External Validation of Persistent Severe Acute Kidney Injury Prediction With Machine Learning Model.

Objective: To externally validate the persistent electronic alert (PersEA) machine learning model for predicting persistent severe acute kidney injury (psAKI), addressing the scarcity of validated prediction models.

Patients And Methods: We included adult patients (18 years or older) admitted to intensive care unit with at least stage 2 acute kidney injury (AKI) at a tertiary medical center, using retrospective data collected between January 1st, 2017 and December 31st, 2022. The data were accessed and analyzed during the period from March 1st, 2023, through July 28th, 2023.

Development and validation of a machine learning model for real-time prediction of invasive mechanical ventilation weaning readiness.

Purpose: To develop and validate a bedside machine learning (ML) decision support tool for prediction of invasive mechanical ventilation (IMV) weaning readiness.

Methods: Adults admitted after 2010 who underwent IMV (>24 h) were included from MIMIC-IV (development and internal validation) and AmsterdamUMCdb (external validation) databases. XGBoost boosted trees approach was used to develop three models predicting IMV weaning readiness within 24, 48, and 72 h by integrating electronic health record data.

Development 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.

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.

Continuous and early prediction of future moderate and severe Acute Kidney Injury in critically ill patients: Development and multi-centric, multi-national external validation of a machine-learning model.

Background: Acute Kidney Injury (AKI) is a major complication in patients admitted to Intensive Care Units (ICU), causing both clinical and economic burden on the healthcare system. This study develops a novel machine-learning (ML) model to predict, with several hours in advance, the AKI episodes of stage 2 and 3 (according to KDIGO definition) acquired in ICU.

Methods: A total of 16'760 ICU adult patients from 145 different ICU centers and 3 different countries (US, Netherland, Italy) are retrospectively enrolled for the study.

External validation of a deep-learning model to predict severe acute kidney injury based on urine output changes in critically ill patients.

Objectives: The purpose of this study was to externally validate algorithms (previously developed and trained in two United States populations) aimed at early detection of severe oliguric AKI (stage 2/3 KDIGO) in intensive care units patients.

Methods: The independent cohort was composed of 10'596 patients from the university hospital ICU of Amsterdam (the "AmsterdamUMC database") admitted to their intensive care units. In this cohort, we analysed the accuracy of algorithms based on logistic regression and deep learning methods.

A deep-learning model to continuously predict severe acute kidney injury based on urine output changes in critically ill patients.

Background: Acute Kidney Injury (AKI), a frequent complication of pateints in the Intensive Care Unit (ICU), is associated with a high mortality rate. Early prediction of AKI is essential in order to trigger the use of preventive care actions.

Methods: The aim of this study was to ascertain the accuracy of two mathematical analysis models in obtaining a predictive score for AKI development.

Polydopamine Nanoparticles as an Organic and Biodegradable Multitasking Tool for Neuroprotection and Remote Neuronal Stimulation.

Oxidative stress represents a common issue in most neurological diseases, causing severe impairments of neuronal cell physiological activity that ultimately lead to neuron loss of function and cellular death. In this work, lipid-coated polydopamine nanoparticles (L-PDNPs) are proposed both as antioxidant and neuroprotective agents, and as a photothermal conversion platform able to stimulate neuronal activity. L-PDNPs showed the ability to counteract reactive oxygen species (ROS) accumulation in differentiated SH-SY5Y, prevented mitochondrial ROS-induced dysfunctions and stimulated neurite outgrowth.

Zinc Oxide Nanocrystals and High-Energy Shock Waves: A New Synergy for the Treatment of Cancer Cells.

In the last years, different nanotools have been developed to fight cancer cells. They could be administered alone, exploiting their intrinsic toxicity, or remotely activated to achieve cell death. In the latter case, ultrasound (US) has been recently proposed to stimulate some nanomaterials because of the US outstanding property of deep tissue penetration and the possibility of focusing.

Leveraging re-chargeable nanobubbles on amine-functionalized ZnO nanocrystals for sustained ultrasound cavitation towards echographic imaging.

Nanoparticles able to promote inertial cavitation when exposed to focused ultrasound have recently gained much attention due to their vast range of possible applications in the biomedical field, such as enhancing drug penetration in tumor or supporting ultrasound contrast imaging. Due to their nanometric size, these contrast agents could penetrate through the endothelial cells of the vasculature to target tissues, thus enabling higher imaging resolutions than commercial gas-filled microbubbles. Herein, Zinc Oxide NanoCrystals (ZnO NCs), opportunely functionalized with amino-propyl groups, are developed as novel nanoscale contrast agents that are able, for the first time, to induce a repeatedly and over-time sustained inertial cavitation as well as ultrasound contrast imaging.

Sonophotocatalytic degradation mechanisms of Rhodamine B dye via radicals generation by micro- and nano-particles of ZnO.

In this work, it is proposed an environmental friendly sonophotocatalytic approach to efficiently treat polluted waters from industrial dyes exploiting ZnO micro- and nano-materials. For the first time, we deeply investigated the generation of reactive oxygen species (ROS) under ultrasound stimulation of different ZnO structures by Electron Paramagnetic Resonance Spectroscopy (EPR). Indeed, five zinc oxide (ZnO) micro- and nano-structures, Desert Roses (DRs), Multipods (MPs), Microwires (MWs), Nanoparticles (NPs) and Nanowires (NWs), were studied for the Rhodamine B (RhB) sonodegradation under ultrasonic irradiation.

Nanoparticle-assisted ultrasound: A special focus on sonodynamic therapy against cancer.

At present, ultrasound radiation is broadly employed in medicine for both diagnostic and therapeutic purposes at various frequencies and intensities. In this review article, we focus on therapeutically-active nanoparticles (NPs) when stimulated by ultrasound. We first introduce the different ultrasound-based therapies with special attention to the techniques involved in the oncological field, then we summarize the different NPs used, ranging from soft materials, like liposomes or micro/nano-bubbles, to metal and metal oxide NPs.

Design, Fabrication, and In Vitro Evaluation of Nanoceria-Loaded Nanostructured Lipid Carriers for the Treatment of Neurological Diseases.

Neurodegenerative diseases comprise a large group of disorders characterized by a dramatic synaptic connections loss, occurring as a result of neurodegeneration, which is closely related to the overproduction of reactive oxygen and nitrogen species. Currently, the treatment of neurodegenerative diseases has been limited mainly because of the inability of the synthesized delivery systems to cross the blood-brain barrier and to successfully deliver their therapeutic cargo to the diseased tissue. Taking into consideration the aforementioned limitations, we designed a lipid-based nanotherapeutic vector composed of biomimetic lipids and CeO nanoparticles (nanoceria, NC).

Lipid-Coated Zinc Oxide Nanoparticles as Innovative ROS-Generators for Photodynamic Therapy in Cancer Cells.

In the present paper, we use zinc oxide nanoparticles under the excitation of ultraviolet (UV) light for the generation of Reactive Oxygen Species (ROS), with the aim of further using these species for fighting cancer cells in vitro. Owing to the difficulties in obtaining highly dispersed nanoparticles (NPs) in biological media, we propose their coating with a double-lipidic layer and we evaluate their colloidal stability in comparison to the pristine zinc oxide NPs. Then, using Electron Paramagnetic Resonance (EPR) coupled with the spin-trapping technique, we demonstrate and characterize the ability of bare and lipid-coated ZnO NPs to generate ROS in water only when remotely actuated via UV light irradiation.