In the last years, many studies have focused on risk assessment of exposure of workers to airborne particulate matter (PM). Several studies indicate a strong correlation between PM and adverse health outcomes, as a function of particle size. In the last years, the study of atmospheric particulate matter has focused more on particles less than 10 μm or 2.5 μm in diameter; however, recent studies identify in particles less than 0.1 μm the main responsibility for negative cardiovascular effects. The present paper deals with the determination of 66 organic compounds belonging to six different classes of persistent organic pollutants (POPs) in the ultrafine, fine and coarse fractions of PM (PM < 0.1 µm; 0.1 < PM < 2.5 µm and 2.5 < PM < 10 µm) collected in three outdoor workplaces and in an urban outdoor area. Data obtained were analyzed with principal component analysis (PCA), in order to underline possible correlation between sites and classes of pollutants and characteristic emission sources. Emission source studies are, in fact, a valuable tool for both identifying the type of emission source and estimating the strength of each contamination source, as useful indicator of environment healthiness. Moreover, both carcinogenic and non-carcinogenic risks were determined in order to estimate human health risk associated to study sites. Risk analysis was carried out evaluating the contribution of pollutant distribution in PM size fractions for all the sites. The results highlighted significant differences between the sites and specific sources of pollutants related to work activities were identified. In all the sites and for all the size fractions of PM both carcinogenic and non-carcinogenic risk values were below acceptable and safe levels of risks recommended by the regulatory agencies.
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http://dx.doi.org/10.3390/ijerph18084352 | DOI Listing |
Int J Comput Assist Radiol Surg
January 2025
Advanced Medical Devices Laboratory, Kyushu University, Nishi-ku, Fukuoka, 819-0382, Japan.
Purpose: This paper presents a deep learning approach to recognize and predict surgical activity in robot-assisted minimally invasive surgery (RAMIS). Our primary objective is to deploy the developed model for implementing a real-time surgical risk monitoring system within the realm of RAMIS.
Methods: We propose a modified Transformer model with the architecture comprising no positional encoding, 5 fully connected layers, 1 encoder, and 3 decoders.
Pain Med
January 2025
IRCCS IstitutoOrtopedico Galeazzi, Unit of Clinical Epidemiology, Milan, Italy.
Objective: To assess the effectiveness of cognitive functional therapy (CFT) in reducing disability and pain compared to other interventions in chronic spinal pain patients.
Methods: Five databases were queried to October 2023 for retrieving randomized controlled trials (RCTs), including patients with chronic spinal pain and administering CFT. Primary outcomes were disability and pain.
Integr Cancer Ther
January 2025
Guang 'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
Background: The incidence and mortality of lung cancer is the highest among malignant tumors worldwide, and it seriously threatens human life and health. Surgery is the primary radical treatment for lung cancer. However, patients often experience discomfort, changes in social roles, economic pressures, and other postsurgical challenges.
View Article and Find Full Text PDFJ Sex Med
January 2025
Clinical Obstetric and Gynecological V Buzzi, ASST-FBF-Sacco, Via Castelvetro 24-20124-University of the Study of Milan, Milan, Italy.
Background: Vulvodynia is a multifactorial disease affecting 7%-16% of reproductive-aged women in general population; however, little is still known about the genetics underlying this complex disease.
Aim: To compare polygenic risk scores for hormones and receptors levels in a case-control study to investigate their role in vulvodynia and their correlation with clinical phenotypes.
Methods: Our case-control study included patients with vestibulodynia (VBD) and healthy women.
BMC Oral Health
January 2025
Sub-Institute of Public Safety Standardization, China National Institute of Standardization, No.4 Zhichun Road, Haidian District, Beijing, 100191, PR China.
Background: This study aimed to establish a model for predicting the difficulty of mandibular third molar extraction based on a Bayesian network to meet following requirements: (1) analyse the interaction of the primary risk factors; (2) output quantitative difficulty-evaluation results based on the patient's personal situation; and (3) identify key surgical points and propose surgical protocols to decrease complications.
Methods: Relevant articles were searched to identify risk factors. Clinical knowledge and experience were used to analyse the risk factors to establish the Bayesian network.
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