Brief Report: Hispanic Patients\u27 Trajectory of Cancer Symptom Burden, Depression, Anxiety, and Quality of Life

Background: Anxiety and depression symptoms are known to increase cancer symptom burden, yet little is known about the longitudinal integrations of these among Hispanic/Latinx patients. The goal of this study was to explore the trajectory and longitudinal interactions among anxiety and depression, cancer symptom burden, and health-related quality of life in Hispanic/Latinx cancer patients undergoing chemotherapy. METHODS: Baseline behavioral assessments were performed before starting chemotherapy. Follow-up behavioral assessments were performed at 3, 6, and 9 months after starting chemotherapy. Descriptive statistics, chi-square tests, Fisher\u27s exact tests, and Mann-Whitney tests explored associations among outcome variables. Adjusted multilevel mixed-effects linear regression models were also used to evaluate the association between HADS scores, follow-up visits, FACT-G scale, MDASI scale, and sociodemographic variables. RESULTS: Increased cancer symptom burden was significantly related to changes in anxiety symptoms\u27 scores (adjusted beta^ = 0.11 [95% CI: 0.02, 0.19]. Increased quality of life was significantly associated with decreased depression and anxiety symptoms (adjusted beta^ = -0.33; 95% CI: -0.47, -0.18, and 0.38 adjusted beta^= -0.38; 95% CI: -0.55, -0.20, respectively). CONCLUSIONS: Findings highlight the need to conduct periodic mental health screenings among cancer patients initiating cancer treatment

Decoding Plant–Environment Interactions That Influence Crop Agronomic Traits

To ensure food security in the face of increasing global demand due to population growth and progressive urbanization, it will be crucial to integrate emerging technologies in multiple disciplines to accelerate overall throughput of gene discovery and crop breeding. Plant agronomic traits often appear during the plants’ later growth stages due to the cumulative effects of their lifetime interactions with the environment. Therefore, decoding plant–environment interactions by elucidating plants’ temporal physiological responses to environmental changes throughout their lifespans will facilitate the identification of genetic and environmental factors, timing and pathways that influence complex end-point agronomic traits, such as yield. Here, we discuss the expected role of the life-course approach to monitoring plant and crop health status in improving crop productivity by enhancing the understanding of plant–environment interactions. We review recent advances in analytical technologies for monitoring health status in plants based on multi-omics analyses and strategies for integrating heterogeneous datasets from multiple omics areas to identify informative factors associated with traits of interest. In addition, we showcase emerging phenomics techniques that enable the noninvasive and continuous monitoring of plant growth by various means, including three-dimensional phenotyping, plant root phenotyping, implantable/injectable sensors and affordable phenotyping devices. Finally, we present an integrated review of analytical technologies and applications for monitoring plant growth, developed across disciplines, such as plant science, data science and sensors and Internet-of-things technologies, to improve plant productivity