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Since the outbreak of COVID-19, face masks have been introduced in the complex strategy of infection prevention and control. Face masks consist of plastic polymers and additives such as phthalates. The aim of this study was to evaluate the migration of microplastics (MP) and phthalates from face masks to water. Four types of masks including FFP2 masks and surgical were studied. Masks were first characterized to determine the different layers and the material used for their fabrication. Then, masks were cut into 20 pieces of 0.5 cm, including all their layers, placed in water, and the migration of MP and phthalates was evaluated according to the conditions stated in EU Regulation No 10/2011 on plastic materials and articles intended to come into contact with food. For MP, the morphological analysis (shape, dimension, particle count) was performed using a stereomicroscope, while the identification of both masks and MP released was conducted using μ-Fourier-transform infrared spectroscopy (µ-FT-IR). Migration of phthalates was assessed by ultra-high-performance liquid chromatography coupled to triple quadrupole mass spectrometer (UPLC-MS/MS). Face masks analyzed in the present study were made of atactic polypropylene (PP) as stated by the manufacturer. The μ-FT-IR confirmed that PP and polyamide (PA) were released as fragments, while both PP and polyester (PES) were released as fibers. In addition, 4 phthalates were identified at concentrations between 2.34 and 21.0 µg/mask. This study shows that the migration study can be applied to evaluate the potential release of MP and phthalates from face masks to water and could give a hint for the potential impact of their incorrect disposal on the aquatic resources.
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http://dx.doi.org/10.3390/molecules27206859 | DOI Listing |
Photodiagnosis Photodyn Ther
September 2025
Department of Ophthalmology, People's Hospital of Feng Jie, Chongqing, 404600, China. Electronic address:
Objective: This study aims to develop a robust, multi-task deep learning framework that integrates vessel segmentation and radiomic analysis for the automated classification of four retinal conditions- diabetic retinopathy (DR), hypertensive retinopathy (HR), papilledema, and normal fundus-using fundus images.
Materials: AND.
Methods: A total of 2,165 patients from eight medical centers were enrolled.
Cell Rep
September 2025
State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network, School of Life Sciences, Xiamen University, Xiamen, Fujian 361102, China. Electronic address:
Nuclear factor κB (NF-κB) family transcription factors are critical for innate immune responses across a variety of organisms and are frequently dysregulated in diseases. Understanding their homeostatic regulation is essential for developing therapeutic strategies. Relish, a Drosophila homolog of mammalian NF-κB precursors, provides a valuable model for studying these processes.
View Article and Find Full Text PDFObjective: The objective of this retrospective study is to develop and validate an artificial intelligence model constrained by the anatomical structure of the brain with the aim of improving the accuracy of prenatal diagnosis of fetal cerebellar hypoplasia using ultrasound imaging.
Background: Fetal central nervous system dysplasia is one of the most prevalent congenital malformations, and cerebellar hypoplasia represents a significant manifestation of this anomaly. Accurate clinical diagnosis is of great importance for the purpose of prenatal screening of fetal health.
J Emerg Manag
September 2025
University of Houston, Houston, Texas. ORCID: https://orcid.org/0000-0003-1191-1427.
In 2020, emergency operations resources in the United States began responding to the presence of coronavirus disease 2019 and its variants. Mitigation efforts to control the spread of the coronavirus by these organizations included vaccination, increased sanitation, social distancing, and physical barriers such as masks and shields. Due to the nature of the coronavirus and emergency operations requirements, these approaches have proven not be 100 percent effective in fully meeting those needs.
View Article and Find Full Text PDFAcad Radiol
September 2025
In-Service Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan (H.-C.K., S.-J.P.); Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei Medical University, Taipei, Taiwan (S.-J.P.). Electronic address: sjpeng2
Rationale And Objectives: Computed tomography (CT) remains the primary modality for assessing renal tumors; however, tumor identification and segmentation rely heavily on manual interpretation by clinicians, which is time-consuming and subject to inter-observer variability. The heterogeneity of tumor appearance and indistinct margins further complicate accurate delineation, impacting histopathological classification, treatment planning, and prognostic assessment. There is a pressing clinical need for an automated segmentation tool to enhance diagnostic workflows and support clinical decision-making with results that are reliable, accurate, and reproducible.
View Article and Find Full Text PDF