Efficacy and safety of luspatercept versus epoetin alfa in erythropoiesis-stimulating agent-naive, transfusion-dependent, lower-risk myelodysplastic syndromes (COMMANDS): interim analysis of a phase 3, open-label, randomised controlled trial.

BACKGROUND: Erythropoiesis-stimulating agents (ESAs) are the standard-of-care treatment for anaemia in most patients with lower-risk myelodysplastic syndromes but responses are limited and transient. Luspatercept promotes late-stage erythroid maturation and has shown durable clinical efficacy in patients with lower-risk myelodysplastic syndromes. In this study, we report the results of a prespecified interim analysis of luspatercept versus epoetin alfa for the treatment of anaemia due to lower-risk myelodysplastic syndromes in the phase 3 COMMANDS trial. METHODS: The phase 3, open-label, randomised controlled COMMANDS trial is being conducted at 142 sites in 26 countries. Eligible patients were aged 18 years or older, had a diagnosis of myelodysplastic syndromes of very low risk, low risk, or intermediate risk (per the Revised International Prognostic Scoring System), were ESA-naive, and required red blood cell transfusions (2-6 packed red blood cell units per 8 weeks for ≥8 weeks immediately before randomisation). Integrated response technology was used to randomly assign patients (1:1, block size 4) to luspatercept or epoetin alfa, stratified by baseline red blood cell transfusion burden (<4 units per 8 weeks vs ≥4 units per 8 weeks), endogenous serum erythropoietin concentration (≤200 U/L vs >200 to <500 U/L), and ring sideroblast status (positive vs negative). Luspatercept was administered subcutaneously once every 3 weeks starting at 1·0 mg/kg body weight with possible titration up to 1·75 mg/kg. Epoetin alfa was administered subcutaneously once a week starting at 450 IU/kg body weight with possible titration up to 1050 IU/kg (maximum permitted total dose of 80 000 IU). The primary endpoint was red blood cell transfusion independence for at least 12 weeks with a concurrent mean haemoglobin increase of at least 1·5 g/dL (weeks 1-24), assessed in the intention-to-treat population. Safety was assessed in patients who received at least one dose of study treatment. The COMMANDS trial was registered with ClinicalTrials.gov, NCT03682536 (active, not recruiting). FINDINGS: Between Jan 2, 2019 and Aug 31, 2022, 356 patients were randomly assigned to receive luspatercept (178 patients) or epoetin alfa (178 patients), comprising 198 (56%) men and 158 (44%) women (median age 74 years [IQR 69-80]). The interim efficacy analysis was done for 301 patients (147 in the luspatercept group and 154 in the epoetin alfa group) who completed 24 weeks of treatment or discontinued earlier. 86 (59%) of 147 patients in the luspatercept group and 48 (31%) of 154 patients in the epoetin alfa group reached the primary endpoint (common risk difference on response rate 26·6; 95% CI 15·8-37·4; p<0·0001). Median treatment exposure was longer for patients receiving luspatercept (42 weeks [IQR 20-73]) versus epoetin alfa (27 weeks [19-55]). The most frequently reported grade 3 or 4 treatment-emergent adverse events with luspatercept (≥3% patients) were hypertension, anaemia, dyspnoea, neutropenia, thrombocytopenia, pneumonia, COVID-19, myelodysplastic syndromes, and syncope; and with epoetin alfa were anaemia, pneumonia, neutropenia, hypertension, iron overload, COVID-19 pneumonia, and myelodysplastic syndromes. The most common suspected treatment-related adverse events in the luspatercept group (≥3% patients, with the most common event occurring in 5% patients) were fatigue, asthenia, nausea, dyspnoea, hypertension, and headache; and none (≥3% patients) in the epoetin alfa group. One death after diagnosis of acute myeloid leukaemia was considered to be related to luspatercept treatment (44 days on treatment). INTERPRETATION: In this interim analysis, luspatercept improved the rate at which red blood cell transfusion independence and increased haemoglobin were achieved compared with epoetin alfa in ESA-naive patients with lower-risk myelodysplastic syndromes. Long-term follow-up and additional data will be needed to confirm these results and further refine findings in other subgroups of patients with lower-risk myelodysplastic syndromes, including non-mutated SF3B1 or ring sideroblast-negative subgroups. FUNDING: Celgene and Acceleron Pharma.

Blended learning in undergraduate dental education: a global pilot study.

AIMS: To explore the global trends in blended learning in undergraduate dental education during the COVID pandemic and during the recovery phase by engaging with the students and faculty and evaluate the implications for dental education in the post-COVID era. METHODS: It was a pilot cross-sectional study which employed a convenience sampling technique to recruit representatives of dental faculty and undergraduate students in 80 dental institutions globally. A previously validated questionnaire consisting of a combination of closed and open-ended items was used for data collection. Responses to these online questionnaires were processed and analysed using the R statistical computing environment. RESULTS: A total of 320 dental students and 169 faculty members from 47 different dental institutions participated in the study. Video and Live Online Tutorials were considered to be the most effective method of online learning followed by online question banks by both groups. Significant differences were noted between faculty and students regarding time spent and effectiveness of online teaching and learning, respectively, both before and after the start of COVID. The results highlight the faculty need to engage more closely with the students to address their learning needs. Finally, the participants provided several recommendations regarding the future development of teaching and learning strategies as well as assessments in the post-pandemic era. CONCLUSIONS: This is the first study which explores blended learning in dental education with participants from multiple institutions in different regions of the globe. Compared to the faculty, students considered online learning to be less interactive and preferred learning activities and all assessments to be delivered face-to-face. The results underscore the need to adapt teaching practices to suit the learning needs of the students.

Data-driven analysis and predictive modeling on COVID-19.

The coronavirus (COVID-19) started in China in 2019, has spread rapidly in every single country and has spread in millions of cases worldwide. This paper presents a proposed approach that involves identifying the relative impact of COVID-19 on a specific gender, the mortality rate in specific age, investigating different safety measures adopted by each country and their impact on the virus growth rate. Our study proposes data-driven analysis and prediction modeling by investigating three aspects of the pandemic (gender of patients, global growth rate, and social distancing). Several machine learning and ensemble models have been used and compared to obtain the best accuracy. Experiments have been demonstrated on three large public datasets. The motivation of this study is to propose an analytical machine learning based model to explore three significant aspects of COVID-19 pandemic as gender, global growth rate, and social distancing. The proposed analytical model includes classic classifiers, distinctive ensemble methods such as bagging, feature based ensemble, voting and stacking. The results show a superior prediction performance comparing with the related approaches.

An Empirical Study of Factors Influencing the Perceived Usefulness and Effectiveness of Integrating E-Learning Systems during the COVID-19 Pandemic Using SEM and ML: A Case Study in Jordan

The purpose of this research paper is to identify and test the factors influencing the perceived usefulness and perceived effectiveness of adopting an e-learning system from the perspective of teachers in public and private schools as well as the United Nations Relief and Works Agency for Palestinian Refugees in the Near East (UNRWA) in Jordan during the first wave of the COVID-19 pandemic in the academic year 2019/2020. Based on the findings and best practices, the study intends to make appropriate recommendations to decision-makers. Its significance stems from the use of scientific tools of research and investigation, and it aims to ensure the quality and effectiveness of Jordanian schools' e-learning systems. The study's hypotheses were verified by electronically collecting 551 questionnaires from teachers in Jordan. To test the study hypotheses, the empirical validity of the research model was set up, and the data were analyzed with SPSS version 21.0. Structural equation modeling (SEM), confirmatory factor analysis (CFA), and machine learning (ML) methods were used to test the study hypotheses and validate the properties of the instrument items. Nineteen variables and one mediating variable were studied. The study found that independent variables pertaining to technology (relative advantage, compatibility, top management support, communication technologies, competitive pressure, technology competence, information intensity, and work flexibility) and moderating variables pertaining to the teacher's personal income and those pertaining to school (school size, education program, and work sector) had a positive effect on teachers' perceived usefulness of adopting e-learning systems during the COVID-19 pandemic. On the other hand, independent variables pertaining to technology (complexity and collaboration technology), moderating variables pertaining to the teacher (age, education level, and gender), and moderating variables pertaining to school (educational stage, number of students) were not supported.

Cognitive Learning Style Detection in e-Learning Environments using Artificial Neural Network

COVID-19 pandemic has impacted all aspects of our lives including learning. With the particular growth of e-learning, teaching approaches are being implemented at a distance on online platforms due to this pandemic. In this context, to make student involved throughout the online course, it is recommended to create an efficient platform similar to the traditional learning mode. In this study, we aims to improve learning style detection process by exploring additional such as cognitive traits. In fact, we have proposed novel approach based on Artificial neural network that classify students according to their level of cognitive learning styles in real-time. The proposed automated approach will certainly provide tutors with exhaustive information that helps them in achieving an improved and innovative online learning method. The results obtained are quite interesting and demonstrate the relevance of our solution. © 2022, International Journal of Emerging Technologies in Learning. All Rights Reserved.

Predictors for E-Government Adoption of SANAD App Services Integrating UTAUT, TPB, TAM, Trust, and Perceived Risk.

Using mobile applications in e-government for the purpose of health protection is a new idea during COVID-19 epidemic. Hence, the goal of this study is to examine the various factors that influence the use of SANAD App As a health protection tool. The factors were adopted from well-established models like UTAUT, TAM, and extended PBT. Using survey data from 442 SANAD App from Jordan, the model was empirically validated using AMOS 20 confirmatory factor analysis, structural equation modeling (SEM) and machine learning (ML) methods were performed to assess the study hypotheses. The ML methods used are ANN, SMO, the bagging reduced error pruning tree (RepTree), and random forest. The results suggested several key findings: the respondents' performance expectancy, effort expectancy, social influence, facilitating conditions, perceived risk, trust, and perceived service quality of this digital technology were significant antecedents for their attitude to using it. The strength of these relationships is affected by the moderating variables, including age, gender, educational level, and internet experience on behavioral intention. Yet, perceived risk did not have a significant effect on attitude towards SANAD App The study adds to literature by empirically testing and theorizing the effects of SANAD App on public health protection.

Quality, Effectiveness and Outcome of Blended Learning in Dental Education during the COVID Pandemic: Prospects of a Post-Pandemic Implementation

Blended learning is growing in popularity particularly following the emergence of COVID-19 pandemic. One of the fields that the pandemic has substantially affected is dental education. Purpose: The aim of this study was to evaluate the quality and effectiveness of the online dental education. Students’perceptions and experiences of blended learning were also investigated. Methods: A 28-question online survey was designed to gauge students’perceptions of the effect of blended learning on their academic performance. Results: 314 participants in preclinical and clinical years completed the questionnaire (223 females and 91 males). The majority of students (89%) believed that clinical and practical courses cannot be given by the internet. In terms of students’opinion in the assessment process, more females (65.8%) preferred traditional exams than males (50.5%) (p < 0.05). Most clinical students (83%) preferred a combination of online and traditional teaching compared to 72% of preclinical students (p < 0.05). Clinical year students were more willing to communicate electronically with their classmates and instructors. The majority of dental students (65%) reported that future dental courses should be blended. Conclusions: In the pandemic era, blended learning, should become the preferred method of education whereby theoretical knowledge is delivered through online tutorials and clinical training is resumed on-site, to ensure competency of dental graduates while maintaining safety of the dental team. Current facilities and course designs should be improved in order to improve students’experiences with blended learning.