Nano-enabled products (NEPs) represent a growing economic global market that integrates nanotechnology into our everyday lives. Increased consumer use and disposal of NEPs at their end of life has led to increased environmental, health and safety (EHS) concerns, due to the potential environmental release of constituent engineered nanomaterials (ENMs) used in the production of NEPs. Although, there is an urgent need to assess particulate matter (PM) release scenarios and potential EHS implications, no current standardized methodologies exist across the exposure-toxicological characterization continuum. Here, an integrated methodology is presented, that can be used to sample, extract, disperse and estimate relevant dose of life cycle-released PM (LCPM), for in vitro and in vivo toxicological studies. The proposed methodology was utilized to evaluate two "real world" LCPM systems simulating consumer use and disposal of NEPs. This multi-step integrated methodology consists of: (1) real-time monitoring and sampling of size fractionated LCPM; (2) efficient extraction of LCPM collected on substrates using aqueous or ethanol extraction protocols to ensure minimal physicochemical alterations; (3) optimized LCPM dispersion preparation and characterization; (4) use of dosimetric techniques for in vitro and in vivo toxicological studies. This comprehensive framework provides a standardized protocol to assess the release and toxicological implications of ENMs released across the life cycle of NEPs and will help in addressing important knowledge gaps in the field of nanotoxicology.
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http://dx.doi.org/10.1093/toxsci/kfv095 | DOI Listing |
Gastric Cancer
January 2025
Department of Medical Oncology, Hospital Clinico Universitario, INCLIVA, Biomedical Research Institute, University of Valencia, Avenida Menendez Pelayo nro 4 accesorio, Valencia, Spain.
Introduction: Gastric cancer (GC) burden is currently evolving with regional differences associated with complex behavioural, environmental, and genetic risk factors. The LEGACy study is a Horizon 2020-funded multi-institutional research project conducted prospectively to provide comprehensive data on the tumour biological characteristics of gastroesophageal cancer from European and LATAM countries.
Material And Methods: Treatment-naïve advanced gastroesophageal adenocarcinoma patients were prospectively recruited in seven European and LATAM countries.
J Cancer Res Clin Oncol
January 2025
Sarcoma Unit, Department of Surgery, University Medical Center and Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany.
Purpose: The management of soft tissue sarcoma (STS) at reference centers with specialized multidisciplinary tumor boards (MTB) improves patient survival. The German Cancer Society (DKG) certifies sarcoma centers in German-speaking countries, promoting high standards of care. This study investigated the variability in treatment recommendations for localized STS across different German-speaking tertiary sarcoma centers.
View Article and Find Full Text PDFTech Coloproctol
January 2025
Colorectal Surgery Unit, Vall d'Hebron University Hospital, Universitat Autònoma de Barcelona UAB, Barcelona, Spain.
Background: Patients with rectal cancer often experience adverse effects on urinary, sexual, and digestive functions. Despite recognised impacts and available treatments, they are not fully integrated into follow-up protocols, thereby hindering appropriate interventions. The aim of the study was to discern the activities conducted in our routine clinical practice outside of clinical trials.
View Article and Find Full Text PDFInt J Comput Assist Radiol Surg
January 2025
Department of Medical Biophysics, University of Toronto, Toronto, Canada.
Purpose: During endovascular revascularization interventions for peripheral arterial disease, the standard modality of X-ray fluoroscopy (XRF) used for image guidance is limited in visualizing distal segments of infrapopliteal vessels. To enhance visualization of arteries, an image registration technique was developed to align pre-acquired computed tomography (CT) angiography images and to create fusion images highlighting arteries of interest.
Methods: X-ray image metadata capturing the position of the X-ray gantry initializes a multiscale iterative optimization process, which uses a local-variance masked normalized cross-correlation loss to rigidly align a digitally reconstructed radiograph (DRR) of the CT dataset with the target X-ray, using the edges of the fibula and tibia as the basis for alignment.
Sci Rep
January 2025
North Carolina School of Science and Mathematics, Durham, NC, 27705, USA.
Mobile Ad Hoc Networks (MANETs) are increasingly replacing conventional communication systems due to their decentralized and dynamic nature. However, their wireless architecture makes them highly vulnerable to flooding attacks, which can disrupt communication, deplete energy resources, and degrade network performance. This study presents a novel hybrid deep learning approach integrating Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures to effectively detect and mitigate flooding attacks in MANETs.
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