Safety and efficacy of zilucoplan in patients with generalised myasthenia gravis (RAISE): a randomised, double-blind, placebo-controlled, phase 3 study.

BACKGROUND: Generalised myasthenia gravis is a chronic, unpredictable, and debilitating rare disease, often accompanied by high treatment burden and with an unmet need for more efficacious and well tolerated treatments. Zilucoplan is a subcutaneous, self-administered macrocyclic peptide complement C5 inhibitor. We aimed to assess safety, efficacy, and tolerability of zilucoplan in patients with acetylcholine receptor autoantibody (AChR)-positive generalised myasthenia gravis. METHODS: RAISE was a randomised, double-blind, placebo-controlled, phase 3 trial that was done at 75 sites in Europe, Japan, and North America. We enrolled patients (aged 18-74 years) with AChR-positive generalised myasthenia gravis (Myasthenia Gravis Foundation of America disease class II-IV), a myasthenia gravis activities of daily living (MG-ADL) score of least 6, and a quantitative myasthenia gravis score of at least 12. Participants were randomly assigned (1:1) to receive subcutaneous zilucoplan 0·3 mg/kg once daily by self-injection, or matched placebo, for 12 weeks. The primary efficacy endpoint was change from baseline to week 12 in MG-ADL score in the modified intention-to-treat population (all randomly assigned patients who received at least one dose of study drug and had at least one post-dosing MG-ADL score). Safety was mainly assessed by the incidence of treatment-emergent adverse events (TEAEs) in all patients who had received at least one dose of zilucoplan or placebo. This trial is registered at ClinicalTrials.gov, NCT04115293. An open-label extension study is ongoing (NCT04225871). FINDINGS: Between Sept 17, 2019, and Sept 10, 2021, 239 patients were screened for the study, of whom 174 (73%) were eligible. 86 (49%) patients were randomly assigned to zilucoplan 0·3 mg/kg and 88 (51%) were assigned to placebo. Patients assigned to zilucoplan showed a greater reduction in MG-ADL score from baseline to week 12, compared with those assigned to placebo (least squares mean change -4·39 [95% CI -5·28 to -3·50] vs -2·30 [-3·17 to -1·43]; least squares mean difference -2·09 [-3·24 to -0·95]; p=0·0004). TEAEs occurred in 66 (77%) patients in the zilucoplan group and in 62 (70%) patients in the placebo group. The most common TEAE was injection-site bruising (n=14 [16%] in the zilucoplan group and n=8 [9%] in the placebo group). Incidences of serious TEAEs and serious infections were similar in both groups. One patient died in each group; neither death (COVID-19 [zilucoplan] and cerebral haemorrhage [placebo]) was considered related to the study drug. INTERPRETATION: Zilucoplan treatment showed rapid and clinically meaningful improvements in myasthenia gravis-specific efficacy outcomes, had a favourable safety profile, and was well tolerated, with no major safety findings. Zilucoplan is a new potential treatment option for a broad population of patients with AChR-positive generalised myasthenia gravis. The long-term safety and efficacy of zilucoplan is being assessed in an ongoing open-label extension study. FUNDING: UCB Pharma.

Assessing the Impact of Traffic Emissions on Fine Particulate Matter and Carbon Monoxide Levels in Hanoi through COVID-19 Social Distancing Periods

Hanoi, Vietnam, is usually ranked as one of the most polluted capital cities in terms of air quality, particularly PM2.5. However, there has not been enough data to determine the main source of this pollution. In this study, we utilized the rare opportunity of the COVID-19 social distancing to assess the contribution of traffic emission to PM2.5 and CO levels when traffic volume was reduced significantly in Hanoi. Hourly PM2.5 and CO concentrations were measured from nine urban and traffic monitoring stations during pre-, soft, hard, and post-social distancing periods. As a result, we observed large reductions in both PM2.5 and CO levels during social distancing periods. PM2.5 concentrations were 14–18% lower during the social distancing than before this period, while CO concentrations had a more considerable drop by 28–41%. It is known that meteorological conditions can have significant effects on the ambient levels of air pollutants. To overcome this challenge, weather normalized concentrations of those pollutants were estimated using the random forest model, a machine learning technique. The normalized weather concentrations showed smaller reductions by 7–10% for PM2.5 and 5–11% for CO, indicating the presence of favorable weather conditions for better air quality during the social distancing period. In further analysis, the apparent improvement of air quality in Hanoi during the social distancing period was in line with reducing traffic emissions while emissions from coal-fired power plants remained relatively stable.

How Does Environmental Interpretation Affect Psychological Well-Being? A Study Conducted in the Context of COVID-19

The COVID-19 crisis has challenged and generated severe impact on the global society, economy, and environment. Under this pandemic context, governments and organizations around the world have issued and strengthened environmental policies and regulations to protect the environment and human health. However, the extant knowledge about how people’s interpretation of environmental policies and regulations influence their psychological well-being in the context of the COVID-19 pandemic is still limited. This study, therefore, investigates the impact of environmental interpretation on psychological well-being with the mediating role of environmentally responsible behavior and the moderating role of psychological contract violation. Using the data from a large sample of 960 residents in China, results of structural equation modeling show a positive relationship between environmental interpretation and psychological well-being, and this relationship is mediated by environmentally responsible behavior. Notably, psychological contract violation has a moderating effect on the indirect effect of environmental interpretation on psychological well-being via environmentally responsible behavior. These findings have several important implications for policymakers in environmental sustainability and pandemic planning.

Abrupt but smaller than expected changes in surface air quality attributable to COVID-19 lockdowns.

The COVID-19 lockdowns led to major reductions in air pollutant emissions. Here, we quantitatively evaluate changes in ambient NO2, O3, and PM2.5 concentrations arising from these emission changes in 11 cities globally by applying a deweathering machine learning technique. Sudden decreases in deweathered NO2 concentrations and increases in O3 were observed in almost all cities. However, the decline in NO2 concentrations attributable to the lockdowns was not as large as expected, at reductions of 10 to 50%. Accordingly, O3 increased by 2 to 30% (except for London), the total gaseous oxidant (O x = NO2 + O3) showed limited change, and PM2.5 concentrations decreased in most cities studied but increased in London and Paris. Our results demonstrate the need for a sophisticated analysis to quantify air quality impacts of interventions and indicate that true air quality improvements were notably more limited than some earlier reports or observational data suggested.

The Factors Affecting Acceptance of E-Learning: A Machine Learning Algorithm Approach

The Covid-19 epidemic is affecting all areas of life, including the training activities of universities around the world. Therefore, the online learning method is an effective method in the present time and is used by many universities. However, not all training institutions have sufficient conditions, resources, and experience to carry out online learning, especially in under-resourced developing countries. Therefore, the construction of traditional courses (face to face), e-learning, or blended learning in limited conditions that still meet the needs of students is a problem faced by many universities today. To solve this problem, we propose a method of evaluating the influence of these factors on the e-learning system. From there, it is a matter of clarifying the importance and prioritizing construction investment for each factor based on the K-means clustering algorithm, using the data of students who have been participating in the system. At the same time, we propose a model to support students to choose one of the learning methods, such as traditional, e-learning or blended learning, which is suitable for their skills and abilities. The data classification method with the algorithms multilayer perceptron (MP), random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM) and naïve bayes (NB) is applied to find the model fit. The experiment was conducted on 679 data samples collected from 303 students studying at the Academy of Journalism and Communication (AJC), Vietnam. With our proposed method, the results are obtained from experimentation for the different effects of infrastructure, teachers, and courses, also as features of these factors. At the same time, the accuracy of the prediction results which help students to choose an appropriate learning method is up to 81.52%.