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1. WRSE -- a non-parametric weighted-resolution ensemble for predicting individual survival distributions in the ICU

Author

Heitz, Jonathan, Ficek, Joanna, Faltys, Martin, Merz, Tobias M., Rätsch, Gunnar, and Hüser, Matthias

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning
Abstract

Dynamic assessment of mortality risk in the intensive care unit (ICU) can be used to stratify patients, inform about treatment effectiveness or serve as part of an early-warning system. Static risk scoring systems, such as APACHE or SAPS, have recently been supplemented with data-driven approaches that track the dynamic mortality risk over time. Recent works have focused on enhancing the information delivered to clinicians even further by producing full survival distributions instead of point predictions or fixed horizon risks. In this work, we propose a non-parametric ensemble model, Weighted Resolution Survival Ensemble (WRSE), tailored to estimate such dynamic individual survival distributions. Inspired by the simplicity and robustness of ensemble methods, the proposed approach combines a set of binary classifiers spaced according to a decay function reflecting the relevance of short-term mortality predictions. Models and baselines are evaluated under weighted calibration and discrimination metrics for individual survival distributions which closely reflect the utility of a model in ICU practice. We show competitive results with state-of-the-art probabilistic models, while greatly reducing training time by factors of 2-9x., Comment: 9 pages, 6 figures

Published
2020

2. Establishing standardized immune phenotyping of metastatic melanoma by digital pathology

Author

Sobottka, Bettina, Nowak, Marta, Frei, Anja Laura, Haberecker, Martina, Merki, Samuel, Levesque, Mitchell P., Dummer, Reinhard, Moch, Holger, Koelzer, Viktor Hendrik, Aebersold, Rudolf, Ak, Melike, Al-Quaddoomi, Faisal S., Albinus, Jonas, Alborelli, Ilaria, Andani, Sonali, Attinger, Per-Olof, Bacac, Marina, Baumhoer, Daniel, Beck-Schimmer, Beatrice, Beerenwinkel, Niko, Beisel, Christian, Bernasconi, Lara, Bertolini, Anne, Bodenmiller, Bernd, Bonilla, Ximena, Casanova, Ruben, Chevrier, Stéphane, Chicherova, Natalia, D'Costa, Maya, Danenberg, Esther, Davidson, Natalie, Drăganmoch, Monica-Andreea, Engler, Stefanie, Erkens, Martin, Eschbach, Katja, Esposito, Cinzia, Fedier, André, Ferreira, Pedro, Ficek, Joanna, Frey, Bruno, Goetze, Sandra, Grob, Linda, Gut, Gabriele, Günther, Detlef, Haeuptle, Pirmin, Heinzelmann-Schwarz, Viola, Herter, Sylvia, Holtackers, Rene, Huesser, Tamara, Irmisch, Anja, Jacob, Francis, Jacobs, Andrea, Jaeger, Tim M., Jahn, Katharina, James, Alva R., Jermann, Philip M., Kahles, André, Kahraman, Abdullah, Kuebler, Werner, Kuipers, Jack, Kunze, Christian P., Kurzeder, Christian, Lehmann, Kjong-Van, Lugert, Sebastian, Maass, Gerd, Manz, Markus G., Markolin, Philipp, Mena, Julien, Menzel, Ulrike, Metzler, Julian M., Miglino, Nicola, Milani, Emanuela S., Muenst, Simone, Murri, Riccardo, Ng, Charlotte K.Y., Nicolet, Stefan, Pedrioli, Patrick G.A., Pelkmans, Lucas, Piscuoglio, Salvatore, Prummer, Michael, Ritter, Mathilde, Rommel, Christian, Rosano-González, María L., Rätsch, Gunnar, Santacroce, Natascha, del Castillo, Jacobo Sarabia, Schlenker, Ramona, Schwalie, Petra C., Schwan, Severin, Schär, Tobias, Senti, Gabriela, Singer, Franziska, Sivapatham, Sujana, Snijder, Berend, Sreedharan, Vipin T., Stark, Stefan, Stekhoven, Daniel J., Theocharides, Alexandre P.A., Thomas, Tinu M., Tolnay, Markus, Tosevski, Vinko, Toussaint, Nora C., Tuncel, Mustafa A., Tusup, Marina, Van Drogen, Audrey, Vetter, Marcus, Vlajnic, Tatjana, Weber, Sandra, Weber, Walter P., Wegmann, Rebekka, Weller, Michael, Wendt, Fabian, Wey, Norbert, Wicki, Andreas, Wildschut, Mattheus HE, Wollscheid, Bernd, Yu, Shuqing, Ziegler, Johanna, Zimmermann, Marc, Zoche, Martin, and Zuend, Gregor

Published
2021
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7. WRSE - a non-parametric weighted-resolution ensemble for predicting individual survival distributions in the ICU

Author

Heitz, Jonathan, Ficek, Joanna, Faltys, Martin, Merz, Tobias M., Rätsch, Gunnar, Hüser, Matthias, Greiner, Russell, Kumar, Neeraj, Gerds, Thomas Alexander, and van der Schaar, Mihaela

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Statistics - Machine Learning, Machine Learning (stat.ML), intensive care unit, survival analysis, individual survival distribution, Machine Learning (cs.LG)
Abstract

Dynamic assessment of mortality risk in the intensive care unit (ICU) can be used to stratify patients, inform about treatment effectiveness or serve as part of an early-warning system. Static risk scoring systems, such as APACHE or SAPS, have recently been supplemented with data-driven approaches that track the dynamic mortality risk over time. Recent works have focused on enhancing the information delivered to clinicians even further by producing full survival distributions instead of point predictions or fixed horizon risks. In this work, we propose a non-parametric ensemble model, Weighted Resolution Survival Ensemble (WRSE), tailored to estimate such dynamic individual survival distributions. Inspired by the simplicity and robustness of ensemble methods, the proposed approach combines a set of binary classifiers spaced according to a decay function reflecting the relevance of short-term mortality predictions. Models and baselines are evaluated under weighted calibration and discrimination metrics for individual survival distributions which closely reflect the utility of a model in ICU practice. We show competitive results with state-of-the-art probabilistic models, while greatly reducing training time by factors of 2-9x., 9 pages, 6 figures

Published
2021

8. SCIM: universal single-cell matching with unpaired feature sets

Author

Stark, Stefan G, Ficek, Joanna, Locatello, Francesco, Bonilla, Ximena, Chevrier, Stéphane, Singer, Franziska, Aebersold, Rudolf, Al-Quaddoomi, Faisal S, Albinus, Jonas, Alborelli, Ilaria, Andani, Sonali, Attinger, Per-Olof, Bacac, Marina, Baumhoer, Daniel, Beck-Schimmer, Beatrice, Beerenwinkel, Niko, Beisel, Christian, Bernasconi, Lara, Bertolini, Anne, Bodenmiller, Bernd, Casanova, Ruben, Chicherova, Natalia, D'Costa, Maya, Danenberg, Esther, Davidson, Natalie, gan, Monica-Andreea Dră, Dummer, Reinhard, Engler, Stefanie, Erkens, Martin, Eschbach, Katja, Esposito, Cinzia, Fedier, André, Ferreira, Pedro, Frei, Anja L, Frey, Bruno, Goetze, Sandra, Grob, Linda, Gut, Gabriele, Günther, Detlef, Haberecker, Martina, Haeuptle, Pirmin, Heinzelmann-Schwarz, Viola, Herter, Sylvia, Holtackers, Rene, Huesser, Tamara, Irmisch, Anja, Jacob, Francis, Jacobs, Andrea, Jaeger, Tim M, Jahn, Katharina, James, Alva R, Jermann, Philip M, Kahles, André, Kahraman, Abdullah, Koelzer, Viktor H, Kuebler, Werner, Kuipers, Jack, Kunze, Christian P, Kurzeder, Christian, Lehmann, Kjong-Van, Levesque, Mitchell, Lugert, Sebastian, Maass, Gerd, Manz, Markus, Markolin, Philipp, Mena, Julien, Menzel, Ulrike, Metzler, Julian M, Miglino, Nicola, Milani, Emanuela S, Moch, Holger, Muenst, Simone, Murri, Riccardo, Ng, Charlotte KY, Nicolet, Stefan, Nowak, Marta, Pedrioli, Patrick GA, Pelkmans, Lucas, Piscuoglio, Salvatore, Prummer, Michael, Ritter, Mathilde, Rommel, Christian, Rosano-González, María L, Rätsch, Gunnar, Santacroce, Natascha, Castillo, Jacobo Sarabia del, Schlenker, Ramona, Schwalie, Petra C, Schwan, Severin, Schär, Tobias, Senti, Gabriela, Sivapatham, Sujana, Snijder, Berend, Sobottka, Bettina, Sreedharan, Vipin T, Stark, Stefan, Stekhoven, Daniel J, Theocharides, Alexandre PA, Thomas, Tinu M, Tolnay, Markus, Tosevski, Vinko, Toussaint, Nora C, Tuncel, Mustafa A, Tusup, Marina, Drogen, Audrey Van, Vetter, Marcus, Vlajnic, Tatjana, Weber, Sandra, Weber, Walter P, Wegmann, Rebekka, Weller, Michael, Wendt, Fabian, Wey, Norbert, Wicki, Andreas, Wollscheid, Bernd, Yu, Shuqing, Ziegler, Johanna, Zimmermann, Marc, Zoche, Martin, Zuend, Gregor, and University of Zurich

Subjects
Statistics and Probability, 1303 Biochemistry, AcademicSubjects/SCI01060, Computer science, 610 Medicine & health, computer.software_genre, Biochemistry, 03 medical and health sciences, 0302 clinical medicine, Text mining, 1312 Molecular Biology, 1706 Computer Science Applications, Humans, Profiling (information science), 2613 Statistics and Probability, Molecular Biology, 030304 developmental biology, Data, 0303 health sciences, Sequence Analysis, RNA, business.industry, Gene Expression Profiling, Autoencoder, Computer Science Applications, Computational Mathematics, Computational Theory and Mathematics, 10032 Clinic for Oncology and Hematology, Bipartite graph, Data mining, Single-Cell Analysis, business, computer, 2605 Computational Mathematics, Algorithms, Software, 030217 neurology & neurosurgery, Data integration, 1703 Computational Theory and Mathematics
Abstract

Motivation: Recent technological advances have led to an increase in the production and availability of single-cell data. The ability to integrate a set of multi-technology measurements would allow the identification of biologically or clinically meaningful observations through the unification of the perspectives afforded by each technology. In most cases, however, profiling technologies consume the used cells and thus pairwise correspondences between datasets are lost. Due to the sheer size single-cell datasets can acquire, scalable algorithms that are able to universally match single-cell measurements carried out in one cell to its corresponding sibling in another technology are needed. Results: We propose Single-Cell data Integration via Matching (SCIM), a scalable approach to recover such correspondences in two or more technologies. SCIM assumes that cells share a common (low-dimensional) underlying structure and that the underlying cell distribution is approximately constant across technologies. It constructs a technology-invariant latent space using an autoencoder framework with an adversarial objective. Multi-modal datasets are integrated by pairing cells across technologies using a bipartite matching scheme that operates on the low-dimensional latent representations. We evaluate SCIM on a simulated cellular branching process and show that the cell-to-cell matches derived by SCIM reflect the same pseudotime on the simulated dataset. Moreover, we apply our method to two real-world scenarios, a melanoma tumor sample and a human bone marrow sample, where we pair cells from a scRNA dataset to their sibling cells in a CyTOF dataset achieving 90% and 78% cell-matching accuracy for each one of the samples, respectively., Bioinformatics, 36 (S2), ISSN:1367-4803, ISSN:1460-2059

Published
2020

9. The Tumor Profiler Study: integrated, multi-omic, functional tumor profiling for clinical decision support

Author

Irmisch, Anja, primary, Bonilla, Ximena, additional, Chevrier, Stéphane, additional, Lehmann, Kjong-Van, additional, Singer, Franziska, additional, Toussaint, Nora C., additional, Esposito, Cinzia, additional, Mena, Julien, additional, Milani, Emanuela S., additional, Casanova, Ruben, additional, Stekhoven, Daniel J., additional, Wegmann, Rebekka, additional, Jacob, Francis, additional, Sobottka, Bettina, additional, Goetze, Sandra, additional, Kuipers, Jack, additional, Sarabia del Castillo, Jacobo, additional, Prummer, Michael, additional, Tuncel, Mustafa A., additional, Menzel, Ulrike, additional, Jacobs, Andrea, additional, Engler, Stefanie, additional, Sivapatham, Sujana, additional, Frei, Anja L., additional, Gut, Gabriele, additional, Ficek, Joanna, additional, Miglino, Nicola, additional, Aebersold, Rudolf, additional, Bacac, Marina, additional, Beerenwinkel, Niko, additional, Beisel, Christian, additional, Bodenmiller, Bernd, additional, Dummer, Reinhard, additional, Heinzelmann-Schwarz, Viola, additional, Koelzer, Viktor H., additional, Manz, Markus G., additional, Moch, Holger, additional, Pelkmans, Lucas, additional, Snijder, Berend, additional, Theocharides, Alexandre P.A., additional, Tolnay, Markus, additional, Wicki, Andreas, additional, Wollscheid, Bernd, additional, Rätsch, Gunnar, additional, Levesque, Mitchell P., additional, Ak, Melike, additional, Al-Quaddoomi, Faisal S., additional, Albinus, Jonas, additional, Alborelli, Ilaria, additional, Andani, Sonali, additional, Attinger, Per-Olof, additional, Baumhoer, Daniel, additional, Beck-Schimmer, Beatrice, additional, Bernasconi, Lara, additional, Bertolini, Anne, additional, Chicherova, Natalia, additional, D'Costa, Maya, additional, Danenberg, Esther, additional, Davidson, Natalie, additional, Drăgan, Monica-Andreea, additional, Erkens, Martin, additional, Eschbach, Katja, additional, Fedier, André, additional, Ferreira, Pedro, additional, Frey, Bruno, additional, Grob, Linda, additional, Günther, Detlef, additional, Haberecker, Martina, additional, Haeuptle, Pirmin, additional, Herter, Sylvia, additional, Holtackers, Rene, additional, Huesser, Tamara, additional, Jaeger, Tim M., additional, Jahn, Katharina, additional, James, Alva R., additional, Jermann, Philip M., additional, Kahles, André, additional, Kahraman, Abdullah, additional, Kuebler, Werner, additional, Kunze, Christian P., additional, Kurzeder, Christian, additional, Lugert, Sebastian, additional, Maass, Gerd, additional, Markolin, Philipp, additional, Metzler, Julian M., additional, Muenst, Simone, additional, Murri, Riccardo, additional, Ng, Charlotte K.Y., additional, Nicolet, Stefan, additional, Nowak, Marta, additional, Pedrioli, Patrick G.A., additional, Piscuoglio, Salvatore, additional, Ritter, Mathilde, additional, Rommel, Christian, additional, Rosano-González, María L., additional, Santacroce, Natascha, additional, Schlenker, Ramona, additional, Schwalie, Petra C., additional, Schwan, Severin, additional, Schär, Tobias, additional, Senti, Gabriela, additional, Sreedharan, Vipin T., additional, Stark, Stefan, additional, Thomas, Tinu M., additional, Tosevski, Vinko, additional, Tusup, Marina, additional, Van Drogen, Audrey, additional, Vetter, Marcus, additional, Vlajnic, Tatjana, additional, Weber, Sandra, additional, Weber, Walter P., additional, Weller, Michael, additional, Wendt, Fabian, additional, Wey, Norbert, additional, Wildschut, Mattheus H.E., additional, Yu, Shuqing, additional, Ziegler, Johanna, additional, Zimmermann, Marc, additional, Zoche, Martin, additional, and Zuend, Gregor, additional

Published
2021
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10. SCIM: Universal Single-Cell Matching with Unpaired Feature Sets

Author

Stark, Stefan, Ficek, Joanna, Locatello, Francesco, Bonilla Bustillo, Ximena, Chicherova, Natalia, Singer, Franziska, Tumor Profiler Consortium, Aebersold, Rudolf, Beerenwinkel, Niko, Al-Quaddoomi, Faisal S., Albinus, Jonas, Beisel, Christian, Bertolini, Anne, Davidson, Natalie, Eschbach, Katja, Ferreira, Pedro, Goetze, Sandra, Grob, Linda, Günther, Detlef, Jahn, Katharina, James, Alva R., Kahles, André, Kuipers, Jack, Lehmann, Kjong-Van, Mena, Julien, Menzel, Ulrike, Milani, Emanuela S., Pedrioli, Patrick G.A., Prummer, Michael, Rosano-Gonzalez, Maria L., Rätsch, Gunnar, Schär, Tobias, Snijder, Berend, Thankam Sreedharan, Vipin, Stekhoven, Daniel J., Thomas, Tinu M., Toussaint, Nora C., Tuncel, Mustafa, van Drogen, Audrey, Wegmann, Rebekka, Wendt, Fabian, Wollscheid, Bernd, Yu, Shuqing, and Zimmermann, Marc

Abstract

Motivation Recent technological advances have led to an increase in the production and availability of single-cell data. The ability to integrate a set of multi-technology measurements would allow the identification of biologically or clinically meaningful observations through the unification of the perspectives afforded by each technology. In most cases, however, profiling technologies consume the used cells and thus pairwise correspondences between datasets are lost. Due to the sheer size single-cell datasets can acquire, scalable algorithms that are able to universally match single-cell measurements carried out in one cell to its corresponding sibling in another technology are needed. Results We propose Single-Cell data Integration via Matching (SCIM), a scalable approach to recover such correspondences in two or more technologies. SCIM assumes that cells share a common (low-dimensional) underlying structure and that the underlying cell distribution is approximately constant across technologies. It constructs a technology-invariant latent space using an auto-encoder framework with an adversarial objective. Multi-modal datasets are integrated by pairing cells across technologies using a bipartite matching scheme that operates on the low-dimensional latent representations. We evaluate SCIM on a simulated cellular branching process and show that the cell-to-cell matches derived by SCIM reflect the same pseudotime on the simulated dataset. Moreover, we apply our method to two real-world scenarios, a melanoma tumor sample and a human bone marrow sample, where we pair cells from a scRNA dataset to their sibling cells in a CyTOF dataset achieving 93% and 84% cell-matching accuracy for each one of the samples respectively. Availability https://github.com/ratschlab/scim, bioRxiv

Published
2020

16. Lower utilization of home blood pressure monitoring in younger, poorly educated hypertensive males – real-life data.

Author

Chudek, Anna, Owczarek, Aleksander Jerzy, Ficek, Joanna, Almgren-Rachtan, Agnieszka, and Chudek, Jerzy

Subjects
BLOOD pressure, HYPERTENSION, CORONARY disease, ALCOHOL drinking
Abstract

Background: Home blood pressure monitoring (HBPM) became a standard in the management of hypertension. However, there are few data concerning the utilisation of blood pressure (BP) monitors in daily clinical practice. Aim: The aim of this analysis was to show: (1) how frequently hypertensive patients are equipped with BP monitors, (2) how often they perform regular HBPM and running BP diaries, (3) what are the correlates of utilisation of BP monitors, in a large real-life cohort of hypertensives examined for the efficacy of antihypertensive therapy. Patients and methods: The survey was conducted by 570 physicians among 14,200 hypertensive patients, of whom 12,289 (6163 women; mean age 63 ± 12 years) declared use of antihypertensive medicines. Each patient was asked whether at home is having and using regularly or occasionally BP monitor and running BP diary. BP control was assessed based on the mean of two attended office BP measurements. Results: Among patient equipped with BP monitors (87.2%), 73.4% were conducting HBPM regularly, while 26.6% occasionally, and 66.9% were running BP diaries. Controlled BP was achieved by 34.5% (32.9% men and 36.1% women; p <.001), more frequently by equipped with BP monitors (34.9 vs 31.7%, p <.001). Female sex, education, professional activity, active lifestyle, older age, hypertensive polytherapy, longer than 5-year therapy for hypertension, and coexistence of diabetes were factors increasing, while alcohol consumption, visceral obesity and heart failure decreasing the probability of being equipped with BP monitor and running BP diary. Regular HBPM were more frequently among women, physically active, older, diabetics, viscerally obese and patients with coronary artery disease. Conclusions: (1) The majority of hypertensive Poles are already equipped with BP monitors, (2) three-fourth patients perform regular HBPM and two-third run BP diaries, (3) there is still a need to promote utilisation of BP monitors among younger, poorly educated hypertensive males. [ABSTRACT FROM AUTHOR]

Published
2020
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17. SP649PLASMA SCLEROSTIN LEVEL IS SUPPRESSED BY INFLAMMATION IN HAEMODIALYSIS PATIENTS

Author

Wyskida, Katarzyna, primary, Pietrzyk, Barbara, additional, Ficek, Joanna, additional, Wajda, Jarosław, additional, Klein, Dariusz, additional, Witkowicz, Joanna, additional, Ficek, Rafał, additional, Rotkegel, Sylwia, additional, Spiechowicz-Zatoń, Urszula, additional, Kocemba-Dyczek, Joanna, additional, Ciepał, Jarosław, additional, Więcek, Andrzej, additional, Olszanecka-Glinianowicz, Magdalena, additional, and Chudek, Jerzy, additional

Published
2018
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19. Additional file 5: of Molecular profile of dissociative drug ketamine in relation to its rapid antidepressant action

Author

Ficek, Joanna, Zygmunt, Magdalena, Piechota, Marcin, Dzesika Hoinkis, Parkitna, Jan Rodriguez, Przewlocki, Ryszard, and Korostynski, Michal

Subjects
nervous system
Abstract

The pattern of gene expression alterations induced by the selected NMDA receptor antagonists over the time-course (1â 8Â h after treatment) in the hippocampus and striatum. (PDF 155 kb)

Published
2016
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21. MP099N-TERMINAL PROHORMONE OF BRAIN NATRIURETIC PEPTIDE BUT NOT C-TERMINAL PRE-PRO VASOPRESSIN (COPEPTIN) LEVEL IS ASSOCIATED WITH THE RESPONSE TO ANTIHYPERTENSIVE THERAPY IN HEMODIALYSIS PATIENTS

Author

Wyskida, Katarzyna, primary, Ficek, Joanna, additional, Ficek, Rafał, additional, Adamska, Dagmara, additional, Jędrzejowska, Patrycja, additional, Wajda, Jarosław, additional, Klein, Dariusz, additional, Witkowicz, Joanna, additional, Rotkegel, Sylwia, additional, Spiechowicz-Zatoń, Urszula, additional, Kocemba-Dyczek, Joanna, additional, Ciepał, Jarosław, additional, Więcek, Andrzej, additional, Olszanecka-Glinianowicz, Magdalena, additional, and Chudek, Jerzy, additional

Published
2017
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23. N-Terminal Prohormone of Brain Natriuretic Peptide but not C-Terminal Pre-Pro Vasopressin (Copeptin) Level is Associated with the Response to Antihypertensive Therapy in Haemodialysis Patients

Author

Wyskida, Katarzyna, primary, Ficek, Joanna, additional, Ficek, Rafał, additional, Adamska, Dagmara, additional, Jędrzejowska, Patrycja, additional, Wajda, Jarosław, additional, Klein, Dariusz, additional, Witkowicz, Joanna, additional, Rotkegel, Sylwia, additional, Spiechowicz-Zatoń, Urszula, additional, Kocemba-Dyczek, Joanna, additional, Ciepał, Jarosław, additional, Więcek, Andrzej, additional, Olszanecka-Glinianowicz, Magdalena, additional, and Chudek, Jerzy, additional

Published
2017
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27. SP624RELATIONSHIP BEETWEN PLASMA LEVELS OF ZOLULIN, BACTERIAL LIPOPOLYSACCHARIDES AND D-LACTATE AND MARKERS OF INFLAMMATION IN HAEMODIALYSIS PATIENTS WITH CHRONIC KIDNEY DISEAS

Author

Ficek, Joanna, primary, Wyskida, Katarzyna, additional, Wajda, Jarosław, additional, Klein, Dariusz, additional, Witkowicz, Joanna, additional, Ficek, Rafał, additional, Rotkegel, Sylwia, additional, Spiechowicz, Urszula, additional, Kocemba Dyczek, Joanna, additional, Ciepał, Jarosław, additional, Olszanecka-Glinianowicz, Magdalena, additional, Więcek, Andrzej, additional, and Chudek, Jerzy, additional

Published
2016
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30. N-Terminal Prohormone of Brain Natriuretic Peptide but not C-Terminal Pre-Pro Vasopressin (Copeptin) Level is Associated with the Response to Antihypertensive Therapy in Haemodialysis Patients.

Author

Wyskida, Katarzyna, Ficek, Joanna, Ficek, Rafał, Adamska, Dagmara, Jędrzejowska, Patrycja, Wajda, Jarosław, Klein, Dariusz, Witkowicz, Joanna, Rotkegel, Sylwia, Spiechowicz-Zatoń, Urszula, Kocemba-Dyczek, Joanna, Ciepał, Jarosław, Więcek, Andrzej, Olszanecka-Glinianowicz, Magdalena, and Chudek, Jerzy

Subjects
*HEMODIALYSIS, *HEMODIALYSIS patients, *BRAIN natriuretic factor, *COPEPTINS, *ANTIHYPERTENSIVE agents, *THERAPEUTICS
Abstract

Background/Aims: Volume overload, frequently clinically asymptomatic is considered as a causative factor limiting the effectiveness of antihypertensive therapy in haemodialysis (HD) patients. Therefore, the aim of this study was to assess plasma levels of N-terminal fragment of the prohormone brain natriuretic peptide (NT-proBNP) and a C-terminal portion of the precursor of vasopressin (CT-proAVP, copeptin), surrogate markers of volume overload in HD patients in relation to the number of antihypertensive drugs used in the hypertension treatment. Methods: One hundred and fifty adult HD patients (92 males) were enrolled into this study. Clinical data concerning blood pressure (BP) measurements prior haemodialysis session and pharmacotherapy were collected from all patients. In addition to routine laboratory parameters, plasma levels of NT-proBNP and CT-proAVP were measured, and daily sodium and water consumption were estimated with a portion-size food frequency questionnaire. Results: Among 145 (96.7%) hypertensive HD patients, 131 were receiving antihypertensive medication. Despite antihypertensive therapy, 31.0% had inadequate BP control. Plasma concentration of NT-proBNP was associated with systolic (R=0.19; p=0.02) but not diastolic BP values and with the number of received antihypertensive drugs (R=0.21; p=0.01). The highest NT-proBNP values were observed in patients receiving 3 or more antihypertensive drugs. In contrast, no significant correlation was found between plasma CT-proAVP concentrations and BP values as well as and the number of antihypertensive drugs. Receiver operator curve analysis showed that NT-proBNP values over 13,184 pg/mL predicted the use of at least 3 antihypertensive drugs in maximal doses in the therapy of hypertension, similar analyses performed for CTproAVP showed much less specificity. Conclusions: 1. Increased levels of NT-proBNP seems to be a better biomarker of multidrug antihypertensive therapy requirement than CT-proAVP. 2. Whether estimation of NT-proBNP in these patients will be also better biomarker than copeptin in the prediction of cardiovascular complications related to hypertension needs further investigations. [ABSTRACT FROM AUTHOR]

Published
2018
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34. SCIM: universal single-cell matching with unpaired feature sets.

Author

Stark SG, Ficek J, Locatello F, Bonilla X, Chevrier S, Singer F, Rätsch G, and Lehmann KV

Subjects
Algorithms, Gene Expression Profiling, Humans, Sequence Analysis, RNA, Single-Cell Analysis, Software
Abstract

Motivation: Recent technological advances have led to an increase in the production and availability of single-cell data. The ability to integrate a set of multi-technology measurements would allow the identification of biologically or clinically meaningful observations through the unification of the perspectives afforded by each technology. In most cases, however, profiling technologies consume the used cells and thus pairwise correspondences between datasets are lost. Due to the sheer size single-cell datasets can acquire, scalable algorithms that are able to universally match single-cell measurements carried out in one cell to its corresponding sibling in another technology are needed., Results: We propose Single-Cell data Integration via Matching (SCIM), a scalable approach to recover such correspondences in two or more technologies. SCIM assumes that cells share a common (low-dimensional) underlying structure and that the underlying cell distribution is approximately constant across technologies. It constructs a technology-invariant latent space using an autoencoder framework with an adversarial objective. Multi-modal datasets are integrated by pairing cells across technologies using a bipartite matching scheme that operates on the low-dimensional latent representations. We evaluate SCIM on a simulated cellular branching process and show that the cell-to-cell matches derived by SCIM reflect the same pseudotime on the simulated dataset. Moreover, we apply our method to two real-world scenarios, a melanoma tumor sample and a human bone marrow sample, where we pair cells from a scRNA dataset to their sibling cells in a CyTOF dataset achieving 90% and 78% cell-matching accuracy for each one of the samples, respectively., Availability and Implementation: https://github.com/ratschlab/scim., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2020. Published by Oxford University Press.)

Published
2020
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35. [Renalase and its role in the development of hypertension in patients with chronic renal failure].

Author

Ficek J, Małyszko J, and Chudek J

Subjects
Blood Pressure physiology, Catecholamines blood, Catecholamines metabolism, Gene Expression, Humans, Hypertension physiopathology, Monoamine Oxidase genetics, Monoamine Oxidase metabolism, Hypertension enzymology, Hypertension etiology, Monoamine Oxidase physiology, Renal Insufficiency, Chronic complications
Abstract

Renalase is a newly-discovered enzyme--amine oxidase containing flavin adenine dinucleotide that determines its activity. Kidneys are the main source of renalase, however, the enzyme has also been detected in the myocardium, skeletal muscles, small intestine, peripheral nerves, adrenal cortex and adipose tissue. This enzyme metabolizes circulating catecholamines, particularly epinephrine and L-3,4-dihydroxyphenylalanine (a dopamine precursor). For this reason, it is considered that renalase may play a significant role in the regulation of blood pressure. There are some factors enhancing the release of renalase: rising catecholamines levels in the circulation and increase in blood pressure. Experimental and clinical studies revealed renalase deficiency in chronic kidney disease (CKD) and hypertension. In contrast, the results of assays based on currently available ELISA kits demonstrate an increase in renalase concentration in patients with CKD. On the basis of currently available studies it is difficult to determine how important are changes in the expression and secretion of renalase in the pathogenesis of hypertension in CKD patients. Stimulation of catecholamines degradation, perhaps using recombinant renalase or its analogues, is a new concept in the treatment of hypertension in CKD.

Published
2015

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