dr inż. Paweł Wityk | Gdańsk University of Technology

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dr inż. Paweł Wityk

Contact:

email:
pawel.wityk@pg.edu.pl
website:
https://mostwiedzy.pl/pawel-wityk,682563-1

Positions:

Assistant professor

dr inż. Paweł Wityk

Publications:

  1. Publication

    - SENSORS - Year 2025

    Biosensors are transforming point-of-care diagnostics by simplifying the detection process and enabling rapid, accurate testing. This study introduces a novel, reusable biosensor designed for direct viral RNA detection from unfiltered saliva, targeting SARS-CoV-2. Unlike conventional methods requiring filtration, our biosensor leverages a unique electrode design that prevents interference from saliva debris, allowing precise measurements....

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  2. Publication

    - Scientific Reports - Year 2024

    The rapid and sensitive indicator of inflammation in the human body is C-Reactive Protein (CRP). Determination of CRP level is important in medical diagnostics because, depending on that factor, it may indicate, e.g., the occurrence of inflammation of various origins, oncological, cardiovascular, bacterial or viral events. In this study, we describe an interferometric sensor able to detect the CRP level for distinguishing between...

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  3. In this article we present the novel spectroscopy method supported with machine learning for real-time detection of infectious agents in wastewater. In the case of infectious diseases, wastewater monitoring can be used to detect the presence of inflammation biomarkers, such as the proposed C-reactive protein, for monitoring inflammatory conditions and mass screening during epidemics for early detection in communities of concern,...

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  4. In this paper, we present an investigation of the influence of the temperature on the sensing of biological samples. We used biofunctionalized microsphere-based fiber-optic sensor to detect immunoglobulin G attached to the sensor head at temperatures relevant in biological research: 5°C, 25°C, and 55°C. The construction of the sensor allowed us to perform measurements in the small amount of solution. The results of our experiment...

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  5. The study presents an optical method supported by machine learning for discriminating urinary tract infections from an infection capable of causing urosepsis. The method comprises spectra of spectroscopy measurement of artificial urine samples with bacteria from solid cultures of clinical E. coli strains. To provide a reliable classification of results assistance of 27 algorithms was tested. We proved that is possible to obtain...

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