Kennesaw State University

Although validated prognostic models exist for men with metastatic castration-resistant prostate cancer (mCRPC), current tools do not incorporate genomic biomarkers such as circulating tumor DNA (ctDNA) aneuploidy or pathogenic genetic alterations (PGAs). This study aimed to estimate the prevalence of PGAs in ctDNA, assess their correlation with ctDNA aneuploidy fraction, and evaluate their association with overall survival (OS). Additionally, we developed and validated a clinical-genetic (CG) model to predict OS. We analyzed ctDNA from 776 patients enrolled in the Alliance phase 3 trial (A031201). PGAs were derived using the AR-ctDETECT assay. The net reclassification improvement (NRI) evaluated the added value of the CG model, and the time-dependent area under the receiver operating characteristic curve (tAUC) assessed the accuracy of the OS model. Feature selection using random survival forest identified gains in androgen receptor (AR), AR enhancer, MYC, RSPO2, CCND1, BRAF, and MET; losses or pathogenic variants in ZBTB16, PTEN, MSH6, PPP2R2A, NKX3-1, ZFHX3, TP53, ZNFR3, RB1, FANCA, CHECK1, APC, and CHD1; and a pathogenic AR variant. The CG model significantly outperformed the clinical model, with an average tAUC of 0.77 (95% confidence interval [CI]: 0.73-0.79) for the CG model compared with the tAUC of 0.72 (95% CI: 0.69-0.75, p=0.01) for the clinical model, and NRI of 0.29 between the models. Patients were categorized by the predicted risk scores into poor-, intermediate-, and low-risk groups with median OS of 19.6, 33.6, and 60.8mo, respectively. Incorporation of PGAs from ctDNA into a CG model improved OS prediction by nearly 30% over a clinical model. This model can classify patients into risk groups and is useful for selecting patients in future mCRPC trials.

Cancer treatment decision making (CTDM) presents important challenges among older adults due to variation in health and functional status, presence of comorbidities, differing goals/values, quality and quantity of life considerations, and limited inclusion in clinical trials. Nursing standards and guidelines call for competence in supporting CTDM and nurses advocate for greater involvement. However, clear understanding of the existing evidence to inform nursing practice is lacking. We aimed to map and synthesize evidence that provides insight into nursing practice in CTDM among older adults, with attention to nursing roles, required skills and competencies, potential barriers and facilitators, and outcomes studied. Following JBI (formerly Joanna Briggs Institute) methodology for scoping reviews, we included empirical articles that describe nursing contribution, individually or as part of a multidisciplinary team (MDT), related to CTDM for active (curative or non-curative) treatment among adults aged ≥60years diagnosed with cancer. Nine databases were searched systematically from inception to January 2024, no limits applied. Two independent reviewers screened identified records and full texts, then systematically extracted data from included articles. Basic qualitative content analysis was conducted on charted data. Of the 9582 records screened, 980 full texts were assessed for eligibility; 84 reports describing 78 studies were included. Conducted primarily in the United States or United Kingdom, studies described nursing interventions, nursing involvement in geriatric assessment and MDT meetings/clinics, and the perspectives of patients, family, and/or healthcare professionals. Although seldom the primary focus, researchers have highlighted the important roles of nurses in the CTDM process, before, during, and after consultations with physicians and MDT meetings, which require disease-specific knowledge and relational skills. MDT collaboration, training, dedicated time and space, adequate resources, and support from leadership are critical to promoting involvement, with potential impact on decision satisfaction and optimal treatment decisions. The value of nurses' involvement is endorsed by patients, family, and other healthcare professionals. Nurses play a vital role in CTDM among older adults, particularly in the MDT context. However, rigorous studies demonstrating the impact of nursing practice on CTDM outcomes among older adults are lacking. Further research is needed to inform nursing practice and interventions.

Electric vehicles (EVs) emit substantially fewer air pollutants than conventional internal combustion engine vehicles. However, the continuous increase in electricity demand from the power grid for EV charging, resulting from the growing adoption and total vehicle miles traveled, leads to higher greenhouse gas emissions from the power sector. This study presents a predictive analysis of energy sector greenhouse gas emissions from EV charging at the regional level across the United States under various projection scenarios of technology costs, fuel prices, demand growth, and electricity sector policies. The predictive modeling of greenhouse gas emissions from EV charging is performed using a machine learning model developed on the Meta Prophet platform designed to capture temporal patterns and seasonality with a high level of precision. Trained on simulation data from the Cambium model, developed by the National Renewable Energy Laboratory (NREL), our model provides accurate and continuous predictions of CO2, N2O, and CH4 emission rates from EV charging through 2050 under eight power generation planning scenarios, each outlining different projections for costs, prices, demand, and policy outcomes. Our analysis suggests that, by 2030, total grid emissions of CO2, N2O, and CH4 from EV battery charging in the United States are projected to decline by 52.67%, 65.71%, and 53.65%, respectively, compared to 2025 under the mid-case scenario, despite an overall increase of 152.57% in EV electricity demand. By 2050, emissions are expected to decline further by 80.13%, 94.15%, and 75.62%, respectively, with total EV electricity demand rising by 802.02%. Our scenario analysis results underscore the pivotal role of regional energy mixes and the pace of renewable energy deployment in decarbonizing transportation through electrification, highlighting the urgent need for proactive policy measures to accelerate the adoption of clean and renewable energy technologies in order to meet long-term climate targets.

Construction progress monitoring is crucial for successful project completion. Traditional methods involve labor-intensive site visits, which are inefficient and potentially hazardous. A novel conceptual framework for a robotic cyber-physical system (CPS) was designed to automate reality capture and visualization for remote progress monitoring in construction. The proposed CPS integrates a quadruped robot, building information modeling (BIM), and 360° reality capture technology to autonomously collect and visualize up-to-date site information. Using the Technology Acceptance Model (TAM) as the theoretical framework, semi-structured interviews were conducted with 17 construction professionals, representing diverse roles in project management, engineering, and field operations. Through systematic thematic analysis, 39 critical adoption factors were identified. This research contributes to both theory and practice by identifying factors affecting the industry acceptance of a robotic CPS framework for construction progress monitoring, providing guidance for construction management teams, and establishing a foundational understanding of industry adoption factors for human-centered CPS development in construction.

Growing Problem Solvers provides four original, related, classroom-ready mathematical tasks, one for each grade band. Together, these tasks illustrate the trajectory of learners’ growth as problem solvers across their years of school mathematics.

Quantum-based machine learning (QML) is known to integrate quantum computing (QC) and machine learning (ML) to boost performance in data analysis and decision-making. The demand for professionals in QML has increased; however, QML is not part of the curricula for most colleges and universities. In this project, we create labware in Google Colab that encompasses knowledge of QC, ML, and QML, and demonstrates QML applications for analyzing scientific and engineering data. We integrate our learning modules into courses in Computer Science, Industrial Engineering, Physics, and other disciplines, and host a faculty workshop, a student camp, and a conference tutorial session to promote the use of our learning modules. The feedback from participating faculty and students is overwhelmingly positive.