A pulmonary hypertension targeted algorithm to improve referral to right heart catheterization: A machine learning approach.

Background: Pulmonary hypertension (PH) is a pathophysiological problem that may involve several clinical symptoms and be linked to various respiratory and cardiovascular illnesses. Its diagnosis is made invasively by Right Cardiac Catheterization (RHC), which is difficult to perform routinely. Aim of the current study was to develop a Machine Learning (ML) algorithm based on the analysis of anamnestic data to predict the presence of an invasively measured PH.

Unstable Delamination Growth in Stiffened Composite Panels Under Cyclic Loading Conditions.

Aeronautical structures can be damaged by objects during operation and maintenance. Indeed, foreign object impacts (FOIs) affect the overall performance of composite structural components. Delamination is the most critical damage mechanism as it is undetectable and develops silently.

Compositional Analysis and Mechanical Recycling of Polymer Fractions Recovered via the Industrial Sorting of Post-Consumer Plastic Waste: A Case Study toward the Implementation of Artificial Intelligence Databases.

Nowadays, society is oriented toward reducing the production of plastics, which have a significant impact on the environment. In this context, the recycling of existing plastic objects is currently a fundamental step in the mitigation of pollution. Very recently, the outstanding development of artificial intelligence (AI) has concerned and continues to involve a large part of the industrial and informatics sectors.

Detection of fibrous microplastics and natural microfibers in fish species (Engraulis encrasicolus, Mullus barbatus and Merluccius merluccius) for human consumption from the Tyrrhenian sea.

The occurrence of natural/artificial and synthetic microfibers was assessed in three commercial fish species (Engraulis encrasicolus, Mullus barbatus, Merluccius merluccius) from the Tyrrhenian Sea sold for human consumption. The gastrointestinal tracts of n. 150 samples were analyzed, the isolated microfibers were classified applying a morphological approach, based on the analysis of their morphological features, coupled with the identification of the chemical composition of a subsample of microfibers.

Risk score model to automatically detect prostate cancer patients by integrating diagnostic parameters.

Introduction: Prostate cancer (PCa) is one of the prevailing forms of cancer among men. At present, multiparametric MRI is the imaging method for localizing tumors and staging cancer. Radiomics plays a key role and hold potential for PCa detection, reducing the need for unnecessary biopsies, characterizing tumor aggression, and overseeing PCa recurrence post-treatment.