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Author: Popp, Jürgen × End date: 2023 × Start date: 2020 × Subject: Humans ×

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    Fiber enhanced Raman spectroscopic analysis as a novel method for diagnosis and monitoring of diseases related to hyperbilirubinemia and hyperbiliverdinemia
    (Cambridge : Soc., 2016) Yan, Di; Domes, Christian; Domes, Robert; Frosch, Timea; Popp, Jürgen; Pletz, Mathias W.; Frosch, Torsten
    Fiber enhanced resonance Raman spectroscopy (FERS) is introduced for chemically selective and ultrasensitive analysis of the biomolecules hematin, hemoglobin, biliverdin, and bilirubin. The abilities for analyzing whole intact, oxygenated erythrocytes are proven, demonstrating the potential for the diagnosis of red blood cell related diseases, such as different types of anemia and hemolytic disorders. The optical fiber enables an efficient light-guiding within a miniaturized sample volume of only a few micro-liters and provides a tremendously improved analytical sensitivity (LODs of 0.5 μM for bilirubin and 0.13 μM for biliverdin with proposed improvements down to the pico-molar range). FERS is a less invasive method than the standard ones and could be a new analytical method for monitoring neonatal jaundice, allowing a precise control of the unconjugated serum bilirubin levels, and therefore, providing a better prognosis for newborns. The potential for sensing very low concentrations of the bile pigments may also open up new opportunities for cancer research. The abilities of FERS as a diagnostic tool are explored for the elucidation of jaundice with different etiologies including the rare, not yet well understood diseases manifested in green jaundice. This is demonstrated by quantifying clinically relevant concentrations of bilirubin and biliverdin simultaneously in the micro-molar range: for the case of hyperbilirubinemia due to malignancy, infectious hepatitis, cirrhosis or stenosis of the common bile duct (1 μM biliverdin together with 50 μM bilirubin) and for hyperbiliverdinemia (25 μM biliverdin and 75 μM bilirubin). FERS has high potential as an ultrasensitive analytical technique for a wide range of biomolecules and in various life-science applications.
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    Deep learning-based classification of blue light cystoscopy imaging during transurethral resection of bladder tumors
    ([London] : Macmillan Publishers Limited, part of Springer Nature, 2021) Ali, Nairveen; Bolenz, Christian; Todenhöfer, Tilman; Stenzel, Arnulf; Deetmar, Peer; Kriegmair, Martin; Knoll, Thomas; Porubsky, Stefan; Hartmann, Arndt; Popp, Jürgen; Kriegmair, Maximilian C.; Bocklitz, Thomas
    Bladder cancer is one of the top 10 frequently occurring cancers and leads to most cancer deaths worldwide. Recently, blue light (BL) cystoscopy-based photodynamic diagnosis was introduced as a unique technology to enhance the detection of bladder cancer, particularly for the detection of flat and small lesions. Here, we aim to demonstrate a BL image-based artificial intelligence (AI) diagnostic platform using 216 BL images, that were acquired in four different urological departments and pathologically identified with respect to cancer malignancy, invasiveness, and grading. Thereafter, four pre-trained convolution neural networks were utilized to predict image malignancy, invasiveness, and grading. The results indicated that the classification sensitivity and specificity of malignant lesions are 95.77% and 87.84%, while the mean sensitivity and mean specificity of tumor invasiveness are 88% and 96.56%, respectively. This small multicenter clinical study clearly shows the potential of AI based classification of BL images allowing for better treatment decisions and potentially higher detection rates.