Publications by authors named "L Cavaleri"
Plastic waste management has received significant attention in recent decades due to the urgent global environmental crisis caused by plastic pollution. The versatile and durable nature of plastic has led to its widespread usage across various sectors. However, its nonbiodegradable nature contributes to unsustainable production practices, leading to extensive landfill usage and posing threats to marine ecosystems and the food chain.
View Article and Find Full Text PDFBackground: The study aimed to determine the most crucial parameters associated with CVD and employ a novel data ensemble refinement procedure to uncover the optimal pattern of these parameters that can result in a high prediction accuracy.
Methods And Results: Data were collected from 369 patients in total, 281 patients with CVD or at risk of developing it, compared to 88 otherwise healthy individuals. Within the group of 281 CVD or at-risk patients, 53 were diagnosed with coronary artery disease (CAD), 16 with end-stage renal disease, 47 newly diagnosed with diabetes mellitus 2 and 92 with chronic inflammatory disorders (21 rheumatoid arthritis, 41 psoriasis, 30 angiitis).
Article Synopsis
- Complement inhibition shows promise for COVID-19 treatment, and the study aims to identify key genetic variants for predicting patient outcomes using an artificial intelligence-based tool.
- Genetic data from 204 hospitalized COVID-19 patients were analyzed, leading to the identification of 30 predictive variants and a 97% accuracy rate in predicting whether patients would need ICU admission.
- The study highlights the effectiveness of the alpha-index and the DERGA algorithm in accurately determining the relevance of numerous genetic variants for disease outcome prediction.
We aimed to develop a prediction model for intensive care unit (ICU) hospitalization of Coronavirus disease-19 (COVID-19) patients using artificial neural networks (ANN). We assessed 25 laboratory parameters at first from 248 consecutive adult COVID-19 patients for database creation, training, and development of ANN models. We developed a new alpha-index to assess association of each parameter with outcome.
View Article and Find Full Text PDFArticle Synopsis
- There is a need for better early prediction models for COVID-19 outcomes, specifically morbidity and mortality in hospital settings.
- The study identified critical genetic variants related to the complement system that are linked to severe COVID-19 outcomes and developed a predictive artificial neural network (ANN) using these variants.
- The ANN successfully predicted severe outcomes in nearly 90% of patients, highlighting the role of genetic factors in worsening COVID-19 conditions and confirming that these variants are associated with an impaired immune response.