Racial Disparities in Cancer Guideline-Concordant Treatment Using Surveillance, Epidemiology, and End Results Data for Patients With NSCLC.

Introduction: Despite efforts to achieve health care equality, racial/ethnic disparities persist in lung cancer survival in the United States, with non-Hispanic Black patients experiencing higher mortality compared with non-Hispanic Whites. Previous research often focused on single treatments, overlooking the broad range of options available. We aimed to highlight disparities in survival and receipt of comprehensive lung cancer treatment by developing a guideline-concordant initial treatment (GCIT) indicator based on disease stage and recommended treatment.

Genome-wide Machine Learning Analysis of Anosmia and Ageusia with COVID-19.

The COVID-19 pandemic has caused substantial worldwide disruptions in health, economy, and society, manifesting symptoms such as loss of smell (anosmia) and loss of taste (ageusia), that can result in prolonged sensory impairment. Establishing the host genetic etiology of anosmia and ageusia in COVID-19 will aid in the overall understanding of the sensorineural aspect of the disease and contribute to possible treatments or cures. By using human genome sequencing data from the University of Iowa (UI) COVID-19 cohort (N=187) and the National Institute of Health All of Us (AoU) Research Program COVID-19 cohort (N=947), we investigated the genetics of anosmia and/or ageusia by employing feature selection techniques to construct a novel variant and gene prioritization pipeline, utilizing machine learning methods for the classification of patients.

Evaluation of thermal parameters with geothermal energy recovery from a cold climate municipal solid waste landfill.

This study presents finite element method (FEM) modeling with COMSOL Multiphysics to estimate the thermal properties of a waste landfill through back-analysis of temperature responses from buried thermistors during a full-scale multi-week active thermal response test. Field tests were conducted at the Loraas MSW landfill in Saskatoon, Canada, using instrumented equipment, including a vertical borehole heat exchanger and thermistor strings. The test consisted of two phases: a heat injection test during the summer and a heat extraction test in the winter.

Evaluating machine learning model bias and racial disparities in non-small cell lung cancer using SEER registry data.

Background: Despite decades of pursuing health equity, racial and ethnic disparities persist in healthcare in America. For cancer specifically, one of the leading observed disparities is worse mortality among non-Hispanic Black patients compared to non-Hispanic White patients across the cancer care continuum. These real-world disparities are reflected in the data used to inform the decisions made to alleviate such inequities.

A method for evaluating and verifying biochemical methane potential test completion performed with landfilled municipal solid waste.

The biochemical methane potential (BMP) test is significant for the landfill industry as it provides a means to evaluate the gas potential, and therefore potential degradability, of both incoming and in-place municipal solid waste (MSW). However, the BMP test is not standardized making comparison of BMP results across sites problematic. For example, the BMP test duration has historically ranged from 20 days to several months with most current BMP tests lasting 60 days.