Despite the harmful effect on health, e-cigarette and hookah smoking in youth in the U.S. has increased. Developing tailored e-cigarette and hookah cessation programs for youth is imperative. The aim of this study was to identify predictor variables such as social, mental, and environmental determinants that cause nicotine addiction in youth e-cigarette or hookah users and build nicotine addiction prediction models using machine learning algorithms. A total of 6511 participants were identified as ever having used e-cigarettes or hookah from the National Youth Tobacco Survey (2019) datasets. Prediction models were built by Random Forest with ReliefF and Least Absolute Shrinkage and Selection Operator (LASSO). ReliefF identified important predictor variables, and the Davies-Bouldin clustering evaluation index selected the optimal number of predictors for Random Forest. A total of 193 predictor variables were included in the final analysis. Performance of prediction models was measured by Root Mean Square Error (RMSE) and Confusion Matrix. The results suggested high performance of prediction. Identified predictor variables were aligned with previous research. The noble predictors found, such as 'witnessed e-cigarette use in their household' and 'perception of their tobacco use', could be used in public awareness or targeted e-cigarette and hookah youth education and for policymakers.
Background: Delirium is common in hospitalized patients and correlated with increased morbidity and mortality. Despite this, delirium is underdiagnosed, and many institutions do not have sufficient resources to consistently apply effective screening and prevention.
Objective: To develop a machine learning algorithm to identify patients at highest risk of delirium in the hospital each day in an automated fashion based on data available in the electronic medical record, reducing the barrier to large-scale delirium screening.
Ceratapion basicorne (Illiger) (Coleoptera: Apionidae), a weevil native to Europe and western Asia, shows promise for enhancing the control of yellow starthistle (Centaurea solstitialis L.), an invasive annual forb in the western United States. However, a paucity of data on this biocontrol agent's environmental constraints has made it difficult to assess the suitability of potential release locations.
Chromatin is a dynamic polymer in constant motion. These motions are heterogeneous between cells and within individual cell nuclei and are profoundly altered in response to DNA damage. The shifts in chromatin motions following genomic insults depend on the temporal and physical scales considered.
Objective: We have shown that men aged 50 years+ at high risk of type 2 diabetes treated with testosterone together with a lifestyle program reduced the risk of type 2 diabetes at two years by 40% compared to a lifestyle program alone. To develop a personalized approach to treatment, we aimed to explore a prognostic model for incident type 2 diabetes at two years and investigate biomarkers predictive of the testosterone effect.
Design: Model development in 783 men with impaired glucose tolerance but not type 2 diabetes from T4DM; a multicenter, 2-year trial of Testosterone vs placebo.
Colorectal cancer (CRC) is a significant contributor to cancer-related mortality, emphasizing the need for advanced biomarkers to guide treatment. As part of an international consortium, we previously categorized CRCs into four consensus molecular subtypes (CMS1-CMS4), showing promise for outcome prediction. To facilitate clinical integration of CMS classification in settings where formalin-fixed paraffin-embedded (FFPE) samples are routinely used, we developed NanoCMSer, a NanoString-based CMS classifier using 55 genes.
