Single ventricle disease is a serious and deadly illness, and advances in clinical management of individuals with Fontan circulation over the past two decades have yet to yield acceptable survival. Patients remain at risk of developing a diverse assortment of Fontan-associated comorbidities that ultimately require a heart transplant. Our goal in this observational cohort study was to determine if application of principal component analysis (PCA) to heterogeneous data collected from a sizable Fontan cohort (n = 140) would predict functional decline. The data, broadly comprised of blood biomarkers, lymphatic biomarkers, measures of cardiac and vascular function, and exercise (VO2max), were collected at a single site over 11 years; 16 events occurred over that time that we consider here as a single composite outcome measure. The standardized data was transformed via PCA, and principal components (PCs) characterizing >5% of total variance were thematically labeled based on their constituents and tested for association with the composite outcome. We found that the 6th PC (PC6), which represents 7.1% of the total variance, is superior to ejection fraction (EF) as a measure of proportional hazard, is greatly influenced by blood serum biomarkers and superior vena cava flow, and displays the greatest accuracy (according to area under the curve analysis) for classifying Fontan patients. In bivariate hazard analysis, we determined that models combining lymphatic dysfunction (PC6) and systolic function (EF or PC5) were most accurate, with the former having the highest c-statistic, and the latter having the greatest AIC. Our findings support our hypothesis that improved prognostication in a Fontan population should utilize a multifactorial model.
Although biodiversity loss is acknowledged as one of the main drivers of financial risk, there is still no clear understanding of how impacts and dependencies on biodiversity affect the financial sector. In fact, nature degradation does not manifest itself as a systemic risk because it does not threaten the very nature of the financial system. There are transmission channels between nature and finance that need to be investigated: the many intermediate cause-and-effect relationships should be identified and assessed. Such a process involves multiple disciplinary domains, ranging from ecology and economics to finance. An Ecosystem Services-based approach may represent a comprehensive framework to (i) reconcile coherently different environmental issues such as climate change, biodiversity loss, pollution and sustainable use of resources, and (ii) connect ecosystems and socio-economic systems. Not only can ecosystem services be assessed, but also ecosystem vulnerabilities which are at the origin of nature-related financial risks. Adopting an ecosystem services-based perspective can be the first step toward building ecologically meaningful and economically useful transmission channels for financial risks.
Spatial transcriptomics technologies have emerged as powerful tools for understanding cellular identity and function within the natural spatial context of tissues. Traditional transcriptomics techniques, such as bulk and single-cell RNA sequencing, lose this spatial information, which is critical for addressing many biological questions. Here, we present a protocol for high-resolution spatial transcriptomics using fixed frozen mouse lung sections mounted on 10X Genomics Xenium slides. This method integrates multiplexed fluorescent in situ hybridization (FISH) with high-throughput imaging to reveal the spatial distribution of mRNA molecules in lung tissue sections, allowing detailed analysis of gene expression changes in a mouse model of pulmonary hypertension (PH). We compared two tissue preparation methods, fixed frozen and fresh frozen, for compatibility with the Xenium platform. Our fixed frozen approach, utilizing a free-floating technique to mount thin lung sections onto Xenium slides at room temperature, preserved tissue integrity and maximized the imaging area, resulting in high-fidelity spatial transcriptomics data. Using a predesigned 379-gene mouse panel, we identified 40 major lung cell types. We detected key cellular changes in PH, including an increase in arterial endothelial cells (AECs) and fibroblasts, alongside a reduction in capillary endothelial cells (CAP1 and CAP2). Through differential gene expression analysis, we observed markers of endothelial-to-mesenchymal transition and fibroblast activation in PH lungs. High-resolution spatial mapping further confirmed increased arterialization in the distal microvasculature. These findings underscore the utility of spatial transcriptomics in preserving the native tissue architecture and enhancing our understanding of cellular heterogeneity in disease. Our protocol provides a reliable method for integrating spatial and transcriptomic data using fixed frozen lung tissues, offering significant potential for future studies in complex diseases such as PH.
This research investigates how different branding aspects influence Generation Z’s intention to purchase newly launched technological products designed for the agricultural sector. Given Gen Z’s strong digital engagement and preference for authenticity, sustainability, and innovation, branding plays a pivotal role in shaping their buying decisions. The study aims to assess the impact of key branding elements—such as brand experience, knowledge, image, trust, and loyalty—on the purchase intention of newly launched technological products with applications in agriculture management and informatics. As agricultural practices increasingly integrate smart farming technologies, data-driven decision-making, and precision agriculture, branding becomes crucial in ensuring the adoption of these innovations. Agricultural informatics—encompassing IoT-based monitoring systems, AI-driven analytics, and automated farm management solutions—relies on user trust and engagement for successful market penetration. Gen Z, a tech-savvy and socially conscious demographic, is particularly responsive to brands that emphasize efficiency, sustainability, and transparency in agricultural innovations. A quantitative research approach was adopted, utilizing a structured questionnaire administered to 302 Generation Z participants. Statistical analyses, including correlation and multiple regression, were conducted to examine the relationships between branding factors and purchasing behavior. The results indicate that online brand experience, brand knowledge, and brand image are the most significant predictors of purchase intention, highlighting the critical role of digital interactions, educational branding, and the perceived value of technology in optimizing agricultural processes. Although brand trust and loyalty influence consumer behavior, their impact is less significant than that of experience and knowledge. Although brand awareness and engagement correlate with purchase intention, they do not independently drive purchasing decisions. The study concludes that companies should prioritize enhancing digital brand experiences, providing transparent information, and reinforcing brand imagery to drive product adoption among Generation Z, particularly in the agricultural sector. As this generation continues to shape market trends, agricultural informatics, and smart farming technologies, businesses must craft branding strategies that align with Gen Z’s digital habits, values, and expectations. Future research should explore the long-term impact of branding on agricultural technology adoption and investigate the role of emerging technologies such as blockchain, AI, and big data in strengthening brand engagement and loyalty within the agricultural sector.
Short videos attract users across various age groups; however, studies focusing on single populations, such as adolescents, have limited the understanding of possible age-related changes and differences in short video use. The aim of this study was to examine age trends in short video use and to identify age differences in the psychological mechanisms underlying use behaviors. A total of 1006 adults aged 18–83 years participated in the study and completed a battery of assessments, including short video use, self-control, social motivation, and covariates. The results showed that age moderated the effects of boredom proneness and fear of missing out on short video use. Self-control was associated with people’s use behavior, and boredom proneness and fear of missing out mediated this association across age. Specifically, older adults’ use was more likely to be associated with alleviating boredom rather than fear of missing out, whereas both were associated with young adults’ use. Investigating these mechanisms may provide a better understanding of the factors that correlate with short video use and help target interventions to different age groups.
