Background: Italian national legislation guarantees safety and health for workers exposed to artificial optical radiations (AOR) by Legislative Decree 81/2008.
Objectives And Methods: Effects and damages to health resulting from AOR exposure were analyzed from literature data.
Results: Ultraviolet radiations (UV), particularly those in the wavelength range between 220 and 310 nm, causes chronic conjunctivitis and kerato-conjunctivitis. Skin cancer caused by UV exposure included basal cell carcinoma, squamous cell carcinoma and melanoma. As regards Infrared Radiations (portion of the spectrum between 780 nm and 1 mm), the biological effect is essentially of thermal nature. Exposure to blue light (portion ofthe spectrum of visible light radiation in a wavelength range between 380 and 550 nm) causes exclusively retinal damage and is considered to be responsible for the development of situations of age-related macular degeneration (AMD). Even if experimental data are available, at the present time there is still no epidemiological evidence of retinal damage caused by blue light.
Conclusions: The forensic criteria for investigating the causality link between occupational exposure to AOR and damage, and the methodology necessary for the assessment process, are reported. Two lists of occupational diseases which were included in the Italian Ministerial Decrees, issued respectively on April 2008 and 11 December 2009, are also considered. Lastly, on the basis of the current existing guidelines and scientific evidence, the authors propose occupational health surveillance protocols for workers exposed to AOR risk.
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Angew Chem Int Ed Engl
January 2025
Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Key Laboratory of Advanced Marine Materials, 1219 Zhongguan West Road, 315201, Ningbo, CHINA.
Many marine organisms feature sensitive sensory-perceptual systems to sense the surrounding environment and respond to disturbance with intense bioluminescence. However, it remains a great challenge to develop artificial materials that can sense external disturbance and simultaneously activate intense luminescence, although such materials are attractive for visual sensing and intelligent displays. Herein, we present a new class of bioinspired smart gels constructed by integrating hydrophilic polymeric networks, metastable supersaturated salt and fluorophores containing heterogenic atoms.
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January 2025
Department of Genetic Engineering, Computational Biology Lab, School of Bioengineering, SRM Institute of Science and Technology, SRM Nagar, Chennai, India.
Cell-penetrating peptides (CPPs) are highly effective at passing through eukaryotic membranes with various cargo molecules, like drugs, proteins, nucleic acids, and nanoparticles, without causing significant harm. Creating drug delivery systems with CPP is associated with cancer, genetic disorders, and diabetes due to their unique chemical properties. Wet lab experiments in drug discovery methodologies are time-consuming and expensive.
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January 2025
Department of Ophthalmology, Gangnam Severance Hospital, Institute of Vision Research, Yonsei University College of Medicine, 211, Eonjuro, Gangnam-gu, Seoul, 06273, Republic of Korea.
Branch retinal vein occlusion (BRVO) is a leading cause of visual impairment in working-age individuals, though predicting its occurrence from retinal vascular features alone remains challenging. We developed a deep learning model to predict BRVO based on pre-onset, metadata-matched fundus hemisection images. This retrospective cohort study included patients diagnosed with unilateral BRVO from two Korean tertiary centers (2005-2023), using hemisection fundus images from 27 BRVO-affected eyes paired with 81 unaffected hemisections (27 counter and 54 contralateral) for training.
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January 2025
Department of Mathematics, College of Science and Humanities in Alkharj, Prince Sattam bin AbdulazizUniversity, Alkharj, 11942, Saudi Arabia.
BMJ Open Ophthalmol
January 2025
Department of Ophthalmology, Peking University People's Hospital, Beijing, China
Purpose: To develop an artificial intelligence algorithm to automatically identify the anterior segment structures and assess multiple parameters of primary angle closure disease (PACD) in ultrasound biomicroscopy (UBM) images.
Design: Development and validation of an artificial intelligence algorithm for UBM images.
Methods: 2339 UBM images from 592 subjects were collected for algorithm development.
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