Publications by authors named "V Cauda"
Coronavirus disease 2019 (COVID-19) has affected not only individual lives but also the world and global systems, both natural and human-made. Besides millions of deaths and environmental challenges, the rapid spread of the infection and its very high socioeconomic impact have affected healthcare, economic status and wealth, and mental health across the globe. To better appreciate the pandemic's influence, multidisciplinary and interdisciplinary approaches are needed.
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- Emulsions stabilized by Pickering particles are gaining popularity due to their stability and compatibility, prompting the need to develop effective production methods for these particles.
- A crystal engineering approach was used to control the size, shape, and structure of curcumin crystals, resulting in hydrophobic sub-micron particles that stabilize water-in-oil emulsions.
- Advanced analytical techniques were employed to characterize the curcumin particles, enhancing the understanding of how their properties impact Pickering stabilization, which could lead to new applications in various industries like pharmaceuticals and food science.
Ultrasound radiation has been widely used in biomedical application for both diagnosis and therapy. Metal oxides nanoparticles (NPs), like ZnO or TiO NPs, have been widely demonstrated to act as excellent sonocatalysts and significantly enhance cavitation at their surface, making them optimal for sonodynamic cancer therapy. These NPs often possess semiconductive and piezoelectric properties that contribute to the complex phenomena occurring at the water-oxide interface during sonostimulation.
View Article and Find Full Text PDFRhodamine B (RhB) is a harmful dye released by industrial wastewaters, thus necessitating its urgent removal. Advanced oxidation processes constitute promising strategies to purify polluted water. Among others, photocatalysis relies on reactive oxygen species (ROS) produced by photocatalytic particles, typically semiconductors like titania or zinc oxide (ZnO), excited by solar or UV-Vis light.
View Article and Find Full Text PDFDevelopment and external validation of a machine learning model for the prediction of persistent acute kidney injury stage 3 in multi-centric, multi-national intensive care cohorts.
Crit Care
June 2024
Background: The aim of this retrospective cohort study was to develop and validate on multiple international datasets a real-time machine learning model able to accurately predict persistent acute kidney injury (AKI) in the intensive care unit (ICU).
Methods: We selected adult patients admitted to ICU classified as AKI stage 2 or 3 as defined by the "Kidney Disease: Improving Global Outcomes" criteria. The primary endpoint was the ability to predict AKI stage 3 lasting for at least 72 h while in the ICU.