One of the social measures applied during the COVID-19 pandemic has been the use of personal protective equipment (PPE)—face masks and gloves. As a result, this waste category has expanded enormously. This study investigates waste management issues from multiple perspectives, including local governments, waste collection companies, and individual citizens in Poland using a telephone survey for institutions and an online questionnaire for individuals. The results of this study show that approximately 80% of local governments in the Silesian region have applied special measures for handling and collection of waste PPE. Only 13% of waste collection companies have applied special collection schedules for the waste generated at quarantine collection points due to the high costs of changing collection schedules, providing additional vehicles, and paying for more labor. The information campaigns focusing on new methods of PPE waste collection have been difficult to introduce on a large scale, and citizens need better information regarding how to handle and dispose of waste PPE. Results indicated the most helpful method in supporting waste PPE collection would be automatic PPE dispensers with waste PPE collection options and waste bags of a designated color. The respondents identified waste PPE pollution of the environment as an issue and the necessity for proper recovery of this waste stream.
PRIMAGE is one of the largest and more ambitious research projects dealing with medical imaging, artificial intelligence and cancer treatment in children. It is a 4-year European Commission-financed project that has 16 European partners in the consortium, including the European Society for Paediatric Oncology, two imaging biobanks, and three prominent European paediatric oncology units. The project is constructed as an observational in silico study involving high-quality anonymised datasets (imaging, clinical, molecular, and genetics) for the training and validation of machine learning and multiscale algorithms. The open cloud-based platform will offer precise clinical assistance for phenotyping (diagnosis), treatment allocation (prediction), and patient endpoints (prognosis), based on the use of imaging biomarkers, tumour growth simulation, advanced visualisation of confidence scores, and machine-learning approaches. The decision support prototype will be constructed and validated on two paediatric cancers: neuroblastoma and diffuse intrinsic pontine glioma. External validation will be performed on data recruited from independent collaborative centres. Final results will be available for the scientific community at the end of the project, and ready for translation to other malignant solid tumours.
The objective of this study was to investigate free amino acids composition of Polish honeys with different botanical origin. Honeys (n=18) with dominant buckwheat, raspberry, acacia, heather and goldenrod pollen, and honeydew honey were analysed. For determination of free amino acids liquid chromatography methods were applied. Identifi cation of 25 free amino acids was performed. Considerable variation in the total content of free amino acids ranging from 186.19 mg/kg to 921.08 mg/kg was stated. The dominant free amino acid in all types of honey was proline with the highest detected amount in one sample of heather honey 387.88 mg/kg. As an indicator of honeys with predominant raspberry and buckwheat pollen high concentrations of aspartic acid and asparagine (accounting for ca. 20 and more mg/kg) are suggested. The content of tyrosine, leucine, isoleucine and valine ranging from 10 to ca. 20 mg/kg was characteristic of raspberry and at concentrations above 20 mg/kg of buckwheat honeys. The cluster analysis showed the closest correlation between heather and goldenrod honeys. The largest distance was stated between buckwheat and all other honey groups. The results show that it was impossible to clearly distinguish the botanical origins of Polish honey samples based on their amino acid composition.
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