Evaluation of risk management in a natalizumab home infusion procedure.

Natalizumab is a well-established disease-modifying therapy used in active multiple sclerosis (MS). The most serious adverse event is progressive multifocal leukoencephalopathy. For safety reasons, hospital implementation is mandatory. The SARS-CoV-2 pandemic has deeply affected hospital practices leading French authorities to temporarily authorize to administer the treatment at home. The safety of natalizumab home administration should be assessed to allow ongoing home infusion. The aim of the study is to describe the procedure and assess the safety in a home infusion natalizumab model. Patients presenting relapsing-remitting MS treated by natalizumab for over two years, non-exposed to John Cunningham Virus (JCV) and living in the Lille area (France) were included from July 2020 to February 2021 to receive natalizumab infusion at home every four weeks for 12 months. Teleconsultation occurrence, infusion occurrence, infusion cancelling, JCV risk management, annual MRI completion were analyzed. The number of teleconsultations allowing infusion was 365 (37 patients included in the analysis), all home infusions were preceded by a teleconsultation. Nine patients did not complete the one-year home infusion follow-up. Two teleconsultations canceled infusions. Two teleconsultations led to a hospital visit to assess a potential relapse. No severe adverse event was reported. All 28 patients who have completed the follow-up benefited from biannual hospital examination and JCV serologies and annual MRI. Our results suggested that the established home natalizumab procedure was safe using the university hospital home-care department. However, the procedure should be evaluated using home-based services outside the university hospital.

Analysis of the uses of digital technologies dedicated to the synchronization: interests for the study of an organizational transformation in automotive design engineering

This article, which is the result of ongoing ergonomic research, aims to shed light on the activity of design workers in the French automotive industry through the organisational uses of digital technologies. These technologies have been integrated into the activity of these workers for several decades and aim to organise the collective activity deployed throughout a design project within distributed and virtual teams. At the same time, this industrial sector is subject to multiple constraints of an economic and ecological nature, leading it to make its organisational methods more flexible in order to innovate while shortening its design times. This context of digitalisation and organisational transformation questions the individual and collective activity of synchronisation of these populations, particularly with regard to the resources and means at their disposal to preserve their room for manoeuvre and regulation strategies. Thus, two case studies were constituted through the analysis of two automotive design projects taking place in different circumstances. Indeed, one of the two projects took place during the Covid-19 health crisis, leading us to adapt our data collection methods but also to reconsider the digitalisation of this design activity, in which interactions between workers could only take place through the use of digital technologies. Thus, these two case studies address the links between the flexibilisation of individuals and organisations, on the one hand, and regulation strategies on the other, through the use of digital technologies. The results of these case studies show that the use of digital technologies, as a resource or constraint for the activity, is linked to the characteristics of the organisation in which they are deployed at two levels;these forms of organisation in projects create specific uses of these technologies and could not exist as they are without them.

Maximum Entropy Networks Applied on Twitter Disinformation Datasets

Identifying and detecting disinformation is a major challenge. Twitter provides datasets of disinformation campaigns through their information operations report. We compare the results of community detection using a classical network representation with a maximum entropy network model. We conclude that the latter method is useful to identify the most significant interactions in the disinformation network over multiple datasets. We also apply the method to a disinformation dataset related to COVID-19, which allows us to assess the repeatability of studies on disinformation datasets. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.