Long-Term Outcomes with Subcutaneous C1-Inhibitor Replacement Therapy for Prevention of Hereditary Angioedema Attacks

Background For the prevention of attacks of hereditary angioedema (HAE), the efficacy and safety of subcutaneous human C1-esterase inhibitor (C1-INH[SC]; HAEGARDA, CSL Behring) was established in the 16-week Clinical Study for Optimal Management of Preventing Angioedema with Low-Volume Subcutaneous C1-Inhibitor Replacement Therapy (COMPACT). Objective To assess the long-term safety, occurrence of angioedema attacks, and use of rescue medication with C1-INH(SC). Methods Open-label, randomized, parallel-arm extension of COMPACT across 11 countries. Patients with frequent angioedema attacks, either study treatment-naive or who had completed COMPACT, were randomly assigned (1:1) to 40 IU/kg or 60 IU/kg C1-INH(SC) twice per week, with conditional uptitration to optimize prophylaxis (ClinicalTrials.gov registration no. NCT02316353). Results A total of 126 patients with a monthly attack rate of 4.3 in 3 months before entry in COMPACT were enrolled and treated for a mean of 1.5 years; 44 patients (34.9%) had more than 2 years of exposure. Mean steady-state C1-INH functional activity increased to 66.6% with 60 IU/kg. Incidence of adverse events was low and similar in both dose groups (11.3 and 8.5 events per patient-year for 40 IU/kg and 60 IU/kg, respectively). For 40 IU/kg and 60 IU/kg, median annualized attack rates were 1.3 and 1.0, respectively, and median rescue medication use was 0.2 and 0.0 times per year, respectively. Of 23 patients receiving 60 IU/kg for more than 2 years, 19 (83%) were attack-free during months 25 to 30 of treatment. Conclusions In patients with frequent HAE attacks, long-term replacement therapy with C1-INH(SC) is safe and exhibits a substantial and sustained prophylactic effect, with the vast majority of patients becoming free from debilitating disease symptoms

Towards the interpretability of machine learning predictions for medical applications targeting personalised therapies: A cancer case survey

Artificial Intelligence is providing astonishing results, with medicine being one of its fa-vourite playgrounds. Machine Learning and, in particular, Deep Neural Networks are behind this revolution. Among the most challenging targets of interest in medicine are cancer diagnosis and therapies but, to start this revolution, software tools need to be adapted to cover the new require-ments. In this sense, learning tools are becoming a commodity but, to be able to assist doctors on a daily basis, it is essential to fully understand how models can be interpreted. In this survey, we analyse current machine learning models and other in-silico tools as applied to medicine—specifi-cally, to cancer research—and we discuss their interpretability, performance and the input data they are fed with. Artificial neural networks (ANN), logistic regression (LR) and support vector machines (SVM) have been observed to be the preferred models. In addition, convolutional neural networks (CNNs), supported by the rapid development of graphic processing units (GPUs) and high-performance computing (HPC) infrastructures, are gaining importance when image processing is feasible. However, the interpretability of machine learning predictions so that doctors can understand them, trust them and gain useful insights for the clinical practice is still rarely consid-ered, which is a factor that needs to be improved to enhance doctors’ predictive capacity and achieve individualised therapies in the near future

Les services culturels récréatifs et éducatifs des zones humides en Méditerranée : des services sous-estimés malgré les avantages qu'ils procurent, résultats d'études en Méditerranée

Cette synthèse donne un aperçu des principaux résultats et analyses des études conduites en France et au Maghreb entre 2012 et 2014 sur les services culturels récréatifs et éducatifs que procurent les zones humides méditerranéennes. L'Observatoire des zones humides méditerranéennes (OZHM), géré par la Tour du Valat dans le cadre de l'initiative méditerranéenne de Ramsar (MedWet), en a coordonné le travail. En partenariat avec l'Institut Agronomique Méditerranéen de Montpellier (IAMM), la synthèse des neuf sites étudiés a été réalisée en 2015, ce qui a permis une première analyse régionale et sous-régionale. Ce travail a aussi pour vocation d'établir un premier état de référence qualitatif utile pour les suivis ultérieurs. Ce travail n'aurait pas pu être réalisé sans la participation active des gestionnaires et associations des neufs sites étudiés et des institutions publiques qui ont facilité le travail: la Direction générale des Forêts en Algérie et en Tunisie et le Haut Commissariat des Eaux et Forêts et de la Lutte contre la Désertification au Maroc