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- Ljungman, P., et al.
(författare)
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Respiratory virus infections after stem cell transplantation : a prospective study from the Infectious Diseases Working Party of the European Group for Blood and Marrow Transplantation
- 2001
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Ingår i: Bone Marrow Transplantation. - : Springer Science and Business Media LLC. - 0268-3369 .- 1476-5365. ; 28:5, s. 479-484
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Tidskriftsartikel (refereegranskat)abstract
- Community-acquired respiratory virus infections are a cause of mortality after stem cell transplantation (SCT). A prospective study was performed at 37 centers to determine their frequency and importance. Additional cases were also collected to allow the analysis of risk factors for severe infection. Forty episodes were collected in the prospective study and 53 additional episodes through subsequent case collection. The frequency of documented respiratory virus infections was 3.5% among 819 allogeneic and 0.4% among 1154 autologous SCT patients transplanted during the study period. The frequency of lower respiratory tract infections (LRTI) was 2.1% among allogeneic and 0.2% among autologous SCT patients. The mortality within 28 days from diagnosis of a respiratory viral infection was 1.1% among allogeneic SCT while no autologous SCT patient died. The deaths of five patients (0.6%) were directly attributed to a respiratory virus infection (three RSV; two influenza A). On multivariate analysis, lymphocytopenia increased the risk for LRTI (P = 0.008). Lymphocytopenia was also a significant risk factor for LRTI in patients with RSV infections. The overall mortality in RSV infection was 30.4% and the direct RSV-associated mortality was 17.4%. For influenza A virus infection, the corresponding percentages were 23.0% and 15.3%. This prospective study supports the fact that community-acquired respiratory virus infections cause transplant-related mortality after SCT.
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- Zeiser, R., et al.
(författare)
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Ruxolitinib in corticosteroid-refractory graft-versus-host disease after allogeneic stem cell transplantation: a multicenter survey
- 2015
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Ingår i: Leukemia. - : Springer Science and Business Media LLC. - 0887-6924 .- 1476-5551. ; 29:10, s. 2062-2068
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Tidskriftsartikel (refereegranskat)abstract
- Despite major improvements in allogeneic hematopoietic cell transplantation over the past decades, corticosteroid-refractory (SR) acute (a) and chronic (c) graft-versus-host disease (GVHD) cause high mortality. Preclinical evidence indicates the potent anti-inflammatory properties of the JAK1/2 inhibitor ruxolitinib. In this retrospective survey, 19 stem cell transplant centers in Europe and the United States reported outcome data from 95 patients who had received ruxolitinib as salvage therapy for SR-GVHD. Patients were classified as having SR-aGVHD (n = 54, all grades III or IV) or SR-cGVHD (n = 41, all moderate or severe). The median number of previous GVHD-therapies was 3 for both SR-aGVHD (1-7) and SR-cGVHD (1-10). The overall response rate was 81.5% (44/54) in SR-aGVHD including 25 complete responses (46.3%), while for SR-cGVHD the ORR was 85.4% (35/41). Of those patients responding to ruxolitinib, the rate of GVHD-relapse was 6.8% (3/44) and 5.7% (2/35) for SR-aGVHD and SR-cGVHD, respectively. The 6-month-survival was 79% (67.3-90.7%, 95% confidence interval (CI)) and 97.4% (92.3-100%, 95% CI) for SR-aGVHD and SR-cGVHD, respectively. Cytopenia and cytomegalovirus-reactivation were observed during ruxolitinib treatment in both SR-aGVHD (30/54, 55.6% and 18/54, 33.3%) and SR-cGVHD (7/41, 17.1% and 6/41, 14.6%) patients. Ruxolitinib may constitute a promising new treatment option for SR-aGVHD and SR-cGVHD that should be validated in a prospective trial.
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- Botteghi, N., et al.
(författare)
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Low dimensional state representation learning with reward-shaped priors
- 2020
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Ingår i: Proceedings - International Conference on Pattern Recognition. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781728188089 ; , s. 3736-3743
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Konferensbidrag (refereegranskat)abstract
- Reinforcement Learning has been able to solve many complicated robotics tasks without any need for feature engineering in an end-to-end fashion. However, learning the optimal policy directly from the sensory inputs, i.e the observations, often requires processing and storage of a huge amount of data. In the context of robotics, the cost of data from real robotics hardware is usually very high, thus solutions that achieve high sample-efficiency are needed. We propose a method that aims at learning a mapping from the observations into a lower-dimensional state space. This mapping is learned with unsupervised learning using loss functions shaped to incorporate prior knowledge of the environment and the task. Using the samples from the state space, the optimal policy is quickly and efficiently learned. We test the method on several mobile robot navigation tasks in a simulation environment and also on a real robot. A video of our experiments can be found at: https://youtu.be/dgWxmfSv95U.
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- Botteghi, N., et al.
(författare)
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Low Dimensional State Representation Learning with Robotics Priors in Continuous Action Spaces
- 2021
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Ingår i: IEEE International Conference on Intelligent Robots and Systems. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665417150 ; , s. 190-197
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Konferensbidrag (refereegranskat)abstract
- Reinforcement learning algorithms have proven to be capable of solving complicated robotics tasks in an end-to-end fashion without any need for hand-crafted features or policies. Especially in the context of robotics, in which the cost of real-world data is usually extremely high, Reinforcement Learning solutions achieving high sample efficiency are needed. In this paper, we propose a framework combining the learning of a low-dimensional state representation, from high-dimensional observations coming from the robot's raw sensory readings, with the learning of the optimal policy, given the learned state representation. We evaluate our framework in the context of mobile robot navigation in the case of continuous state and action spaces. Moreover, we study the problem of transferring what learned in the simulated virtual environment to the real robot without further retraining using real-world data in the presence of visual and depth distractors, such as lighting changes and moving obstacles. A video of our experiments can be found at: https://youtu.be/rUdGPKr2Wuo.
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- Botteghi, N., et al.
(författare)
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Reinforcement learning helps slam : Learning to build maps
- 2020
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Ingår i: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives. - : International Society for Photogrammetry and Remote Sensing. ; , s. 329-336
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Konferensbidrag (refereegranskat)abstract
- In this research, we investigate the use of Reinforcement Learning (RL) for an effective and robust solution for exploring unknown and indoor environments and reconstructing their maps. We benefit from a Simultaneous Localization and Mapping (SLAM) algorithm for real-time robot localization and mapping. Three different reward functions are compared and tested in different environments with growing complexity. The performances of the three different RL-based path planners are assessed not only on the training environments, but also on an a priori unseen environment to test the generalization properties of the policies. The results indicate that RL-based planners trained to maximize the coverage of the map are able to consistently explore and construct the maps of different indoor environments.
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