32 results on '"Krauthammer, Michael"'
Search Results
2. Live slow-frozen human tumor tissues viable for 2D, 3D, ex vivo cultures and single-cell RNAseq
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Restivo, Gaetana, Tastanova, Aizhan, Balázs, Zsolt, Panebianco, Federica, Diepenbruck, Maren, Ercan, Caner, Preca, Bodgan-T., Hafner, Jürg, Weber, Walter P., Kurzeder, Christian, Vetter, Marcus, Soysal, Simone Münst, Beisel, Christian, Bentires-Alj, Mohamed, Piscuoglio, Salvatore, Krauthammer, Michael, and Levesque, Mitchell P.
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- 2022
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3. Explainable deep learning for disease activity prediction in chronic inflammatory joint diseases.
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Trottet, Cécile, Allam, Ahmed, Horvath, Aron N., Finckh, Axel, Hügle, Thomas, Adler, Sabine, Kyburz, Diego, Micheroli, Raphael, Krauthammer, Michael, and Ospelt, Caroline
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- 2024
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4. Innate acting memory Th1 cells modulate heterologous diseases.
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Rakebrandt, Nikolas, Yassini, Nima, Kolz, Anna, Schorer, Michelle, Lambert, Katharina, Goljat, Eva, Brull, Anna Estrada, Rauld, Celine, Balazs, Zsolt, Krauthammer, Michael, Carballido, José M., Peters, Anneli, and Joller, Nicole
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TH1 cells ,LEGIONELLA pneumophila ,IMMUNOLOGIC memory ,T helper cells ,DISEASE susceptibility - Abstract
Through immune memory, infections have a lasting effect on the host. While memory cells enable accelerated and enhanced responses upon rechallenge with the same pathogen, their impact on susceptibility to unrelated diseases is unclear. We identify a subset of memory T helper 1 (Th1) cells termed innate acting memory T (TIA) cells that originate from a viral infection and produce IFN-γ with innate kinetics upon heterologous challenge in vivo. Activation of memory TIA cells is induced in response to IL-12 in combination with IL-18 or IL-33 but is TCR independent. Rapid IFN-γ production by memory TIA cells is protective in subsequent heterologous challenge with the bacterial pathogen Legionella pneumophila. In contrast, antigen-independent reactivation of CD4+ memory TIA cells accelerates disease onset in an autoimmune model of multiple sclerosis. Our findings demonstrate that memory Th1 cells can acquire additional TCR-independent functionality to mount rapid, innate-like responses that modulate susceptibility to heterologous challenges. [ABSTRACT FROM AUTHOR]
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- 2024
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5. Former smoking, but not active smoking, is associated with delirium in postoperative ICU patients: a matched case-control study.
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Komninou, Maria Angeliki, Egli, Simon, Rossi, Aurelio, Ernst, Jutta, Krauthammer, Michael, Schuepbach, Reto A., Delgado, Marcos, and Bartussek, Jan
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NICOTINE replacement therapy ,DELIRIUM ,SURGICAL intensive care ,CASE-control method ,SMOKING ,LOGISTIC regression analysis ,INTENSIVE care units ,INHALATION injuries - Abstract
Objective: To examine the relationship between current and former smoking and the occurrence of delirium in surgical Intensive Care Unit (ICU) patients. Methods: We conducted a single center, case-control study involving 244 delirious and 251 non-delirious patients that were admitted to our ICU between 2018 and 2022. Using propensity score analysis, we obtained 115 pairs of delirious and non-delirious patients matched for age and Simplified Acute Physiology Score II (SAPS II). Both groups of patients were further stratified into non-smokers, active smokers and former smokers, and logistic regression was performed to further investigate potential confounders. Results: Our study revealed a significant association between former smoking and the incidence of delirium in ICU patients, both in unmatched (adjusted odds ratio (OR): 1.82, 95% confidence interval (CI): 1.17-2.83) and matched cohorts (OR: 3.0, CI: 1.53-5.89). Active smoking did not demonstrate a significant difference in delirium incidence compared to non-smokers (unmatched OR = 0.98, CI: 0.62-1.53, matched OR = 1.05, CI: 0.55-2.0). Logistic regression analysis of the matched group confirmed former smoking as an independent risk factor for delirium, irrespective of other variables like surgical history (p = 0.010). Notably, also respiratory and vascular surgeries were associated with increased odds of delirium (respiratory: OR: 4.13, CI: 1.73-9.83; vascular: OR: 2.18, CI: 1.03-4.59). Medication analysis showed that while Ketamine and Midazolam usage did not significantly correlate with delirium, Morphine use was linked to a decreased likelihood (OR: 0.27, 95% CI: 0.13-0.55). Discussion: Nicotine's complex neuropharmacological impact on the brain is still not fully understood, especially its short-term and long-term implications for critically ill patients. Although our retrospective study cannot establish causality, our findings suggest that smoking may induce structural changes in the brain, potentially heightening the risk of postoperative delirium. Intriguingly, this effect seems to be obscured in active smokers, potentially due to the recognized neuroprotective properties of nicotine. Our results motivate future prospective studies, the results of which hold the potential to substantially impact risk assessment procedures for surgeries. [ABSTRACT FROM AUTHOR]
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- 2024
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6. Predicting base editing outcomes with an attention-based deep learning algorithm trained on high-throughput target library screens
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Marquart, Kim F., Allam, Ahmed, Janjuha, Sharan, Sintsova, Anna, Villiger, Lukas, Frey, Nina, Krauthammer, Michael, and Schwank, Gerald
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- 2021
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7. ROS Induction Targets Persister Cancer Cells with Low Metabolic Activity in NRAS-Mutated Melanoma
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Eichhoff, Ossia M, Stoffel, Corinne I, Käsler, Jan, Briker, Luzia, Turko, Patrick, Karsai, Gergely, Zila, Nina, Paulitschke, Verena, Cheng, Phil F, Leitner, Alexander, Bileck, Andrea, Zamboni, Nicola, Irmisch, Anja, Balazs, Zsolt, Tastanova, Aizhan, Pascoal, Susana, Johansen, Pål, Wegmann, Rebekka, Mena, Julien, Othman, Alaa, Viswanathan, Vasanthi S, Wenzina, Judith, Aloia, Andrea, Saltari, Annalisa, Dzung, Andreas, Krauthammer, Michael, Schreiber, Stuart L, Hornemann, Thorsten, Distel, Martin, Snijder, Berend, Dummer, Reinhard, Levesque, Mitchell Paul, and University of Zurich
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Cancer Research ,Oncology ,10177 Dermatology Clinic ,610 Medicine & health ,2730 Oncology ,1306 Cancer Research - Published
- 2023
8. Exploratory Analysis of Federated Learning Methods with Differential Privacy on MIMIC-III
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Horvath, Aron N., Berchier, Matteo, Nooralahzadeh, Farhad, Allam, Ahmed, and Krauthammer, Michael
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FOS: Computer and information sciences ,Computer Science - Machine Learning ,Statistics - Machine Learning ,Machine Learning (stat.ML) ,Machine Learning (cs.LG) - Abstract
Background: Federated learning methods offer the possibility of training machine learning models on privacy-sensitive data sets, which cannot be easily shared. Multiple regulations pose strict requirements on the storage and usage of healthcare data, leading to data being in silos (i.e. locked-in at healthcare facilities). The application of federated algorithms on these datasets could accelerate disease diagnostic, drug development, as well as improve patient care. Methods: We present an extensive evaluation of the impact of different federation and differential privacy techniques when training models on the open-source MIMIC-III dataset. We analyze a set of parameters influencing a federated model performance, namely data distribution (homogeneous and heterogeneous), communication strategies (communication rounds vs. local training epochs), federation strategies (FedAvg vs. FedProx). Furthermore, we assess and compare two differential privacy (DP) techniques during model training: a stochastic gradient descent-based differential privacy algorithm (DP-SGD), and a sparse vector differential privacy technique (DP-SVT). Results: Our experiments show that extreme data distributions across sites (imbalance either in the number of patients or the positive label ratios between sites) lead to a deterioration of model performance when trained using the FedAvg strategy. This issue is resolved when using FedProx with the use of appropriate hyperparameter tuning. Furthermore, the results show that both differential privacy techniques can reach model performances similar to those of models trained without DP, however at the expense of a large quantifiable privacy leakage. Conclusions: We evaluate empirically the benefits of two federation strategies and propose optimal strategies for the choice of parameters when using differential privacy techniques.
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- 2023
9. Fragmentstein—facilitating data reuse for cell-free DNA fragment analysis.
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Balázs, Zsolt, Gitchev, Todor, Ivanković, Ivna, and Krauthammer, Michael
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DNA analysis ,CELL-free DNA ,DNA copy number variations ,BIOINFORMATICS software ,INFORMATION sharing ,CHROMATIN - Abstract
Summary Method development for the analysis of cell-free DNA (cfDNA) sequencing data is impeded by limited data sharing due to the strict control of sensitive genomic data. An existing solution for facilitating data sharing removes nucleotide-level information from raw cfDNA sequencing data, keeping alignment coordinates only. This simplified format can be publicly shared and would, theoretically, suffice for common functional analyses of cfDNA data. However, current bioinformatics software requires nucleotide-level information and cannot process the simplified format. We present Fragmentstein, a command-line tool for converting non-sensitive cfDNA-fragmentation data into alignment mapping (BAM) files. Fragmentstein complements fragment coordinates with sequence information from a reference genome to reconstruct BAM files. We demonstrate the utility of Fragmentstein by showing the feasibility of copy number variant (CNV), nucleosome occupancy, and fragment length analyses from non-sensitive fragmentation data. Availability and implementation Implemented in bash, Fragmentstein is available at https://github.com/uzh-dqbm-cmi/fragmentstein , licensed under GNU GPLv3. [ABSTRACT FROM AUTHOR]
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- 2024
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10. Vision transformer assisting rheumatologists in screening for capillaroscopy changes in systemic sclerosis: an artificial intelligence model.
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Garaiman, Alexandru, Nooralahzadeh, Farhad, Mihai, Carina, Gonzalez, Nicolas Perez, Gkikopoulos, Nikitas, Becker, Mike Oliver, Distler, Oliver, Krauthammer, Michael, and Maurer, Britta
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DIGITAL image processing ,DEEP learning ,PREDICTIVE tests ,NAILS (Anatomy) ,DERMATOMYOSITIS ,CAPILLARIES ,ANGIOSCOPY ,SYSTEMIC scleroderma ,ARTIFICIAL intelligence ,RHEUMATOLOGISTS ,DESCRIPTIVE statistics ,RECEIVER operating characteristic curves ,SENSITIVITY & specificity (Statistics) ,LONGITUDINAL method - Abstract
Objectives The first objective of this study was to implement and assess the performance and reliability of a vision transformer (ViT)-based deep-learning model, an 'off-the-shelf' artificial intelligence solution, for identifying distinct signs of microangiopathy in nailfold capilloroscopy (NFC) images of patients with SSc. The second objective was to compare the ViT's analysis performance with that of practising rheumatologists. Methods NFC images of patients prospectively enrolled in our European Scleroderma Trials and Research group (EUSTAR) and Very Early Diagnosis of Systemic Sclerosis (VEDOSS) local registries were used. The primary outcome investigated was the ViT's classification performance for identifying disease-associated changes (enlarged capillaries, giant capillaries, capillary loss, microhaemorrhages) and the presence of the scleroderma pattern in these images using a cross-fold validation setting. The secondary outcome involved a comparison of the ViT's performance vs that of rheumatologists on a reliability set, consisting of a subset of 464 NFC images with majority vote–derived ground-truth labels. Results We analysed 17 126 NFC images derived from 234 EUSTAR and 55 VEDOSS patients. The ViT had good performance in identifying the various microangiopathic changes in capillaries by NFC [area under the curve (AUC) from 81.8% to 84.5%]. In the reliability set, the rheumatologists reached a higher average accuracy, as well as a better trade-off between sensitivity and specificity compared with the ViT. However, the annotators' performance was variable, and one out of four rheumatologists showed equal or lower classification measures compared with the ViT. Conclusions The ViT is a modern, well-performing and readily available tool for assessing patterns of microangiopathy on NFC images, and it may assist rheumatologists in generating consistent and high-quality NFC reports; however, the final diagnosis of a scleroderma pattern in any individual case needs the judgement of an experienced observer. [ABSTRACT FROM AUTHOR]
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- 2023
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11. ROS induction as a strategy to target persister cancer cells with low metabolic activity in NRAS mutated melanoma
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Eichhoff, Ossia M, Stoffel, Corinne Isabelle, Käsler, Jan, Briker, Luzia, Turko, Patrick, Karsai, Gergely, Zila, Nina, Paulitschke, Verena, Cheng, Phil F, Leitner, Alexander, Bileck, Andrea, Zamboni, Nicola, Irmisch, Anja, Balázs, Zsolt, Tastanova, Aizhan, Pascoal, Susana, Johansen, Pål, Wegmann, Rebekka, Mena, Julien, Othman, Alaa, Viswanathan, Vasanthi S, Wenzina, Judith, Aloia, Andrea, Saltari, Annalisa, Dzung, Andreas, Krauthammer, Michael, Schreiber, Stuart L, Hornemann, Thorsten, Distel, Martin, Snijder, Berend, Dummer, R, Levesque, Mitchell, and University of Zurich
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610 Medicine & health ,11493 Department of Quantitative Biomedicine - Published
- 2022
12. DDoS: A Graph Neural Network based Drug Synergy Prediction Algorithm
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Schwarz, Kyriakos, Pliego-Mendieta, Alicia, Planas-Plaz, Lara, Pauli, Chantal, Allam, Ahmed, Krauthammer, Michael, and University of Zurich
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FOS: Computer and information sciences ,Computer Science - Machine Learning ,Quantitative Biology - Biomolecules ,FOS: Biological sciences ,Biomolecules (q-bio.BM) ,610 Medicine & health ,11493 Department of Quantitative Biomedicine ,Quantitative Biology - Quantitative Methods ,Quantitative Methods (q-bio.QM) ,Machine Learning (cs.LG) - Abstract
Background: Drug synergy occurs when the combined effect of two drugs is greater than the sum of the individual drugs' effect. While cell line data measuring the effect of single drugs are readily available, there is relatively less comparable data on drug synergy given the vast amount of possible drug combinations. Thus, there is interest to use computational approaches to predict drug synergy for untested pairs of drugs. Methods: We introduce a Graph Neural Network (GNN) based model for drug synergy prediction, which utilizes drug chemical structures and cell line gene expression data. We use information from the largest drug combination database available (DrugComb), combining drug synergy scores in order to construct high confidence benchmark datasets. Results: Our proposed solution for drug synergy predictions offers a number of benefits: 1) It utilizes a combination of 34 distinct drug synergy datasets to learn on a wide variety of drugs and cell lines representations. 2) It is trained on constructed high confidence benchmark datasets. 3) It learns task-specific drug representations, instead of relying on generalized and pre-computed chemical drug features. 4) It achieves similar or better prediction performance (AUPR scores ranging from 0.777 to 0.964) compared to state-of-the-art baseline models when tested on various benchmark datasets. Conclusions: We demonstrate that a GNN based model can provide state-of-the-art drug synergy predictions by learning task-specific representations of drugs.
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- 2022
13. A versatile computational pipeline for the preprocessing of cell-free DNA fragmentation data
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Ivankovic, Ivana, Balázs, Zsolt, Gitchev, Todor, Trejo Banos, Daniel, Moldovan, Norbert, Panagiotis, Balermpas, Willmann, Jonas, Andratschke, N, Moulière, Florent, Krauthammer, Michael, and University of Zurich
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610 Medicine & health ,11493 Department of Quantitative Biomedicine - Published
- 2022
14. Abstract 1912: A versatile computational pipeline for the preprocessing of cell-free DNA fragmentation data
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Ivankovic, Ivna, Balázs, Zsolt, Gitchev, Todor, Banos, Daniel Trejo, Moldovan, Norbert, Balermpas, Panagiotis, Willmann, Jonas, Andratschke, Nicolaus, Moulière, Florent, Krauthammer, Michael, and University of Zurich
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Cancer Research ,Oncology ,610 Medicine & health ,11493 Department of Quantitative Biomedicine - Abstract
Cell-free DNA (cfDNA) emerges as a promising liquid biopsy biomarker for cancer diagnosis and patient monitoring. Complementing mutation-based assays, cfDNA carries information about epigenetic modifications from decaying cells. This information is encoded in the shape of the cfDNA fragments. Specifically, fragments from cancer tend to be shorter than those originating from other adult cells, enabling a distinction between cancer patients and healthy individuals. Additional cfDNA features such as fragment end motifs and information on nucleosome positioning provide further insight into cancer biology. These cfDNA measures are typically inferred from low-pass whole genome sequencing and subsequent bioinformatics processing. A key bioinformatics step is the alignment of DNA sequencing reads to the reference genome, which critically depends on preprocessing steps such as read trimming and alignment filters. A good understanding of preprocessing settings is thus crucial to derive accurate information from cfDNA. We therefore investigated to what extent preprocessing choices affect cfDNA analysis. To this end, we have built a robust bioinformatics pipeline that evaluates a range of possible preprocessing settings. The pipeline is implemented in a bioinformatics workflow engine, enabling a scalable and reproducible workflow. We are currently evaluating the effect of preprocessing on global and regional fragmentation patterns, detection of nucleosome positioning and identification of fragment end motifs. The different preprocessing settings are also evaluated for their ability to distinguish cancer from control samples using cfDNA fragmentation information. We are benchmarking the pipeline using cfDNA datasets from multiple centres to ensure our findings are generalizable over different platforms and experimental procedures. Our investigations will allow us to build a versatile bioinformatics preprocessing pipeline for the analysis of cell-free DNA fragmentation data. Citation Format: Ivna Ivankovic, Zsolt Balázs, Todor Gitchev, Daniel Trejo Banos, Norbert Moldovan, Panagiotis Balermpas, Jonas Willmann, Nicolaus Andratschke, Florent Moulière, Michael Krauthammer. A versatile computational pipeline for the preprocessing of cell-free DNA fragmentation data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1912.
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- 2022
15. An empirical study of the effect of background data size on the stability of SHapley Additive exPlanations (SHAP) for deep learning models
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Yuan, Han, Liu, Minxuan, Kang, Lican, Chenkui, Miao, Wu, Ying, et al, Krauthammer, Michael, and University of Zurich
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610 Medicine & health ,11493 Department of Quantitative Biomedicine - Published
- 2022
16. How to Synchronize Longitudinal Patient Data With the Underlying Disease Progression: A Pilot Study Using the Biomarker CRP for Timing COVID-19
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Maibach, Martina A, Allam, Ahmed, Hilty, Matthias P, Perez Gonzalez, Nicolas A, Buehler, Philipp K, Wendel Garcia, Pedro D, Brugger, Silvio D, Ganter, Christoph C, CoViD-19 ICU-Research Group Zurich, RISC-19-ICU Investigators, Krauthammer, Michael, Schuepbach, Reto A, Bartussek, Jan, University of Zurich, Krauthammer, Michael, Schuepbach, Reto A, and Bartussek, Jan
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10234 Clinic for Infectious Diseases ,610 Medicine & health ,2700 General Medicine ,10023 Institute of Intensive Care Medicine - Published
- 2021
17. Leveraging Token-Based Concept Information and Data Augmentation in Few-Resource NER: ZuKyo-EN at the NTCIR-16 Real-MedNLP task
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Cornelius, Joseph, Lithgow-Serrano, Oscar, Kanjirangat, Vani, Rinaldi, Fabio, Fujimoto, Koji, Nishio, Mizuho, Sugiyama, Osamu, Ichikawa, Kana, Nooralahzadeh, Farhad, Horvath, Aron N, Krauthammer, Michael, and University of Zurich
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610 Medicine & health ,11493 Department of Quantitative Biomedicine - Published
- 2022
18. Approach for Named Entity Recognition and Case Identification Implemented by ZuKyo-JA Sub-team at the NTCIR-16 Real-MedNLP Task
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Fujimoto, Koji, Nishio, Mizuho, Sugiyama, Osamu, Ichikawa, Kana, Cornelius, Joseph, Lithgow-Serrano, Oscar, Kanjirangat, Vani, Rinaldi, Fabio, Horvath, Aron N, Nooralahzadeh, Farhad, Krauthammer, Michael, and University of Zurich
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610 Medicine & health ,11493 Department of Quantitative Biomedicine - Published
- 2022
19. Neue Lernziele für das Medizinstudium erarbeitet
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Bonvin, Raphaël, Buhmann, Joachim, Cotrini Jimenez, Carlos, Egger, Marcel, Geissler, Alexander, Krauthammer, Michael, Schirlo, Christian, Spiess, Christiane, Steurer, J, Vokinger, Kerstin N, Vogt, Julia, and University of Zurich
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610 Medicine & health ,11493 Department of Quantitative Biomedicine - Published
- 2022
20. Analyzing Patient Trajectories With Artificial Intelligence
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Allam, Ahmed, Feuerriegel, Stefan, Rebhan, Michael, Krauthammer, Michael, University of Zurich, and Feuerriegel, Stefan
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longitudinal data ,Viewpoint ,digital medicine ,machine learning ,Humans ,610 Medicine & health ,Health Informatics ,artificial intelligence ,11493 Department of Quantitative Biomedicine ,patient trajectories ,2718 Health Informatics - Abstract
In digital medicine, patient data typically record health events over time (eg, through electronic health records, wearables, or other sensing technologies) and thus form unique patient trajectories. Patient trajectories are highly predictive of the future course of diseases and therefore facilitate effective care. However, digital medicine often uses only limited patient data, consisting of health events from only a single or small number of time points while ignoring additional information encoded in patient trajectories. To analyze such rich longitudinal data, new artificial intelligence (AI) solutions are needed. In this paper, we provide an overview of the recent efforts to develop trajectory-aware AI solutions and provide suggestions for future directions. Specifically, we examine the implications for developing disease models from patient trajectories along the typical workflow in AI: problem definition, data processing, modeling, evaluation, and interpretation. We conclude with a discussion of how such AI solutions will allow the field to build robust models for personalized risk scoring, subtyping, and disease pathway discovery., Journal of Medical Internet Research, 23 (12), ISSN:1438-8871
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- 2021
21. Positionspapier: Ein Rechtsrahmen für Künstliche Intelligenz
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Thouvenin, Florent, Christen, Markus, Bernstein, Abraham, Braun Binder, Nadja, Burri, Thomas, Donnay, Karsten, Jäger, Lena A, Jaffé, Mariela, Krauthammer, Michael, Lohmann, Melinda, Mätzener, Anna, Mützel, Sophie, Obrecht, Liliane, Ritter, Nicole, Spielkamp, Matthias, Volz, Stephanie, and University of Zurich
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10105 Institute of Computational Linguistics ,11476 Digital Society Initiative ,10222 Institute of Biomedical Ethics and History of Medicine ,410 Linguistics ,000 Computer science, knowledge & systems - Published
- 2021
22. Exploring the Latest Highlights in Medical Natural Language Processing across Multiple Languages: A Survey.
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Shaitarova, Anastassia, Zaghir, Jamil, Lavelli, Alberto, Krauthammer, Michael, and Rinaldi, Fabio
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- 2023
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23. The Use and Ethics of Digital Twins in Medicine.
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Iqbal, Jeffrey David, Krauthammer, Michael, and Biller-Andorno, Nikola
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COMPUTER simulation , *MEDICINE , *DIGITAL technology , *DIGITAL health , *MACHINE learning , *INDIVIDUALIZED medicine , *MEDICAL care , *BIOETHICS - Abstract
Digital Health Technologies (DHTs) are currently the subject of much debate both in terms of their technological frontiers as well as their ethical, legal and societal implications (ELSI). Regulation of such technologies as medical devices currently lacks behind their level of adoption. Digital Twins are the next evolution step of such DHTs and provide an opportunity to anticipate and act on ELSI before adoption again leaps before the necessary review. This paper introduces the concept and use cases of digital twins in medicine, then frames the debate through the lens of related technologies, machine learning and personalized medicine, and maps ethical challenges stemming from those. Finally, we lay out how digital twins may change and challenge the future practice of medicine. [ABSTRACT FROM AUTHOR]
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- 2022
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24. AttentionDDI: Siamese attention-based deep learning method for drug–drug interaction predictions.
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Schwarz, Kyriakos, Allam, Ahmed, Perez Gonzalez, Nicolas Andres, and Krauthammer, Michael
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DRUG interactions ,DEEP learning ,ARTIFICIAL neural networks ,NATURAL language processing ,DRUG target ,FEATURE selection - Abstract
Background: Drug–drug interactions (DDIs) refer to processes triggered by the administration of two or more drugs leading to side effects beyond those observed when drugs are administered by themselves. Due to the massive number of possible drug pairs, it is nearly impossible to experimentally test all combinations and discover previously unobserved side effects. Therefore, machine learning based methods are being used to address this issue. Methods: We propose a Siamese self-attention multi-modal neural network for DDI prediction that integrates multiple drug similarity measures that have been derived from a comparison of drug characteristics including drug targets, pathways and gene expression profiles. Results: Our proposed DDI prediction model provides multiple advantages: (1) It is trained end-to-end, overcoming limitations of models composed of multiple separate steps, (2) it offers model explainability via an Attention mechanism for identifying salient input features and (3) it achieves similar or better prediction performance (AUPR scores ranging from 0.77 to 0.92) compared to state-of-the-art DDI models when tested on various benchmark datasets. Novel DDI predictions are further validated using independent data resources. Conclusions: We find that a Siamese multi-modal neural network is able to accurately predict DDIs and that an Attention mechanism, typically used in the Natural Language Processing domain, can be beneficially applied to aid in DDI model explainability. [ABSTRACT FROM AUTHOR]
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- 2021
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25. Commensal Clostridiales strains mediate effective anti-cancer immune response against solid tumors.
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Montalban-Arques, Ana, Katkeviciute, Egle, Busenhart, Philipp, Bircher, Anna, Wirbel, Jakob, Zeller, Georg, Morsy, Yasser, Borsig, Lubor, Glaus Garzon, Jesus F., Müller, Anne, Arnold, Isabelle C., Artola-Boran, Mariela, Krauthammer, Michael, Sintsova, Anna, Zamboni, Nicola, Leventhal, Gabriel E., Berchtold, Laura, de Wouters, Tomas, Rogler, Gerhard, and Baebler, Katharina
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Despite overall success, T cell checkpoint inhibitors for cancer treatment are still only efficient in a minority of patients. Recently, intestinal microbiota was found to critically modulate anti-cancer immunity and therapy response. Here, we identify Clostridiales members of the gut microbiota associated with a lower tumor burden in mouse models of colorectal cancer (CRC). Interestingly, these commensal species are also significantly reduced in CRC patients compared with healthy controls. Oral application of a mix of four Clostridiales strains (CC4) in mice prevented and even successfully treated CRC as stand-alone therapy. This effect depended on intratumoral infiltration and activation of CD8
+ T cells. Single application of Roseburia intestinalis or Anaerostipes caccae was even more effective than CC4. In a direct comparison, the CC4 mix supplementation outperformed anti-PD-1 therapy in mouse models of CRC and melanoma. Our findings provide a strong preclinical foundation for exploring gut bacteria as novel stand-alone therapy against solid tumors. [Display omitted] • Clostridiales bacteria are associated with low tumor burden in colon cancer models • Selected Clostridiales bacteria are diminished in colorectal cancer patients • A mix of Clostridiales strains have a potent anti-tumor effect via CD8+ T cells • Clostridiales treatment is effective in solid tumors independently of anti-PD-1 Montalban-Arques et al. report that Clostridiales bacteria strains that are significantly reduced in colorectal cancer patients compared with healthy individuals are effective in driving a potent anti-tumor response in solid tumors. They demonstrate that this is via activation of CD8+ T cells, independently of anti-PD-1 immunotherapy. [ABSTRACT FROM AUTHOR]- Published
- 2021
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26. Predicting base editing outcomes with an attention-based deep learning algorithm trained on high-throughput target library screens
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Gerald Schwank, Ahmed A. Allam, Nina Frey, Sharan Janjuha, Michael Krauthammer, Kim Marquart, Anna Sintsova, Lukas Villiger, University of Zurich, Krauthammer, Michael, and Schwank, Gerald
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CRISPR-Cas9 genome editing ,Computer science ,Base pair ,Science ,10050 Institute of Pharmacology and Toxicology ,General Physics and Astronomy ,610 Medicine & health ,1600 General Chemistry ,Genomics ,Article ,General Biochemistry, Genetics and Molecular Biology ,Deep Learning ,Genome editing ,1300 General Biochemistry, Genetics and Molecular Biology ,Basic research ,Machine learning ,Humans ,Data mining ,Base Pairing ,Throughput (business) ,Gene Library ,Gene Editing ,Genome ,Multidisciplinary ,business.industry ,Deep learning ,High-Throughput Nucleotide Sequencing ,General Chemistry ,Base (topology) ,3100 General Physics and Astronomy ,genomic DNA ,570 Life sciences ,biology ,Artificial intelligence ,business ,11493 Department of Quantitative Biomedicine ,Algorithm ,Algorithms - Abstract
Base editors are chimeric ribonucleoprotein complexes consisting of a DNA-targeting CRISPR-Cas module and a single-stranded DNA deaminase. They enable transition of C•G into T•A base pairs and vice versa on genomic DNA. While base editors have great potential as genome editing tools for basic research and gene therapy, their application has been hampered by a broad variation in editing efficiencies on different genomic loci. Here we perform an extensive analysis of adenine- and cytosine base editors on a library of 28,294 lentivirally integrated genetic sequences and establish BE-DICT, an attention-based deep learning algorithm capable of predicting base editing outcomes with high accuracy. BE-DICT is a versatile tool that in principle can be trained on any novel base editor variant, facilitating the application of base editing for research and therapy., Nature Communications, 12 (1), ISSN:2041-1723
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- 2021
27. COL10A1 expression distinguishes a subset of cancer-associated fibroblasts present in the stroma of high-risk basal cell carcinoma.
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Esposito M, Yerly L, Shukla P, Hermes V, Sella F, Balazs Z, Lattmann E, Tastanova A, Turko P, Lang R, Kolm I, Staeger R, Kuonen F, Krauthammer M, Hafner J, Levesque MP, and Restivo G
- Abstract
Background: Basal cell carcinoma (BCC) is the most frequently diagnosed skin cancer and the most common malignancy in humans. Different morphological subtypes of BCC are associated with low- or high-risk of recurrence and aggressiveness, but the underlying biology of how the individual subtypes arise remains largely unknown. Because the majority of BCCs appear to arise from mutations in the same pathway, we hypothesized that BCC development, growth and invasive potential is also influenced by the tumor microenvironment and in particular by cancer-associated fibroblasts (CAFs) and their secreted factors., Objective: We aimed to characterize the stroma of the different BCC subtypes with a focus on CAF populations., Methods: To investigate the stromal features of the different BCC subtypes, we applied laser-capture microdissection (LCM) followed by RNA sequencing. A cohort of 15 BCC samples from 5 different "pure" subtypes (superficial, nodular, micronodular, sclerosing and basosquamous; n=3 each) were selected and included in the analysis. Healthy skin was used as a control (n=6). We confirmed the results by immunohistochemistry. We validated our findings in two independent, public single-cell RNA sequencing (scRNAseq) datasets and by RNAscope., Results: The stroma of the different BCC subtypes have distinct gene expression signatures. Nodular and micronodular seem to have the most similar signatures, while superficial and sclerosing the most different. By comparing low- and high-risk BCC subtypes, we observed that Collagen 10A1 (COL10A1) is overexpressed in the stroma of sclerosing/infiltrative and basosquamous but not micronodular high-risk subtypes. Those findings were confirmed by immunohistochemistry in a cohort of 89 different BCC and 13 healthy skin samples. Moreover, scRNAseq analysis of BCCs of two independent datasets showed that the COL10A1-expressing population of cells is associated with the stroma adjacent to invasive BCC and shows extracellular matrix remodeling features., Conclusion: We identified COL10A1 as a marker of high-risk BCC, in particular of the sclerosing/infiltrative and basosquamous subtypes. We demonstrated at the single cell level that COL10A1 is expressed by a specific CAF population associated with the stroma of invasive BCC. This opens up new tailored treatment options as well as a new prognostic biomarker for BCC progression., (© The Author(s) 2024. Published by Oxford University Press on behalf of British Association of Dermatologists.)
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- 2024
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28. Machine learning prediction of prime editing efficiency across diverse chromatin contexts.
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Mathis N, Allam A, Tálas A, Kissling L, Benvenuto E, Schmidheini L, Schep R, Damodharan T, Balázs Z, Janjuha S, Ioannidi EI, Böck D, van Steensel B, Krauthammer M, and Schwank G
- Abstract
The success of prime editing depends on the prime editing guide RNA (pegRNA) design and target locus. Here, we developed machine learning models that reliably predict prime editing efficiency. PRIDICT2.0 assesses the performance of pegRNAs for all edit types up to 15 bp in length in mismatch repair-deficient and mismatch repair-proficient cell lines and in vivo in primary cells. With ePRIDICT, we further developed a model that quantifies how local chromatin environments impact prime editing rates., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)
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- 2024
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29. ROS Induction Targets Persister Cancer Cells with Low Metabolic Activity in NRAS-Mutated Melanoma.
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Eichhoff OM, Stoffel CI, Käsler J, Briker L, Turko P, Karsai G, Zila N, Paulitschke V, Cheng PF, Leitner A, Bileck A, Zamboni N, Irmisch A, Balazs Z, Tastanova A, Pascoal S, Johansen P, Wegmann R, Mena J, Othman A, Viswanathan VS, Wenzina J, Aloia A, Saltari A, Dzung A, Krauthammer M, Schreiber SL, Hornemann T, Distel M, Snijder B, Dummer R, and Levesque MP
- Subjects
- Humans, Reactive Oxygen Species, Proto-Oncogene Proteins B-raf genetics, Protein Kinase Inhibitors therapeutic use, Mitogen-Activated Protein Kinase Kinases genetics, Cell Line, Tumor, Mutation, Membrane Proteins genetics, GTP Phosphohydrolases genetics, Melanoma drug therapy, Melanoma genetics, Melanoma pathology, Skin Neoplasms drug therapy
- Abstract
Clinical management of melanomas with NRAS mutations is challenging. Targeting MAPK signaling is only beneficial to a small subset of patients due to resistance that arises through genetic, transcriptional, and metabolic adaptation. Identification of targetable vulnerabilities in NRAS-mutated melanoma could help improve patient treatment. Here, we used multiomics analyses to reveal that NRAS-mutated melanoma cells adopt a mesenchymal phenotype with a quiescent metabolic program to resist cellular stress induced by MEK inhibition. The metabolic alterations elevated baseline reactive oxygen species (ROS) levels, leading these cells to become highly sensitive to ROS induction. In vivo xenograft experiments and single-cell RNA sequencing demonstrated that intratumor heterogeneity necessitates the combination of a ROS inducer and a MEK inhibitor to inhibit both tumor growth and metastasis. Ex vivo pharmacoscopy of 62 human metastatic melanomas confirmed that MEK inhibitor-resistant tumors significantly benefited from the combination therapy. Finally, oxidative stress response and translational suppression corresponded with ROS-inducer sensitivity in 486 cancer cell lines, independent of cancer type. These findings link transcriptional plasticity to a metabolic phenotype that can be inhibited by ROS inducers in melanoma and other cancers., Significance: Metabolic reprogramming in drug-resistant NRAS-mutated melanoma cells confers sensitivity to ROS induction, which suppresses tumor growth and metastasis in combination with MAPK pathway inhibitors., (©2023 American Association for Cancer Research.)
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- 2023
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30. Prospective observational study of the role of the microbiome in BCG responsiveness prediction (SILENT-EMPIRE): a study protocol.
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Bieri U, Scharl M, Sigg S, Szczerba BM, Morsy Y, Rüschoff JH, Schraml PH, Krauthammer M, Hefermehl LJ, Eberli D, and Poyet C
- Subjects
- Adjuvants, Immunologic, Administration, Intravesical, BCG Vaccine therapeutic use, Female, Humans, Male, Models, Statistical, Observational Studies as Topic, Prognosis, Microbiota, Urinary Bladder Neoplasms pathology
- Abstract
Introduction: The human microbiota, the community of micro-organisms in different cavities, has been increasingly linked with inflammatory and neoplastic diseases. While investigation into the gut microbiome has been robust, the urinary microbiome has only recently been described. Investigation into the relationship between bladder cancer (BC) and the bladder and the intestinal microbiome may elucidate a pathophysiological relationship between the two. The bladder or the intestinal microbiome or the interplay between both may also act as a non-invasive biomarker for tumour behaviour. While these associations have not yet been fully investigated, urologists have been manipulating the bladder microbiome for treatment of BC for more than 40 years, treating high grade non-muscle invasive BC (NMIBC) with intravesical BCG immunotherapy. Neither the association between the microbiome sampled directly from bladder tissue and the response to BCG-therapy nor the association between response to BCG-therapy with the faecal microbiome has been studied until now. A prognostic tool prior to initiation of BCG-therapy is still needed., Methods and Analysis: In patients with NMIBC bladder samples will be collected during surgery (bladder microbiome assessment), faecal samples (microbiome assessment), instrumented urine and blood samples (biobank) will also be taken. We will analyse the microbial community by 16S rDNA gene amplicon sequencing. The difference in alpha diversity (diversity of species within each sample) and beta diversity (change in species diversity) between BCG-candidates will be assessed. Subgroup analysis will be performed which will lead to the development of a clinical prediction model estimating risk of BCG-response., Ethics and Dissemination: The study has been approved by the Cantonal Ethics Committee Zurich (2021-01783) and it is being conducted in accordance with the Declaration of Helsinki and Good Clinical Practice. Study results will be disseminated through peer-reviewed journals and national and international scientific conferences., Trial Registration Number: NCT05204199., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)
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- 2022
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31. AI support for ethical decision-making around resuscitation: proceed with care.
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Biller-Andorno N, Ferrario A, Joebges S, Krones T, Massini F, Barth P, Arampatzis G, and Krauthammer M
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- Humans, Pandemics, Resuscitation Orders, SARS-CoV-2, Artificial Intelligence, COVID-19
- Abstract
Artificial intelligence (AI) systems are increasingly being used in healthcare, thanks to the high level of performance that these systems have proven to deliver. So far, clinical applications have focused on diagnosis and on prediction of outcomes. It is less clear in what way AI can or should support complex clinical decisions that crucially depend on patient preferences. In this paper, we focus on the ethical questions arising from the design, development and deployment of AI systems to support decision-making around cardiopulmonary resuscitation and the determination of a patient's Do Not Attempt to Resuscitate status (also known as code status). The COVID-19 pandemic has made us keenly aware of the difficulties physicians encounter when they have to act quickly in stressful situations without knowing what their patient would have wanted. We discuss the results of an interview study conducted with healthcare professionals in a university hospital aimed at understanding the status quo of resuscitation decision processes while exploring a potential role for AI systems in decision-making around code status. Our data suggest that (1) current practices are fraught with challenges such as insufficient knowledge regarding patient preferences, time pressure and personal bias guiding care considerations and (2) there is considerable openness among clinicians to consider the use of AI-based decision support. We suggest a model for how AI can contribute to improve decision-making around resuscitation and propose a set of ethically relevant preconditions-conceptual, methodological and procedural-that need to be considered in further development and implementation efforts., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. No commercial re-use. See rights and permissions. Published by BMJ.)
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- 2022
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32. How to Synchronize Longitudinal Patient Data With the Underlying Disease Progression: A Pilot Study Using the Biomarker CRP for Timing COVID-19.
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Maibach MA, Allam A, Hilty MP, Perez Gonzalez NA, Buehler PK, Wendel Garcia PD, Brugger SD, Ganter CC, Krauthammer M, Schuepbach RA, and Bartussek J
- Abstract
The continued digitalization of medicine has led to an increased availability of longitudinal patient data that allows the investigation of novel and known diseases in unprecedented detail. However, to accurately describe any underlying pathophysiology and allow inter-patient comparisons, individual patient trajectories have to be synchronized based on temporal markers. In this pilot study, we use longitudinal data from critically ill ICU COVID-19 patients to compare the commonly used alignment markers "onset of symptoms," "hospital admission," and "ICU admission" with a novel objective method based on the peak value of the inflammatory marker C-reactive protein (CRP). By applying our CRP-based method to align the progression of neutrophils and lymphocytes, we were able to define a pathophysiological window that improved mortality risk stratification in our COVID-19 patient cohort. Our data highlights that proper synchronization of longitudinal patient data is crucial for accurate interpatient comparisons and the definition of relevant subgroups. The use of objective temporal disease markers will facilitate both translational research efforts and multicenter trials., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Maibach, Allam, Hilty, Perez Gonzalez, Buehler, Wendel Garcia, Brugger, Ganter, The CoViD-19 ICU-Research Group Zurich, The RISC-19-ICU Investigators, Krauthammer, Schuepbach and Bartussek.)
- Published
- 2021
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