125 research outputs found
Rischi professionali e patologia cutanea in operai addetti a trattamenti conservativi di traversine di legno per ferrovia
No full textIndagine igienico-sanitaria nel settore edile della USL n° 31 Valdichiana: analisi degli infortuni e delle malattie professionali.
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Gli infortuni sul lavoro nel settore del legno e del mobile in 4 USL della Toscana Meridionale e in Provincia di Siena.
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Alpha-Tocopherol and contrast-induced nephropathy: A meta-analysis of randomized controlled trials
No full textBackground: Contrast-induced nephropathy (CIN) is a relevant cause of acute renal dysfunction and is associated with an increased morbidity and mortality. Purpose: Verify the effect of α-tocopherol pre-treatment on CIN prevention in subjects with chronic kidney disease. Methods: A Medline/Embase and clinicaltrials.gov were searched up to May 1st, 2017. Randomized controlled trials recruiting patients undergoing diagnostic or therapeutic radiocontrast infusion comparing the effect of either oral or i.v. multiple administration of pharmacological dose of α-tocopherol in preventing CIN versus placebo were included. A random-effects model, calculating Mantel-Haenszel odds ratio with 95% confidence interval, was applied to study the effect of α-tocopherol on CIN occurrence. Funnel plot analysis was used to assess publication bias, while agreement within studies was measured by the I2 index and tested with the Q-Cochran test. Results: Out of 242 studies, 4 trials were selected. CIN incidence resulted significantly lower in α-tocopherol compared to placebo group (5.8% vs. 15.4%, MH-OR [95% C.I.] 0.34 [0.19 - 0.59]). Alpha-tocopherol treatment was associated with both a tendential higher eGFR (mean difference 2.19 [95% C.I. -0.41; 4.79] mL/min) and lower creatinine level (mean difference -0.06 [95% C.I. -0.21; 0.09] mg/dl) compared to placebo. No relevant publication bias (p = 0.48) and heterogeneity (I2 = 0%; χ2 = 1.01, df = 3 [p = 0.80], I2 = 0%) were evident. Conclusions: Alpha-tocopherol pre-treatment is associated with reduction of incidence of CIN. Its administration deserves to be further explored as a simple and inexpensive tool for CIN prevention
Applications of artificial intelligence for the diagnosis of gastrointestinal diseases
No full textThe development of convolutional neural networks has achieved impressive advances of machine learning in recent years, leading to an increasing use of artificial intelligence (AI) in the field of gastrointestinal (GI) diseases. AI networks have been trained to differentiate benign from malignant lesions, analyze endoscopic and radiological GI images, and assess histological diagnoses, obtaining excellent results and high overall diagnostic accuracy. Nevertheless, there data are lacking on side effects of AI in the gastroenterology field, and high-quality studies comparing the performance of AI networks to health care professionals are still limited. Thus, large, controlled trials in real-time clinical settings are warranted to assess the role of AI in daily clinical practice. This narrative review gives an overview of some of the most relevant potential applications of AI for gastrointestinal diseases, highlighting advantages and main limitations and providing considerations for future development
Diffusion Correction in Fricke Hydrogel Dosimeters: A Deep Learning Approach with 2D and 3D Physics-Informed Neural Network Models
No full textIn this work an innovative approach was developed to address a significant challenge in the field of radiation dosimetry: the accurate measurement of spatial dose distributions using Fricke gel dosimeters. Hydrogels are widely used in radiation dosimetry due to their ability to simulate the tissue-equivalent properties of human tissue, making them ideal for measuring and mapping radiation dose distributions. Among the various gel dosimeters, Fricke gels exploit the radiation-induced oxidation of ferrous ions to ferric ions and are particularly notable due to their sensitivity. The concentration of ferric ions can be measured using various techniques, including magnetic resonance imaging (MRI) or spectrophotometry. While Fricke gels offer several advantages, a significant hurdle to their widespread application is the diffusion of ferric ions within the gel matrix. This phenomenon leads to a blurring of the dose distribution over time, compromising the accuracy of dose measurements. To mitigate the issue of ferric ion diffusion, researchers have explored various strategies such as the incorporation of additives or modification of the gel composition to either reduce the mobility of ferric ions or stabilize the gel matrix. The computational method proposed leverages the power of artificial intelligence, particularly deep learning, to mitigate the effects of ferric ion diffusion that can compromise measurement precision. By employing Physics Informed Neural Networks (PINNs), the method introduces a novel way to apply physical laws directly within the learning process, optimizing the network to adhere to the principles governing ion diffusion. This is particularly advantageous for solving the partial differential equations that describe the diffusion process in 2D and 3D. By inputting the spatial distribution of ferric ions at a given time, along with boundary conditions and the diffusion coefficient, the model can backtrack to accurately reconstruct the original ion distribution. This capability is crucial for enhancing the fidelity of 3D spatial dose measurements, ensuring that the data reflect the true dose distribution without the artifacts introduced by ion migration. Here, multidimensional models able to handle 2D and 3D data were developed and tested against dose distributions numerically evolved in time from 20 to 100 h. The results in terms of various metrics show a significant agreement in both 2D and 3D dose distributions. In particular, the mean square error of the prediction spans the range 1x10-6-1x10-4, while the gamma analysis results in a 90-100% passing rate with 3%/2 mm, depending on the elapsed time, the type of distribution modeled and the dimensionality. This method could expand the applicability of Fricke gel dosimeters to a wider range of measurement tasks, from simple planar dose assessments to intricate volumetric analyses. The proposed technique holds great promise for overcoming the limitations imposed by ion diffusion in Fricke gel dosimeters
Characterization of alanine/EPR pellets for reference dosimetry of UHDR beams of FLASH radiotherapy
No full textDosimetry and first radiobiological assay of multi-Gy, multi-MeV TNSA proton beam with ultrahigh dose-rate
No full textBackground and aim: The potential of a compact, laser-based ion accelerator for radiobiological and medical applications relies heavily on the control of the laser-target source and on the use of custom beam transport and delivery to the final target. Here we show the results of an experimental campaign dedicated to the dosimetry of the beam at the crosswire and the first radiobiological micronuclei (MN) assay study in the regime of ultra-high dose rate at selected proton energies at multi-MeV. Methods: We have developed a proton beamline based on the so-called Target Normal Sheath Acceleration (TNSA) to deliver a proton beam radiobiological applications. We use TNSA driven by a 200 TW ultra intense laser to accelerate protons with a cut-off energy of up to 10 MeV. We use permanent magnet quadrupoles to select protons at 6 MeV and transport them, in the form of a collimated beam, to the final target position in air. Results: We measured the spectrum and the deliverable dose of the proton beam at the sample position for each shot. We also evaluated uniformity across the beam and shot by shot fluctuations. We carried out dosimetric measurements that show a ultra-high instantaneous dose rate, with good uniformity over a cm-scale dimension and good shot to shot reproducibility. A preliminary measurement of radiation damage vs dose based on the MN assay was successfully carried out and compared with conventional X-ray source
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