Preclinical Safety Assessment of 2 Inhaled Single-Domain Antibodies in the Cynomolgus Monkey

2020 ◽  
pp. 019262332097238
Author(s):  
Richard Haworth ◽  
Molly Boyle ◽  
Paul Edwards ◽  
Reenu Gupta ◽  
Rajni Fagg ◽  
...  
Keyword(s):  
Lymph Node ◽  
Cynomolgus Monkey ◽  
Tumor Necrosis Factor Receptor ◽  
Lavage Fluid ◽  
Single Domain ◽  
Repeat Dose ◽  
Inhalation Study ◽  
First Case ◽  
Single Domain Antibodies

The safety of 2 single domain antibodies (dAbs) was evaluated by inhalation toxicology studies in the cynomolgus monkey. In the first case study, a 14-day repeat-dose study evaluating an anti-thymic stromal lymphopoietin (anti-TSLP) dAb resulted in minimal mononuclear inflammatory cell infiltrates in the lungs, increases in lymphocytes in bronchoalveolar lavage fluid, and development of antidrug antibodies (ADAs). In a 6-week inhalation study, there was an increase in incidence and/or severity of mononuclear cell infiltrates in the lung, increased cellularity in the tracheobronchial lymph node (TBLN), and development of ADA. The second case study evaluated a change in duration of inhalation dosing, a different route of exposure (intravenous or IV), and recovery following an off-dose period with an anti-tumor necrosis factor receptor 1 dAb. A 7-day repeat-dose inhalation study and a 14-day IV study produced no microscopic effects in the lung, whereas a 14-day inhalation study resulted in moderate increases in pulmonary perivascular/peribronchiolar/alveolar lymphocytic infiltrates and increased cellularity in the TBLN, with partial and full recovery, respectively, after 14 days. The lung and lymph node findings seen after inhalation of either dAb were considered secondary to the immunogenic response to a human protein and were considered nonadverse.

scholarly journals Generation and Characterization of Small Single Domain Antibodies Inhibiting Human Tumor Necrosis Factor Receptor 1

2014 ◽  
Vol 290 (7) ◽  
pp. 4022-4037 ◽  
Author(s):  
Sophie Steeland ◽  
Leen Puimège ◽  
Roosmarijn E. Vandenbroucke ◽  
Filip Van Hauwermeiren ◽  
Jurgen Haustraete ◽  
...  
Keyword(s):  
Tumor Necrosis Factor ◽  
Tumor Necrosis ◽  
Tumor Necrosis Factor Receptor ◽  
Human Tumor ◽  
Single Domain ◽  
Human Tumor Necrosis Factor ◽  
Single Domain Antibodies ◽  
Necrosis Factor

Scales and Units

2018 ◽  
Author(s):  
Kathryn M. de Luna
Keyword(s):  
Language Change ◽  
Historical Linguistics ◽  
Bantu Languages ◽  
The North ◽  
First Case ◽  
Linguistic Evidence ◽  
River Region ◽  
History Of ◽  
Alternative Approaches

This chapter uses two case studies to explore how historians study language movement and change through comparative historical linguistics. The first case study stands as a short chapter in the larger history of the expansion of Bantu languages across eastern, central, and southern Africa. It focuses on the expansion of proto-Kafue, ca. 950–1250, from a linguistic homeland in the middle Kafue River region to lands beyond the Lukanga swamps to the north and the Zambezi River to the south. This expansion was made possible by a dramatic reconfiguration of ties of kinship. The second case study explores linguistic evidence for ridicule along the Lozi-Botatwe frontier in the mid- to late 19th century. Significantly, the units and scales of language movement and change in precolonial periods rendered visible through comparative historical linguistics bring to our attention alternative approaches to language change and movement in contemporary Africa.

Applications of random graphs

2017 ◽  
Author(s):  
A.C.C. Coolen ◽  
A. Annibale ◽  
E.S. Roberts
Keyword(s):  
Social Networks ◽  
Random Graphs ◽  
Interaction Network ◽  
Power Grids ◽  
Protein Protein Interaction ◽  
Topological Features ◽  
First Case ◽  
The Stability ◽  
Protein Protein Interaction Network

This chapter reviews graph generation techniques in the context of applications. The first case study is power grids, where proposed strategies to prevent blackouts have been tested on tailored random graphs. The second case study is in social networks. Applications of random graphs to social networks are extremely wide ranging – the particular aspect looked at here is modelling the spread of disease on a social network – and how a particular construction based on projecting from a bipartite graph successfully captures some of the clustering observed in real social networks. The third case study is on null models of food webs, discussing the specific constraints relevant to this application, and the topological features which may contribute to the stability of an ecosystem. The final case study is taken from molecular biology, discussing the importance of unbiased graph sampling when considering if motifs are over-represented in a protein–protein interaction network.

Investigating the potential of redundant manipulators in narrow channels

2020 ◽  
pp. 095440622096451
Author(s):  
Ashish Singla ◽  
Jyotindra Narayan ◽  
Himanshu Arora
Keyword(s):  
Case Studies ◽  
Fuzzy Inference ◽  
Redundant Manipulators ◽  
Redundancy Resolution ◽  
Inference System ◽  
Narrow Channels ◽  
First Case ◽  
Task Priority ◽  
Patient Side

In this paper, an attempt has been made to investigate the potential of redundant manipulators, while tracking trajectories in narrow channels. The behavior of redundant manipulators is important in many challenging applications like under-water welding in narrow tanks, checking the blockage in sewerage pipes, performing a laparoscopy operation etc. To demonstrate this snake-like behavior, redundancy resolution scheme is utilized using two different approaches. The first approach is based on the concept of task priority, where a given task is split and prioritize into several subtasks like singularity avoidance, obstacle avoidance, torque minimization, and position preference over orientation etc. The second approach is based on Adaptive Neuro Fuzzy Inference System (ANFIS), where the training is provided through given datasets and the results are back-propagated using augmentation of neural networks with fuzzy logics. Three case studies are considered in this work to demonstrate the redundancy resolution of serial manipulators. The first case study of 3-link manipulator is attempted with both the approaches, where the objective is to track the desired trajectory while avoiding multiple obstacles. The second case study of 7-link manipulator, tracking trajectory in a narrow channel, is investigated using the concept of task priority. The realistic application of minimum-invasive surgery (MIS) based trajectory tracking is considered as the third case study, which is attempted using ANFIS approach. The 5-link spatial redundant manipulator, also known as a patient-side manipulator being developed at CSIR-CSIO, Chandigarh is used to track the desired surgical cuts. Through the three case studies, it is well demonstrated that both the approaches are giving satisfactory results.

Engineering of single-domain antibodies for next generation snakebite antivenoms

2021 ◽  
Author(s):  
Carla F.C. Fernandes ◽  
Soraya S. Pereira ◽  
Marcos B. Luiz ◽  
Nauanny K.R.L. Silva ◽  
Marcela Cristina da Silva ◽  
...  
Keyword(s):  
Single Domain ◽  
Next Generation ◽  
Single Domain Antibodies

scholarly journals Sensitivity analysis for interpretation of machine learning based segmentation models in cardiac MRI

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Markus J. Ankenbrand ◽  
Liliia Shainberg ◽  
Michael Hock ◽  
David Lohr ◽  
Laura M. Schreiber
Keyword(s):  
Neural Networks ◽  
Sensitivity Analysis ◽  
Cardiac Magnetic Resonance ◽  
Magnetic Resonance ◽  
Cardiac Mri ◽  
Magnetic Resonance Images ◽  
Model Interpretation ◽  
First Case ◽  
Segmentation Models

Abstract Background Image segmentation is a common task in medical imaging e.g., for volumetry analysis in cardiac MRI. Artificial neural networks are used to automate this task with performance similar to manual operators. However, this performance is only achieved in the narrow tasks networks are trained on. Performance drops dramatically when data characteristics differ from the training set properties. Moreover, neural networks are commonly considered black boxes, because it is hard to understand how they make decisions and why they fail. Therefore, it is also hard to predict whether they will generalize and work well with new data. Here we present a generic method for segmentation model interpretation. Sensitivity analysis is an approach where model input is modified in a controlled manner and the effect of these modifications on the model output is evaluated. This method yields insights into the sensitivity of the model to these alterations and therefore to the importance of certain features on segmentation performance. Results We present an open-source Python library (misas), that facilitates the use of sensitivity analysis with arbitrary data and models. We show that this method is a suitable approach to answer practical questions regarding use and functionality of segmentation models. We demonstrate this in two case studies on cardiac magnetic resonance imaging. The first case study explores the suitability of a published network for use on a public dataset the network has not been trained on. The second case study demonstrates how sensitivity analysis can be used to evaluate the robustness of a newly trained model. Conclusions Sensitivity analysis is a useful tool for deep learning developers as well as users such as clinicians. It extends their toolbox, enabling and improving interpretability of segmentation models. Enhancing our understanding of neural networks through sensitivity analysis also assists in decision making. Although demonstrated only on cardiac magnetic resonance images this approach and software are much more broadly applicable.

scholarly journals Single-domain antibodies make a difference

Science ◽  
2021 ◽  
Vol 371 (6530) ◽  
pp. 681-682 ◽  
Author(s):  
Xavier Saelens ◽  
Bert Schepens
Keyword(s):  
Single Domain ◽  
Single Domain Antibodies
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