Publications by authors named "D Fabbri"

Unlabelled: Welding fumes are a main source of occupational exposure to particulate matter (PM), besides gases and ultraviolet radiations, that involves millions of operators worldwide and is related to several health effects, including lung cancer. Our study aims to evaluate the exposure to fine and ultrafine airborne particulate in welding operators working in a steel making factory.In October 2019, air monitoring was performed for four days in five different welding scenarios and in the external area of a steelmaking factory to assess the exposure to airborne particles, ultrafine (UFP) particulate and inhalable fraction, during welding activities.

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In this work, we describe the results of a computational investigation aimed at identifying potential biological targets of honokiol, magnolol and a series of synthetic prodrug derivatives obtained through esterification of the free hydroxyl groups. The ligand-based and structure-based analyses revealed that these compounds potentially interact with several biological targets, some of which are known while others are new. Honokiol, magnolol, and three of the newly synthesized derivatives may bind to estrogen receptors ERα and ERβ.

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Article Synopsis
  • Post-marketing safety surveillance can be improved by detecting clinical events through spontaneous reporting, but it requires healthcare professionals to be well-informed and aware of the reporting process.
  • The study introduces a new method for identifying incidents using unstructured clinical data and natural language processing, validated against traditional methods for two specific health concerns: suicide attempts and sleep-related behaviors.
  • Results showed that while the new approach effectively identified suicide attempts with decent precision, it struggled more with sleep-related behaviors; additionally, performance varied by race, highlighting the need for careful monitoring and bias reduction in healthcare AI applications.
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Background: Recent advancements of large language models (LLMs) like Generative Pre-trained Transformer 4 (GPT-4) have generated significant interest among the scientific community. Yet, the potential of these models to be utilized in clinical settings remains largely unexplored. This study investigated the abilities of multiple LLMs and traditional machine learning models to analyze emergency department (ED) reports and determine if the corresponding visits were caused by symptomatic kidney stones.

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This study reports the first co-infection of and two nematodes affecting canid cardiopulmonary systems, in golden jackals () in Italy. Data on golden jackal carcasses, recovered in Friuli Venezia Giulia (Italy) from 2020 to 2023, were recorded using InfoFaunaFVG wildlife monitoring network. Out of 60 necropsied golden jackals, 24 tested positive for either pathogen, with 3 animals displaying co-infection.

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