Quentin Le Masne

It is important for people living with multiple sclerosis (MS) to take their medication regularly – and to keep doing so – in order to control their symptoms. Some people with MS receive a medication called interferon beta-1a (Rebif®) as a subcutaneous injection (given just under the skin), and the RebiSmart® electromechanical autoinjector was designed to help them to self-inject such medication. This study aimed to find out whether people were using the RebiSmart® device as often as they… Voir plus

It is important for people living with multiple sclerosis (MS) to take their medication regularly – and to keep doing so – in order to control their symptoms. Some people with MS receive a medication called interferon beta-1a (Rebif®) as a subcutaneous injection (given just under the skin), and the RebiSmart® electromechanical autoinjector was designed to help them to self-inject such medication. This study aimed to find out whether people were using the RebiSmart® device as often as they should be, and how long they continued to use it for. Information was taken from the MSdialog database, which recorded peoples’ use of the RebiSmart® device between January 2014 and November 2019. Records for 2644 people using the device were analyzed. Results showed that the RebiSmart® device was used most of the time (around 91.7%). On average, people kept using the device for around a year and 4 months before stopping. This duration was generally longer for men compared with women, and longer for older people than younger people. These results increase our understanding of how people are using the RebiSmart® device to treat their MS symptoms. Voir moins

Our aim was to develop a machine learning model, using real-world data captured from a connected auto-injector device and from early indicators from the first 3 months of treatment, to predict sub-optimal adherence to recombinant human growth hormone (r-hGH) in patients with growth disorders.

Data from 10,929 children showed that a random forest model with mean and standard deviation of adherence over the first 3 months, infrequent transmission of data, not changing certain comfort… Voir plus

Our aim was to develop a machine learning model, using real-world data captured from a connected auto-injector device and from early indicators from the first 3 months of treatment, to predict sub-optimal adherence to recombinant human growth hormone (r-hGH) in patients with growth disorders.

Data from 10,929 children showed that a random forest model with mean and standard deviation of adherence over the first 3 months, infrequent transmission of data, not changing certain comfort settings, and starting treatment at an older age was important in predicting the risk of sub-optimal adherence in the following 3, 6, or 9 months. Sensitivities ranged between 0.72 and 0.77, and specificities between 0.80 and 0.81.

To the authors’ knowledge, this is the first attempt to integrate a machine learning model into a digital health ecosystem to help healthcare providers to identify patients at risk of sub-optimal adherence to r-hGH in the following 3, 6, or 9 months. This information, together with patient-specific indicators of sub-optimal adherence, can be used to provide support to at-risk patients and their caregivers to achieve optimal adherence and, subsequently, improve clinical outcomes. Voir moins

Here, we describe our experience in offering devices and digital health tools to support GH therapy across some 40 countries. We also discuss how this ecosystem of care has evolved over the years based on learnings and advances in technology. Finally, we offer a glimpse of future planned enhancements and directions for digital health to play a bigger role in better managing conditions treated with GH therapy, as well as model development for adherence prediction.

The early adoption of digital health solutions in the treatment of growth disorders has enabled the collection and analysis of more than 10 years of real-world data using the easypod™ connect platform. Using this rich dataset, we were able to study the impact of engagement on three key treatment-related outcomes: adherence, persistence of use, and growth. In total, data for 17,906 patients were available. The three features, regularity of injection (≤2h vs >2h), change of comfort setting… Voir plus

The early adoption of digital health solutions in the treatment of growth disorders has enabled the collection and analysis of more than 10 years of real-world data using the easypod™ connect platform. Using this rich dataset, we were able to study the impact of engagement on three key treatment-related outcomes: adherence, persistence of use, and growth. In total, data for 17,906 patients were available. The three features, regularity of injection (≤2h vs >2h), change of comfort setting (yes/no), and opting-in to receive injection reminders (yes/no), were used as a proxy for engagement. Patients were assigned to the low-engagement group (n=1,752) when all of their features had the low-engagement flag (>2h, no, no) and to the high engagement group (n=1,081) when all of their features had the high-engagement flag (≤2h, yes, yes). The low-engagement group was down-sampled to 1,081 patients (subsample of n=37 for growth) using the iterative proportional fitting algorithm. Statistical tests were used to study the impact of engagement to the outcomes. The results show that all three outcomes were significantly improved by a factor varying from 1.8 up to 2.2 when the engagement level was high. These results should encourage the promotion of engagement and associated behaviors by both patients and healthcare professionals. Voir moins

Therapeutic proteins are privileged in drug development because of their exquisite specificity, which is due to their three-dimensional conformation in solution. During their manufacture, storage, and delivery, interactions with material surfaces and air interfaces are known to affect their stability. The growing use of automated devices for handling and injection of therapeutics increases their exposure to protocols involving intermittent wetting, during which the solid–liquid and liquid–air… Voir plus

Therapeutic proteins are privileged in drug development because of their exquisite specificity, which is due to their three-dimensional conformation in solution. During their manufacture, storage, and delivery, interactions with material surfaces and air interfaces are known to affect their stability. The growing use of automated devices for handling and injection of therapeutics increases their exposure to protocols involving intermittent wetting, during which the solid–liquid and liquid–air interfaces meet at a triple contact line, which is often dynamic. Using a microfluidic setup, we analyze the effect of a moving triple interface on insulin aggregation in real time over a hydrophobic surface. We combine thioflavin T fluorescence and reflection interference microscopy to concomitantly monitor insulin aggregation and the morphology of the liquid as it dewets the surface. We demonstrate that insulin aggregates in the region of a moving triple interface and not in regions submitted to hydrodynamic shear stress alone, induced by the moving liquid. During dewetting, liquid droplets form on the surface anchored by adsorbed proteins, and the accumulation of amyloid aggregates is observed exclusively as fluorescent rings growing eccentrically around these droplets. The fluorescent rings expand until the entire channel surface sweeped by the triple interface is covered by amyloid fibers. On the basis of our experimental results, we propose a model describing the growth mechanism of insulin amyloid fibers at a moving triple contact line, where proteins adsorbed at a hydrophobic surface are exposed to the liquid–air interface. Voir moins

We have observed that the supercurrent across phase-biased, highly transmitting atomic size contacts is strongly reduced within a broad phase interval around π. We attribute this effect to quasiparticle trapping in one of the discrete subgap Andreev bound states formed at the contact. Trapping occurs essentially when the Andreev energy is smaller than half the superconducting gap Δ, a situation in which the lifetime of trapped quasiparticles is found to exceed 100  μs. The origin of this sharp… Voir plus

We have observed that the supercurrent across phase-biased, highly transmitting atomic size contacts is strongly reduced within a broad phase interval around π. We attribute this effect to quasiparticle trapping in one of the discrete subgap Andreev bound states formed at the contact. Trapping occurs essentially when the Andreev energy is smaller than half the superconducting gap Δ, a situation in which the lifetime of trapped quasiparticles is found to exceed 100  μs. The origin of this sharp energy threshold is presently not understood. Voir moins

Fluctuations of the current through a tunnel junction are measured using a Josephson junction. The current noise adds to the bias current of the Josephson junction and affects its switching out of the supercurrent branch. The experiment is carried out in a regime where switching is determined by thermal activation. The variance of the noise results in an elevated effective temperature, whereas the third cumulant, related to its asymmetric character, leads to a difference in the switching rates… Voir plus

Fluctuations of the current through a tunnel junction are measured using a Josephson junction. The current noise adds to the bias current of the Josephson junction and affects its switching out of the supercurrent branch. The experiment is carried out in a regime where switching is determined by thermal activation. The variance of the noise results in an elevated effective temperature, whereas the third cumulant, related to its asymmetric character, leads to a difference in the switching rates observed for opposite signs of the current through the tunnel junction. Measurements are compared quantitatively with recent theoretical predictions. Voir moins