120 research outputs found
Paravasation with cyclophosphamide - Case report of tissue necrosis in a patient with primary breast cancer
No full textBackground: Paravasation is a rare but severe complication of treatment with cytotoxic agents. Some anticancer drugs are considered to be of high toxicity (vesicant), some are merely irritant, and some are regarded as nearly non-toxic to healthy tissue as is the case with cyclophosphamide. Case Report: In this report, we present the first case of severe tissue damage caused by a paravasation of cyclophosphamide in a breast cancer patient receiving chemotherapy. Conclusion: Therefore, every attending oncological physician should be aware of the possibility of severe tissue damage as a consequence of cyclophosphamide paravasation
On the participation of the priests in the Kashubian harvest
No full textThe author presents the selected figures of priests who in their priestly activities worked to preserve the cultural heritage of the Kashubian region. Among those portrayed in the article we can find: Father Henryk Mross, Father Janusz Stanisław Pasierb, Father Władysław Łęga, Father Franciszek Kamecki, Father Bernard Sychta, Ph.D. The author highly appreciates the most recent, valuable, lexicographical source i.e. The Biographical Dictionary of the Kashubian Region by R. Szwocha. The article concludes that the region’s revival would not have taken place if it had not been for the legacy of the extraordinary priests
Automatic Air-Coupled Ultrasound Detection of Impact Damages in Fiber-Reinforced Composites Based on One-Dimension Deep Learning Models
No full textImpact damage constitutes a major threat to the performance and safety of fiber-reinforced composites. In this regard, transmission air-coupled ultrasound inspection technology has been identified as an ideal method for detection of common structural defects in modern multilayer composites. However, traditional machine learning algorithms and ultrasonic signal analysis methods are limited in terms of efficiency and accuracy. To remedy the situation, four one-dimensional deep learning models based on A-scan signals obtained from air-coupled ultrasound, which can automatically detect the impact damage in fiber-reinforced polymer composites, are constructed in this paper. Remarkably, all four models have attained high accuracy and recall on the testing sets, even though the training data and test data correspond to different materials and even structures. Among the four models, the long short-term memory recurrent neural network outperforms the other three models, which demonstrates its robustness and effectiveness
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