management protocols. For biopsy recommendations, the radiologist and Copilot advised biopsy in 33 (89.2%) cases while they did not recommend biopsy in 23 (41.8%) cases. Conclusions: Generative AI platforms at this point may have value in generating DD and management protocols based on maxillofacial CBCT findings. However, the radiologist’s judgement based on clinical context, feature recognition, and critical analysis seemed to outperform MCP. Larger studies with statistical validation are warranted.

Background: Breast cancer outcomes are worse among Black women in the U.S. compared to White women. While extensive research has focused on risk factors contributing to breast cancer; the role of genomic elements in health disparities between these racial groups remains unclear. This study aims to identify genomic variants and socioeconomic status (SES) determinants influencing racial disparities in breast cancer survival through multiple mediation analyses. Methods: Our investigation is based on the NIH-supported All of Us (AoU) program and analyzes 7452 female participants with malignant tumors of breast, including 5073 with genomic data. A log-rank test reveals significant racial differences in overall survival time between Black and White participants (p-value = 0.04). Multiple mediation analysis examines the effects of 9481 genetic variables across 23 chromosomes in explaining the racial disparity in survival, adjusting for SES variables. Results: 15 gene mutations, in addition to age, general health, and general quality of life, have significant effects (p-values < 0.001) in explaining the observed racial disparity. Mutations in TMEM132B, NARFL, SALL1, PAD12, RIPK1, ASB14, DCX, GNB1L, ARHGAP32, AL135787.1, WBP11, SLC16A12AS1, AP000345.1, IKBKB, and SUPT20H have significantly different distributions between Black and White participants. The disparity is completely explained by the included variables as the direct effect is insignificant (p-value = 0.73). Conclusions: The combined impact of SES determinants and genetic mutations can explain the observed differences in breast cancer survival among Black and White participants. Future studies will explore pathways and design in vivo and in vitro experiments to validate the functions of these genes

Classification problems are tackled across various industries throughout multiple disciplines. A model used for classification attempts to predict the class of an outcome variable based on some predictors. There are number of classification models available. But as the underlying population distribution of the predictors is always unknown it is difficult to know which model fits the situation best. Several studies have been done on which supervised model performs better given specific datasets. But little work has been done to compare the models’ performance for predicting one or more outcomes under multivariate settings.This study compares the performance of seven popular statistical learning models used for classification when the dataset is from a multivariate population. The models are: K-nearest neighbor, logistic regression, support vector machines, linear discriminant analysis, random forest, adaptive boosting and gradient boosting. We compare these methods under three different distribution settings, e.g., multivariate normal distribution, multivariate t distribution and multivariate log-normal distributions for both binary, k=2 and multiple outcomes, k=5. Three different sample sizes, n=100, 300, 500 are considered along with two different number of predictors, p=3,10 to check if the performance changes with sample size and number of predictors. We also compare the models for balanced and unbalanced datasets under these different settings. The models are evaluated using two criteria: accuracy, which works best for balanced datasets and Cohen’s kappa coefficient, which gives good result under unbalanced datasets.A 10-fold cross validation technique is used where the data is randomly split into 75% training set and 25 % testing set to test the models’ prediction skills on new data. The model parameters are tuned under each setting to get the best performing model. Boxplots are used to show the spread of performance metrics calculated from multiple iterations. It is found that for the multivariate normal distribution, boosting algorithms are superior to others, whereas for the multivariate t distribution, support vector machines are preferable. Lastly, for the multivariate log-normal distribution, all models perform well but the random forest algorithm was better than the rest in most cases. The preference of models changes with an increase in the number of classes depending on balanced and unbalanced datasets. But overall, the gradient boosting algorithm and random forest have good performance accuracies for all the settings. This is further implemented on a real dataset of heart disease patients to verify the results of the simulation. Gradient boosting produces the highest prediction accuracy among all seven models and the K-nearest neighbor had the narrowest spread of accuracies.