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- Guimond, S. E., et al.
(författare)
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Synthetic Heparan Sulfate Mimetic Pixatimod (PG545) Potently Inhibits SARS-CoV-2 by Disrupting the Spike-ACE2 Interaction
- 2022
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Ingår i: ACS Central Science. - : American Chemical Society (ACS). - 2374-7943 .- 2374-7951. ; 8:5, s. 527-545
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Tidskriftsartikel (refereegranskat)abstract
- Heparan sulfate (HS) is a cell surface polysaccharide recently identified as a coreceptor with the ACE2 protein for the S1 spike protein on SARS-CoV-2 virus, providing a tractable new therapeutic target. Clinically used heparins demonstrate an inhibitory activity but have an anticoagulant activity and are supply-limited, necessitating alternative solutions. Here, we show that synthetic HS mimetic pixatimod (PG545), a cancer drug candidate, binds and destabilizes the SARS-CoV-2 spike protein receptor binding domain and directly inhibits its binding to ACE2, consistent with molecular modeling identification of multiple molecular contacts and overlapping pixatimod and ACE2 binding sites. Assays with multiple clinical isolates of SARS-CoV-2 virus show that pixatimod potently inhibits the infection of monkey Vero E6 cells and physiologically relevant human bronchial epithelial cells at safe therapeutic concentrations. Pixatimod also retained broad potency against variants of concern (VOC) including B.1.1.7 (Alpha), B.1.351 (Beta), B.1.617.2 (Delta), and B.1.1.529 (Omicron). Furthermore, in a K18-hACE2 mouse model, pixatimod significantly reduced SARS-CoV-2 viral titers in the upper respiratory tract and virus-induced weight loss. This demonstration of potent anti-SARS-CoV-2 activity tolerant to emerging mutations establishes proof-of-concept for targeting the HS-Spike protein-ACE2 axis with synthetic HS mimetics and provides a strong rationale for clinical investigation of pixatimod as a potential multimodal therapeutic for COVID-19.
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- Brunton, A., et al.
(författare)
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A Low-Dimensional Representation for Robust Partial Isometric Correspondences Computation
- 2014
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Ingår i: Graphical Models. - : Elsevier. - 1524-0703 .- 1524-0711. ; 76:2, s. 70-85
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Tidskriftsartikel (refereegranskat)abstract
- Intrinsic shape matching has become the standard approach for pose invariant correspondence estimation among deformable shapes. Most existing approaches assume global consistency. While global isometric matching is well understood, only a few heuristic solutions are known for partial matching. Partial matching is particularly important for robustness to topological noise, which is a common problem in real-world scanner data. We introduce a new approach to partial isometric matching based on the observation that isometries are fully determined by local information: a map of a single point and its tangent space fixes an isometry. We develop a new representation for partial isometric maps based on equivalence classes of correspondences between pairs of points and their tangent-spaces. We apply our approach to register partial point clouds and compare it to the state-of-the-art methods, where we obtain significant improvements over global methods for real-world data and stronger guarantees than previous partial matching algorithms.
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- Wand, M., et al.
(författare)
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Analysis of Neural Network Based Proportional Myoelectric Hand Prosthesis Control
- 2022
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Ingår i: Ieee Transactions on Biomedical Engineering. - : Institute of Electrical and Electronics Engineers (IEEE). - 0018-9294 .- 1558-2531. ; 69:7, s. 2283-2293
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Tidskriftsartikel (refereegranskat)abstract
- Objective: We show that state-of-the-art deep neural networks achieve superior results in regression-based multi-class proportional myoelectric hand prosthesis control than two common baseline approaches, and we analyze the neural network mapping to explain why this is the case. Methods: Feedforward neural networks and baseline systems are trained on an offline corpus of 11 able-bodied subjects and 4 prosthesis wearers, using the R-2 score as metric. Analysis is performed using diverse qualitative and quantitative approaches, followed by a rigorous evaluation. Results: Our best neural networks have at least three hidden layers with at least 128 neurons per layer; smaller architectures, as used by many prior studies, perform substantially worse. The key to good performance is to both optimally regress the target movement, and to suppress spurious movements. Due to the properties of the underlying data, this is impossible to achieve with linear methods, but can be attained with high exactness using sufficiently large neural networks. Conclusion: Neural networks perform significantly better than common linear approaches in the given task, in particular when sufficiently large architectures are used. This can be explained by salient properties of the underlying data, and by theoretical and experimental analysis of the neural network mapping. Significance: To the best of our knowledge, this work is the first one in the field which not only reports that large and deep neural networks are superior to existing architectures, but also explains this result.
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