Antibody Therapies in Autoimmune Encephalitis

Autoimmune encephalitis (AE) comprises a heterogeneous group of disorders in which the host immune system targets self-antigens expressed in the central nervous system. The most conspicuous example is an anti-N-methyl-d-aspartate receptor encephalitis linked to a complex neuropsychiatric syndrome. Current treatment of AE is based on immunotherapy and has been established according to clinical experience and along the concept of a B cell-mediated pathology induced by highly specific antibodies to neuronal surface antigens. In general, immunotherapy for AE follows an escalating approach. When first-line therapy with steroids, immunoglobulins, and/or plasma exchange fails, one converts to second-line immunotherapy. Alkylating agents could be the first choice in this stage. However, due to their side effect profile, most clinicians give preference to monoclonal antibodies (mAbs) directed at B cells such as rituximab. Newer mAbs might be added as a third-line therapy in the future, or be given even earlier if shown effective. In this chapter, we will discuss mAbs targeting B cells (rituximab, ocrelizumab, inebulizumab, daratumumab), IL-6 (tocilizumab, satralizumab), the neonatal Fc receptor (FCRn) (efgartigimod, rozanolixizumab), and the complement cascade (eculizumab). SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13311-021-01178-4

Evidence Propagation and Consensus Formation in Noisy Environments

We study the effectiveness of consensus formation in multi-agent systems where there is both belief updating based on direct evidence and also belief combination between agents. In particular, we consider the scenario in which a population of agents collaborate on the best-of-n problem where the aim is to reach a consensus about which is the best (alternatively, true) state from amongst a set of states, each with a different quality value (or level of evidence). Agents' beliefs are represented within Dempster-Shafer theory by mass functions and we investigate the macro-level properties of four well-known belief combination operators for this multi-agent consensus formation problem: Dempster's rule, Yager's rule, Dubois & Prade's operator and the averaging operator. The convergence properties of the operators are considered and simulation experiments are conducted for different evidence rates and noise levels. Results show that a combination of updating on direct evidence and belief combination between agents results in better consensus to the best state than does evidence updating alone. We also find that in this framework the operators are robust to noise. Broadly, Yager's rule is shown to be the better operator under various parameter values, i.e. convergence to the best state, robustness to noise, and scalability.Comment: 13th international conference on Scalable Uncertainty Managemen

Regarding: Nicotinic acetylcholine receptors α7 and α9 modify tobacco smoke risk for multiple sclerosis.

This is a author manuscript of an article accepted for publication in Multiple Sclerosis Journal. Version of record is available online at Jacobs BM, Smets I, Giovannoni G, Noyce A, Jokubaitis V, Dobson R. Regarding: Nicotinic acetylcholine receptors α7 and α9 modify tobacco smoke risk for multiple sclerosis. Multiple Sclerosis Journal. December 2020. doi:10.1177/1352458520969941. Copyright (c) 2020. The Authors. doi:10.1177/135245852096994