ABSTRACT
Introduction: The development of advanced sewage technologies empowers the industry to produce high-quality recycled water, which greatly influences human's life and health. Thus, this study investigates the mechanism of individuals' adoption of recycled water from the technology adoption perspective. Methods: Employing the mixed method of structural equation modeling and artificial neural network analysis, we examined a research model developed from the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) framework. To examine the research model, this study employs a leading web-survey company (Sojump) to collect 308 valid samples from the residents in mainland China. Results: The structural equation modeling results verified the associations between the six predictors (performance expectancy, effort expectancy, social influence, facilitating conditions, environmental motivation, and price value), individuals' cognitive and emotional attitudes, and acceptance intention. The artificial neural network analysis validates and complements the structural equation modeling results by unveiling the importance rank of the significant determinants of the acceptance decisions. Discussion: The study provides theoretical implications for recycled water research and useful insights for practitioners and policymakers to reduce the environmental hazards of water scarcity.
ABSTRACT
Background: Sivelestat, a neutrophil elastase inhibitor, is specifically developed to mitigate the occurrence of acute lung injury (ALI) in individuals who are undergoing cardiovascular surgery. However, its impact on patients who are at a heightened risk of developing ALI after scheduled cardiac surgery has yet to be determined. In order to address this knowledge gap, we undertook a study to assess the efficacy of sivelestat in protecting the lungs of these patients. Methods: We conducted a prospective cohort study involving 718 patients who were at high risk of developing postoperative acute lung injury (ALI) and underwent scheduled cardiac surgery between April 25th, 2022, and September 7th, 2023. Among them, 52 patients received sivelestat (administered at a dosage of 0.2mg/kg/h for 3 days), while 666 patients served as controls, not receiving sivelestat. The control conditions were the same for all patients, including ventilation strategy, extubating time, and fluid management. Subsequently, a propensity-score matched cohort was established, consisting of 40 patients in both the sivelestat and control groups. The primary outcome measure encompassed a composite of adverse outcomes, including 30-day mortality, ALI, acute respiratory distress syndrome (ARDS), and others. Secondary outcomes assessed included pneumonia, ventricular arrhythmias, mechanical ventilation (MV) time, and more. Results: After conducting propensity matching in our study, we observed that there were no significant differences in 30-day mortality between the sivelestat and control groups (0% vs 2.5%, P=0.32). However, the use of sivelestat exhibited a significant reduction in the incidence of acute lung injury/acute respiratory distress syndrome (ALI/ARDS) compared to the control group (0% vs 55%, P<0.01), pneumonia (0 vs 37.5%, P<0.01), MV time (median:8 hours, IQR:4-14.8 hours vs median: 15.2 hours, IQR:14-16.3 hours, P<0.01). Compared to the control group, the sivelestat could significantly decrease white cell count (P<0.01), neutrophile percentage (P<0.01) and C-reactive protein (P<0.01) in the period of postoperative 5 days. Conclusion: The prophylactic administration of sivelestat has shown promising results in reducing the occurrence of acute lung injury/acute respiratory distress syndrome (ALI/ARDS) in patients with a heightened risk of developing these conditions after elective cardiac surgery. Our study findings indicate that sivelestat may provide protective effects by suppressing inflammation triggered by neutrophil activation, thereby safeguarding pulmonary function. Registration: ChiCTR2200059102, https://www.chictr.org.cn/showproj.html?proj=166643.
