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27 results on '"Pastuszak, Krzysztof"'

2. Neural Graph Collaborative Filtering: Analysis of Possibilities on Diverse Datasets

Author

Kobiela, Dariusz, Groth, Jan, Sieczczyński, Michał, Wolniak, Rafał, Pastuszak, Krzysztof, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Abelló, Alberto, editor, Vassiliadis, Panos, editor, Romero, Oscar, editor, Wrembel, Robert, editor, Bugiotti, Francesca, editor, Gamper, Johann, editor, Vargas Solar, Genoveva, editor, and Zumpano, Ester, editor

Published
2023
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7. Towards Cancer Patients Classification Using Liquid Biopsy

Author

Cygert, Sebastian, Górski, Franciszek, Juszczyk, Piotr, Lewalski, Sebastian, Pastuszak, Krzysztof, Czyżewski, Andrzej, Supernat, Anna, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Rekik, Islem, editor, Adeli, Ehsan, editor, Park, Sang Hyun, editor, and Schnabel, Julia, editor

Published
2021
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10. Improving platelet‐RNA‐based diagnostics: a comparative analysis of machine learning models for cancer detection and multiclass classification.

Author

Jopek, Maksym A., Pastuszak, Krzysztof, Sieczczyński, Michał, Cygert, Sebastian, Żaczek, Anna J., Rondina, Matthew T., and Supernat, Anna

Abstract

Liquid biopsy demonstrates excellent potential in patient management by providing a minimally invasive and cost‐effective approach to detecting and monitoring cancer, even at its early stages. Due to the complexity of liquid biopsy data, machine‐learning techniques are increasingly gaining attention in sample analysis, especially for multidimensional data such as RNA expression profiles. Yet, there is no agreement in the community on which methods are the most effective or how to process the data. To circumvent this, we performed a large‐scale study using various machine‐learning techniques. First, we took a closer look at existing datasets and filtered out some patients to assert data collection quality. The final data collection included platelet RNA samples acquired from 1397 cancer patients (17 types of cancer) and 354 asymptomatic, presumed healthy, donors. Then, we assessed an array of different machine‐learning models and techniques (e.g., feature selection of RNA transcripts) in pan‐cancer detection and multiclass classification. Our results show that simple logistic regression performs the best, reaching a 68% cancer detection rate at a 99% specificity level, and multiclass classification accuracy of 79.38% when distinguishing between five cancer types. In summary, by revisiting classical machine‐learning models, we have exceeded the previously used method by 5% and 9.65% in cancer detection and multiclass classification, respectively. To ease further research, we open‐source our code and data processing pipelines (https://gitlab.com/jopekmaksym/improving‐platelet‐rna‐based‐diagnostics), which we hope will serve the community as a strong baseline. [ABSTRACT FROM AUTHOR]

Published
2024
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15. Platelet-Based Liquid Biopsies through the Lens of Machine Learning

Author

Cygert, Sebastian, primary, Pastuszak, Krzysztof, additional, Górski, Franciszek, additional, Sieczczyński, Michał, additional, Juszczyk, Piotr, additional, Rutkowski, Antoni, additional, Lewalski, Sebastian, additional, Różański, Robert, additional, Jopek, Maksym Albin, additional, Jassem, Jacek, additional, Czyżewski, Andrzej, additional, Wurdinger, Thomas, additional, Best, Myron G., additional, Żaczek, Anna J., additional, and Supernat, Anna, additional

Published
2023
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16. Long-term mortality after transcatheter aortic valve implantation for aortic stenosis in immunosuppression-treated patients: a propensity-matched multicentre retrospective registry-based analysis

Author

Walczewski, Michał, primary, Gąsecka, Aleksandra, additional, Witkowski, Adam, additional, Dabrowski, Maciej, additional, Huczek, Zenon, additional, Wilimski, Radosław, additional, Ochała, Andrzej, additional, Parma, Radosław, additional, Rymuza, Bartosz, additional, Grygier, Marek, additional, Jemielity, Marek, additional, Olasińska-Wiśniewska, Anna, additional, Jagielak, Dariusz, additional, Targoński, Radosław, additional, Pastuszak, Krzysztof, additional, Grešner, Peter, additional, Grabowski, Marcin, additional, and Kochman, Janusz, additional

Published
2023
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17. Long-term mortality after transcatheter aortic valve implantation for aortic stenosis in immunosuppressiontreated patients: a propensity-matched multicentre retrospective registry-based analysis.

Author

Walczewski, Michał, Gąsecka, Aleksandra, Witkowski, Adam, Dabrowski, Maciej, Huczek, Zenon, Wilimski, Radosław, Ochała, Andrzej, Parma, Radosław, Rymuza, Bartosz, Grygier, Marek, Jemielity, Marek, Olasińska-Wiśniewska, Anna, Jagielak, Dariusz, Targoński, Radosław, Pastuszak, Krzysztof, Grešner, Peter, Grabowski, Marcin, and Kochman, Janusz

Subjects
HEART valve prosthesis implantation, AORTIC stenosis, RETROSPECTIVE studies
Abstract

Introduction: Data regarding patients with a previous medical record of immunosuppression treatment who have undergone transcatheter aortic valve implantation (TAVI) are limited and extremely inconclusive. Available studies are mostly short term observations; thus there is a lack of evidence on efficacy and safety of TAVI in this specific group of patients. Aim: To compare the in-hospital and long-term outcomes between patients with or without a medical history of immunosuppressive treatment undergoing TAVI for aortic valve stenosis (AS). Material and methods: We conducted a retrospective registry-based analysis including patients undergoing TAVI for AS at 5 centres between January 2009 and August 2017. The primary endpoint was long-term all-cause mortality. Secondary endpoints comprised major vascular complications, life-threatening or disabling bleeding, stroke and new pacemaker implantation. Results: Of 1451 consecutive patients who underwent TAVI, two propensity-matched groups including 25 patients with a history of immunosuppression and 75 patients without it were analysed. No differences between groups in all-cause mortality were found in a median follow-up time of 2.7 years following TAVI (p = 0.465; HR = 0.73; 95% CI: 0.30-1.77). The rate of major vascular complications (4.0% vs. 5.3%) was similar in the two groups (p = 1.000). There were no statistically significant differences in the composite endpoint combining life-threatening or disabling bleeding, major vascular complications, stroke and new pacemaker implantation (40.0% vs. 20.0%, p = 0.218). Conclusions: Patients who had undergone TAVI for AS had similar long-term mortality regardless of whether they had a previous medical record of immunosuppression. Procedural complication rates were comparable between the groups. [ABSTRACT FROM AUTHOR]

Published
2023
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21. Platelet RNA Sequencing Data Through the Lens of Machine Learning

Author

Cygert, Sebastian, primary, Pastuszak, Krzysztof, additional, Gorski, Franciszek, additional, Sieczczynski, Michal, additional, Juszczyk, Piotr, additional, Rutkowski, Antoni, additional, Lewalski, Sebastian, additional, Rozanski, Robert, additional, Jopek, Maksym, additional, Zaczek, Anna J., additional, Jassem, Jacek, additional, Czyzewski, Andrzej, additional, Wurdinger, Thomas, additional, G. Best, Myron, additional, and Supernat, Anna, additional

Published
2022
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23. Diagnostic Accuracy of Liquid Biopsy in Endometrial Cancer

Author

Łukasiewicz, Marta, primary, Pastuszak, Krzysztof, additional, Łapińska-Szumczyk, Sylwia, additional, Różański, Robert, additional, Veld, Sjors G. J. G. In ‘t, additional, Bieńkowski, Michał, additional, Stokowy, Tomasz, additional, Ratajska, Magdalena, additional, Best, Myron G., additional, Würdinger, Thomas, additional, Żaczek, Anna J., additional, Supernat, Anna, additional, and Jassem, Jacek, additional

Published
2021
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24. Interval Edge Coloring of Bipartite Graphs with Small Vertex Degrees

Author

Ma��afiejska, Anna, Ma��afiejski, Micha��, Ocetkiewicz, Krzysztof M., and Pastuszak, Krzysztof

Subjects
Mathematics of computing ��� Graph coloring, coloring algorithm, biregular graphs, interval edge coloring
Abstract

An edge coloring of a graph G is called interval edge coloring if for each v ��� V(G) the set of colors on edges incident to v forms an interval of integers. A graph G is interval colorable if there is an interval coloring of G. For an interval colorable graph G, by the interval chromatic index of G, denoted by ��'_i(G), we mean the smallest number k such that G is interval colorable with k colors. A bipartite graph G is called (��,��)-biregular if each vertex in one part has degree �� and each vertex in the other part has degree ��. A graph G is called (��*,��*)-bipartite if G is a subgraph of an (��,��)-biregular graph and the maximum degree in one part is �� and the maximum degree in the other part is ��. In the paper we study the problem of interval edge colorings of (k*,2*)-bipartite graphs, for k ��� {3,4,5}, and of (5*,3*)-bipartite graphs. We prove that every (5*,2*)-bipartite graph admits an interval edge coloring using at most 6 colors, which can be found in O(n^{3/2}) time, and we prove that an interval edge 5-coloring of a (5*,2*)-bipartite graph can be found in O(n^{3/2}) time, if it exists. We show that every (4^*,2^*)-bipartite graph admits an interval edge 4-coloring, which can be found in O(n) time. The two following problems of interval edge coloring are known to be NP-complete: 6-coloring of (6,3)-biregular graphs (Asratian and Casselgren (2006)) and 5-coloring of (5*,5*)-bipartite graphs (Giaro (1997)). In the paper we prove NP-completeness of 5-coloring of (5*,3*)-bipartite graphs., LIPIcs, Vol. 212, 32nd International Symposium on Algorithms and Computation (ISAAC 2021), pages 26:1-26:12

Published
2021
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25. imPlatelet classifier: image‐converted RNA biomarker profiles enable blood‐based cancer diagnostics

Author

Pastuszak, Krzysztof, primary, Supernat, Anna, additional, Best, Myron G., additional, In 't Veld, Sjors G.J.G., additional, Łapińska‐Szumczyk, Sylwia, additional, Łojkowska, Anna, additional, Różański, Robert, additional, Żaczek, Anna J., additional, Jassem, Jacek, additional, Würdinger, Thomas, additional, and Stokowy, Tomasz, additional

Published
2021
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27. Hydrogen Production in Institute of Fluid Machinery of Polish Academy of Science in Gdańsk — theory and practice

Author

Sołowski, Gaweł, Czylkowski, Dariusz, Hrycak, Bartosz, Siuzdak, Katarzyna, Pastuszak, Krzysztof, and Cenian, Adam

Abstract

The hydrogen production technologies developed in the Institute of Fluid-Flow Machinery, Polish Academy of Sciences in Gdańsk are discussed here. They include the following methods: dark fermentation, photoelectrochemical water oxidation and hydrocarbons (or alcohols) reforming by microwave plasma. The potential of hydrogen production by using dark fermentation of different popular wastes such as: agricultural wastes, textile or wood waste, was determined using suitable models. Also, the influence of microaeration during dark fermentation of some substrates, e.g. sour cabbage, was tested. Photochemical oxidation is a water-splitting process driven by radiation at the surface of a titanium-oxide anode. The Si microrods covered by titania films were verified as a photoanode material. The hydrogen production from methane, ethanol, isopropanol and kerosene was driven by a microwave plasma. The results obtained confirm that microwave plasma sources have a high potential for hydrogen production via gaseous and liquid fuels reforming., Eco-Energetics: technologies, environment, law and economy, Tom 2 (2019)

Published
2019
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