Institut für Medizintechnik und Medizininformatik
Dauerhafte URI für die Sammlunghttps://irf.fhnw.ch/handle/11654/23
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64 Ergebnisse
Ergebnisse nach Hochschule und Institut
Publikation Automatische Datenextraktion aus Anamnesebögen(Hochschule für Life Sciences FHNW, 2024) Kamber, Lukas; Kahraman, Abdullah; Universität Zürich11 - Studentische ArbeitPublikation Characterization of cells for In-Vitro Fertilization(Hochschule für Life Sciences, 2024) Braun Ponce de Leon, Andreas; Nahum, Uri; Smart-Pick GmbH; Universitätsspital Basel, Basel11 - Studentische ArbeitPublikation GPS für das Becken. 3D Visualisierung von anatomischen Strukturen(Hochschule für Life Sciences FHNW, 2024) Bopp, Nicolas; Brodbeck, Dominique; Universitätsspital Zürich (USZ), Zürich11 - Studentische ArbeitPublikation Cloud-based three-dimensional pattern analysis and classification of proximal humeral fractures – A feasibility study(EasyChair, 2022) Kalt, Denise; Gerber Popp, Ariane; Degen, Markus; Brodbeck, Dominique; Coigny, Florian; Suter, Thomas; Schkommodau, Erik; Rodriguez y Baena, Ferdinando; Giles, Joshua W.; Stindel, EricFor the complex clinical issue of treatment decision for proximal humeral fractures, dedicated software based on three-dimensional (3D) computer tomography (CT) models would potentially allow for a more accurate fracture classification and help to plan the surgical strategy needed to reduce the fracture in the operating theatre. The aim of this study was to elaborate the feasibility of implementation of such software using state-of-the-art cloud technology to enable access to its functionalities in a distributed manner. Feasibility was studied by implementation of a prototype application, which was tested in a usability study with five biomedical engineers. Implementation of a cloud-based solution was feasible using state-of-the-art technology under application of a specific software architectural approach allowing to distribute computational load between client and server. Mean System Usability Scale (SUS) Score for the developed application was determined to be 63 (StDev 20.4). These results can be interpreted as a medium low usability with high standard deviation of the measured SUS score. We conclude that more test subjects should be included in future studies and the developed application should be evaluated with a representative user group such as orthopaedic shoulder surgeons in a clinical setting.04B - Beitrag KonferenzschriftPublikation Tracking the orientation of deep brain stimulation electrodes using an embedded magnetic sensor(2021) Vergne, Céline; Madec, Morgan; Hemm-Ode, Simone; Quirin, Thomas; Vogel, Dorian; Hebrard, Luc; Pascal, JorisThis paper proposes a three-dimensional (3D) orientation tracking method of a 3D magnetic sensor embedded in a 2.5 mm diameter electrode. Our system aims to be used during intraoperative surgery to detect the orientation of directional leads (D-leads) for deep brain stimulation (DBS).06 - PräsentationPublikation Author Correction. The dengue-specific immune response and antibody identification with machine learning(Nature, 20.01.2024) Natali, Eriberto Noel; Horst, Alexander; Meier, Patrick; Greiff, Victor; Nuvolone, Mario; Babrak, Lmar Marie; Fink, Katja; Miho, EnkelejdaDengue virus poses a serious threat to global health and there is no specific therapeutic for it. Broadly neutralizing antibodies recognizing all serotypes may be an effective treatment. High-throughput adaptive immune receptor repertoire sequencing (AIRR-seq) and bioinformatic analysis enable in-depth understanding of the B-cell immune response. Here, we investigate the dengue antibody response with these technologies and apply machine learning to identify rare and underrepresented broadly neutralizing antibody sequences. Dengue immunization elicited the following signatures on the antibody repertoire: (i) an increase of CDR3 and germline gene diversity; (ii) a change in the antibody repertoire architecture by eliciting power-law network distributions and CDR3 enrichment in polar amino acids; (iii) an increase in the expression of JNK/Fos transcription factors and ribosomal proteins. Furthermore, we demonstrate the applicability of computational methods and machine learning to AIRR-seq datasets for neutralizing antibody candidate sequence identification. Antibody expression and functional assays have validated the obtained results.01A - Beitrag in wissenschaftlicher ZeitschriftPublikation The dengue-specific immune response and antibody identification with machine learning(Nature, 20.01.2024) Natali, Eriberto Noel; Horst, Alexander; Meier, Patrick; Greiff, Victor; Nuvolone, Mario; Babrak, Lmar Marie; Fink, Katja; Miho, EnkelejdaDengue virus poses a serious threat to global health and there is no specific therapeutic for it. Broadly neutralizing antibodies recognizing all serotypes may be an effective treatment. High-throughput adaptive immune receptor repertoire sequencing (AIRR-seq) and bioinformatic analysis enable in-depth understanding of the B-cell immune response. Here, we investigate the dengue antibody response with these technologies and apply machine learning to identify rare and underrepresented broadly neutralizing antibody sequences. Dengue immunization elicited the following signatures on the antibody repertoire: (i) an increase of CDR3 and germline gene diversity; (ii) a change in the antibody repertoire architecture by eliciting power-law network distributions and CDR3 enrichment in polar amino acids; (iii) an increase in the expression of JNK/Fos transcription factors and ribosomal proteins. Furthermore, we demonstrate the applicability of computational methods and machine learning to AIRR-seq datasets for neutralizing antibody candidate sequence identification. Antibody expression and functional assays have validated the obtained results.01A - Beitrag in wissenschaftlicher ZeitschriftPublikation Single-molecule real-time sequencing of the M protein.Toward personalized medicine in monoclonal gammopathies(Wiley, 05.08.2022) Cascino, Pasquale; Nevone, Alice; Piscitelli, Maggie; Scopelliti, Claudia; Girelli, Maria; Mazzini, Giulia; Caminito, Serena; Russo, Giancarlo; Milani, Paolo; Basset, Marco; Foli, Andrea; Fazio, Francesca; Casarini, Simona; Massa, Margherita; Bozzola, Margherita; Ripepi, Jessica; Sesta, Melania Antonietta; Acquafredda, Gloria; De Cicco, Marica; Moretta, Antonia; Rognoni, Paola; Milan, Enrico; Ricagno, Stefano; Lavatelli, Francesca; Petrucci, Maria Teresa; Miho, Enkelejda; Klersy, Catherine; Merlini, Giampaolo; Palladini, Giovanni; Nuvolone, Mario01A - Beitrag in wissenschaftlicher ZeitschriftPublikation In silico proof of principle of machine learning-based antibody design at unconstrained scale(Taylor & Francis, 04.04.2022) Akbar, Rahmad; Robert, Philippe A.; Weber, Cédric R.; Widrich, Michael; Frank, Robert; Pavlović, Milena; Scheffer, Lonneke; Chernigovskaya, Maria; Snapkov, Igor; Slabodkin, Andrei; Mehta, Brij Bhushan; Miho, Enkelejda; Lund-Johansen, Fridtjof; Andersen, Jan Terje; Hochreiter, Sepp; Hobæk Haff, Ingrid; Klambauer, Günter; Sandve, Geir Kjetil; Greiff, VictorGenerative machine learning (ML) has been postulated to become a major driver in the computational design of antigen-specific monoclonal antibodies (mAb). However, efforts to confirm this hypothesis have been hindered by the infeasibility of testing arbitrarily large numbers of antibody sequences for their most critical design parameters: paratope, epitope, affinity, and developability. To address this challenge, we leveraged a lattice-based antibody-antigen binding simulation framework, which incorporates a wide range of physiological antibody-binding parameters. The simulation framework enables the computation of synthetic antibody-antigen 3D-structures, and it functions as an oracle for unrestricted prospective evaluation and benchmarking of antibody design parameters of ML-generated antibody sequences. We found that a deep generative model, trained exclusively on antibody sequence (one dimensional: 1D) data can be used to design conformational (three dimensional: 3D) epitope-specific antibodies, matching, or exceeding the training dataset in affinity and developability parameter value variety. Furthermore, we established a lower threshold of sequence diversity necessary for high-accuracy generative antibody ML and demonstrated that this lower threshold also holds on experimental real-world data. Finally, we show that transfer learning enables the generation of high-affinity antibody sequences from low-N training data. Our work establishes a priori feasibility and the theoretical foundation of high-throughput ML-based mAb design.01A - Beitrag in wissenschaftlicher ZeitschriftPublikation Kidins220 regulates the development of B cells bearing the λ light chain(eLife Sciences Publications, 25.01.2024) Schaffer, Anna-Maria; Fiala, Gina Jasmin; Hils, Miriam; Natali, Eriberto; Babrak, Lmar; Herr, Laurenz Alexander; Romero-Mulero, Mari Carmen; Cabezas-Wallscheid, Nina; Rizzi, Marta; Miho, Enkelejda; Schamel, Wolfgang W.A.; Minguet, SusanaThe ratio between κ and λ light chain (LC)-expressing B cells varies considerably between species. We recently identified Kinase D-interacting substrate of 220 kDa (Kidins220) as an interaction partner of the BCR. In vivo ablation of Kidins220 in B cells resulted in a marked reduction of λLC-expressing B cells. Kidins220 knockout B cells fail to open and recombine the genes of the Igl locus, even in genetic scenarios where the Igk genes cannot be rearranged or where the κLC confers autoreactivity. Igk gene recombination and expression in Kidins220-deficient B cells is normal. Kidins220 regulates the development of λLC B cells by enhancing the survival of developing B cells and thereby extending the time-window in which the Igl locus opens and the genes are rearranged and transcribed. Further, our data suggest that Kidins220 guarantees optimal pre-BCR and BCR signaling to induce Igl locus opening and gene recombination during B cell development and receptor editing.01A - Beitrag in wissenschaftlicher Zeitschrift