University of Texas Health Science Center at Dallas

The adoption of artificial intelligence (AI) chatbots in higher education promises improved student support and administrative efficiency. Our objective is to offer insights to institutions in effectively adopting AI chatbots, tailored to the needs of both students and faculty. Using secondary survey data from United States higher education, we conducted a moderated mediation analysis to examine the mediation effect of perceived resources/support on the relationship between user role and perceived efficiency of AI chatbots, moderated by technological tool usage. The results revealed that perceived resources/support availability plays a mediating role in shaping how both students and faculty perceive the efficiency of AI chatbots, with students reporting greater efficiency when resources and support are more readily available. This mediation effect becomes marginally stronger as the usage of technological tools increases. While both student and faculty groups demonstrate openness to using AI chatbots, their perceptions of benefits and challenges towards AI chatbots differ significantly.

Only 10% of US children participate in active commuting (walking and bicycling) to school (ACS). Parental concerns related to traffic are a primary barrier to ACS, yet no studies have assessed these concerns together with neighborhood traffic safety-related features. This study examined the associations between traffic safety-related features in neighborhoods and ACS among school-age children in Austin, Texas. This cross-sectional analysis utilized pre-COVID-19 baseline data (2019-2020) of parent-child dyads enrolled in the Safe TRavel Environment Evaluation in Texas Schools study. ACS, traffic safety-related features in the neighborhood (sidewalk availability, safe road crossings, traffic safety, traffic speed, and safety at intersections and crossings), and child- and family-level covariates were reported by parents. Geographic Information Systems methods were used to construct neighborhood traffic safety-related variables (bike lane coverage, sidewalk coverage, traffic volume, high-speed streets, major roads, pedestrian beacons, and traffic/pedestrian signals) and neighborhood-level covariates within an 800-m straight-line Euclidean buffer from children's home to school. Multivariable logistic regression was used to test the association between traffic safety-related features and ACS adjusted for neighborhood-, child-, and family-level covariates. Among parent-child dyads with complete data (N = 187) from 22 schools, bike lane coverage was directly associated with ACS (AOR = 2.61; 95% CI, 1.44-4.90). Compared with low bicycle lane coverage (below the median, <0.35), high bike lane coverage was significantly associated with 2.49 times the odds of ACS (95% CI, 1.23-5.23). These findings support the need to increase the availability of bicycle infrastructure near schools as a possible strategy to enhance ACS participation.

We evaluated the incidence of anxiety, depression, and suicidality amongst TC survivors and the impact of chemotherapy on these outcomes. We conducted a retrospective cohort study of men diagnosed with TC in the United States Veterans Affairs Health System from 1990 to 2016. De novo anxiety or depression was a composite endpoint comprised of diagnosis codes for anxiety, depression, or medications used to treat these diagnoses. Incident suicidality was defined as a diagnosis code for suicidal ideation. 2022 TC patients were compared in a 1:3 ratio to 6375 controls. Cox proportional hazards models were employed for statistical analysis. Mean age at diagnosis was 42.6 years. 5-year cumulative incidence of anxiety or depression was 53.4% in TC patients and 35% for controls (p < 0.001). TC patients were more likely to develop anxiety or depression (HR 1.66, 95% CI 1.56-1.78, p < 0.001) and suicidality (HR 22.99, 95% CI 17.52-30.17, p < 0.001). In the TC cohort, factors associated with a higher risk of anxiety or depression were divorce (HR 1.15, 95% CI 1.00-1.32, p = 0.044), unemployment (HR 1.68, 95% CI 1.47-1.9, p < 0.001), and receipt of chemotherapy (HR 1.20, 95% CI 1.06-1.35, p < 0.001). Psychological morbidity due to depression, anxiety, and suicidality is high among TC survivors. In our analysis chemotherapy increases the rates of psychosocial morbidity. Clinicians should be proactive in screening and intervening for these diagnoses in TC survivors to provide early intervention and improve health comes.

The term Industrial Processes Industry 6.0 refers to a revolutionary approach to industrial processes that can be produced from the convergence of remote sensing technology and Artificial Intelligence (AI). This chapter explores the revolutionary possibility of AI-driven remote sensing to further sustainable economic development and respond to urgent environmental issues by systematically integrating advanced sensing capabilities with AI algorithms. These technologies work together synergistically, enabling efficient resource management, predictable maintenance, and proactive environmental protection measures. This research focuses on the recent advancements in AI-based remote sensing, its applications across the industrial sectors, and its application in encouraging sustainability. It dwells on issues of data privacy, technological constraints, and standardized practices in the implementation of these technologies. It also discusses possible mitigation activities to overcome these difficulties and maximize the benefits of AI-driven remote sensing in a commercial context. This chapter provides an overview of practical implications and prospects by analysing the case and experimental studies. The results elucidate the importance of AI-powered remote sensing for realizing sustainable industrial development and reducing ecological damage. This paper brings insight into the views of scholars, industrial strategists, and government policymakers in the quest for striking a balance between being good stewards of the environment and achieving technological advancement. This study contributes to the increasingly growing body of literature on Industry 6.0.

Understanding the fate of microbial fecal indicators is crucial for interpreting water quality data and protecting public health. Here, we released sewage in artificial streams to understand the influence of biofilm growth on the transport of the fecal indicators Escherichia coli, Carjivirus, HF183/BacR287, and pepper mild mottle virus (PMMoV). PMMoV exhibited significantly lower retention rates than all other targets before (−0.0029 m–1) biofilm colonization (p < 0.05), with no statistically significant difference between targets identified after biofilm colonization. When comparing retention rates between target biofilm conditions, we found marginally significant differences for PMMoV (p < 0.05), whereas Carjivirus, HF183/BacR287, and E. coli showed no changes before and after biofilm growth. All targets showed higher removal rates before biofilm growth, with PMMoV being the only target to demonstrate statistical significance (p < 0.05). These findings suggest the influence of biofilm colonization on the transport of fecal indicators in water is specific to the target being observed, with PMMoV consistently traveling longer in the water column than other targets. This study advances our understanding of fecal indicator dynamics in flowing waters, highlighting the critical influence of both indicator choice and environmental conditions on the fate of these indicators in the environment.

Primary care provides a broad service range, including preventive screenings and chronic disease care. Busy clinicians frequently select which health care processes to address per visit based on time or perceived disease impact, especially for patients with multimorbidity. Little evidence exists for how to prioritize processes by patient-centered health goals. To identify criteria for prioritizing health care processes for patients with multimorbidity and using these criteria, develop recommended priority rankings of health care processes aligned with patient-centered health goals. This consensus statement was developed using multicriteria decision analysis, a group consensus method. Using purposeful sampling, 21 national subject matter experts were recruited across 10 sites, with input from patient and health system partners. Experts completed 2 electronic survey rounds. The first round was conducted February 26 to April 2, 2024, with experts weighting 12 criteria for health care processes by importance (0-100 scale, with higher score indicating more importance) for addressing 3 common domains of patient-centered health goals (mortality, symptom relief, physical function) for hypothetical patients with multimorbidity. The second round was conducted June 3 to July 26, 2024, with experts scoring 53 health care processes by criteria (1-9 scale, with higher scores indicating greater priority). Experts received an evidence summary (publications through January 19, 2023) underlying health care processes. Weights and scores were combined as weighted sums, converted to process rankings per goal. Monte Carlo simulation (5000 randomizations) was used to evaluate confidence in findings (described as percentage of iterations at rank). A total of 20 experts participated (95% response rate). Survey 1 determined attributes important for decision-making. Seven criteria were selected: actionability, safety impact, completion ease, impact on high-prevalence health issues, evidence quality, long-term health outcome impact, and validity. The second survey used these criteria to rank health care processes by patient goals. Tobacco use screening was the highest priority health care process for all goals (function: total score, 6.9 [49% of iterations ranked as No. 1]; mortality: total score, 7.4 [27% of iterations ranked as No. 1]; symptoms: total score, 7.1 [44% of participants ranked as No. 1]). Rankings for remaining health care processes varied by goal. In this consensus statement, consideration of patient-centered health goals led to varied recommended decision-making criteria and prioritization of health care processes by subject matter experts. For patients with multimorbidity, these findings could inform systems evaluating health care process applications (eg, selection of screenings for patient-centered quality initiatives) or clinicians personalizing care delivery for patients (eg, catalyzing conversation about goals or efficiently addressing processes if goals are known).

Background: Clonal hematopoiesis of indeterminate potential (CHIP), the age-related clonal expansion of hematopoietic stem cells with preleukemic driver mutations, is a novel risk factor for cardiovascular diseases (CVD). While associations between CHIP and plasma proteins are emerging, the causal nature of these relationships remains undetermined. Hypothesis: We aimed to identify specific CHIP driver mutations that causally alter plasma protein levels using human genetics and validate key findings in experimental models. Approach: We analyzed 61,833 participants from the NHLBI TOPMed Program (SomaScan for proteomics measurement) and UK Biobank (Olink for proteomics measurement) with paired DNA sequencing and proteomics. Associations between CHIP variables (composite or drivers DNMT3A , TET2 , ASXL1 ) and proteins were analyzed separately by platform. Bi-directional Mendelian Randomization (MR) was performed to assess causal relations between CHIP, DNMT3A , or TET2 and protein levels. Proteins implicated by MR for TET2 (LCN2, MPO, FLT3LG) were selected for validation via ELISA in 8-9 week-old hematopoietic Tet2 -/- vs wild-type (WT) mice. Results: MR analyses identified multiple instances where CHIP likely causes proteomic changes, with less evidence for reverse causality. In TOPMed (SomaScan), we identified 9 causal pairs (FDR&lt;0.05) among 35 (24 examined) significant CHIP-protein pairs, with TET2 causally increasing MPO levels being the strongest (beta [SE]:0.022[0.008]; P=3.6x10 -3 ). In UK Biobank (Olink), among 473 (318 examined) significant CHIP-protein pairs, 121 were causal, with TET2 showing the strongest effects on increased LCN2 (beta[SE]:0.056[0.008]; P=4.3x10 -11 ) and decreased FLT3LG (beta[SE]:-0.089[0.015]; P=1.1x10 -9 ). The causal effect of TET2 on LCN2 was consistent across platforms. Murine experiments corroborated these findings: hematopoietic Tet2 -/- mice exhibited significantly increased plasma MPO and LCN2 levels compared to WT controls. Plasma FLT3LG levels were not significantly different, though a decrease in Tet2 -/- mice was directionally consistent with human MR. Conclusions: This study provides robust human genetic and experimental evidence for causal effects of CHIP, particularly TET2 mutations, on the plasma proteome. This validation of MR-identified proteomic changes strengthens the causal link and offers potential mechanistic insights into how CHIP may influence downstream outcomes, including CVD.