Universidad Internacional del Ecuador
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HATAY-ALTINÖZÜ İLÇESİNDE ZEYTİN ÜRETİCİLERİNİN GİRDİ KULLANIM DÜZEYİ VE TARIMSAL KREDİ İHTİYAÇLARININ BELİRLENMESİ
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Development of molnupiravir and peramivir loaded liposome formulations for combined antiviral therapy
No full textThe pandemic caused by the SARS-CoV-2 virus has led scientists to intensify research on antiviral drugs and vaccines. As a result of these studies, it was observed that molnupiravir (MLP) and peramivir (PRV) could be used against pandemic. MLP affects SARS-CoV-2 replication, but it necessitates high doses, which can cause adverse effects in patients. PRV is a neuraminidase inhibitor, but the bioavailability of the drug after oral administration is very low. In this study, MLP-, PRV-loaded and combined liposome (COMB-Lipo) formulations were prepared via the thin film hydration method. Phospholipon 90 G-based formulations exhibited the most favorable characteristics, with a particle size of 111-145 nm, a polydispersity index (PDI) of less than 0.4, and a zeta potential (ZP) of 6-12 mV). Cell culture studies demonstrated that developed stable formulations are nontoxic to L929 and Vero E6 cells. Antiviral activity assessments against SARS-CoV-2 suggested the effectiveness of liposomes in inhibiting viral activity. These findings demonstrate that a possible synergistic effect of the newly developed sustained-release COMB-Lipo formulation is suggested with the complementary antiviral mechanisms of the combined agents. As a result, the therapeutic potential of co-delivery of anti-SARS-CoV-2 drugs for pulmonary application is considered a promising approach for long-acting treatment of COVID-19
Artificial intelligence literacy, lifelong learning, and fear of innovation: Identification of profiles and relationships
No full textThe study aims to identify the profiles of university students regarding Artificial Intelligence Literacy, Lifelong Learning, and Fear of Innovation using cluster analysis and to examine the relationships among these variables. Cluster analysis and structural equation modeling were conducted with valid responses from 402 university students. The cluster analysis identified three distinct student profiles: the highly adaptive group (Profile 1), the needs improvement group (Profile 2), and the high support required group (Profile 3). Structural equation modeling revealed that artificial intelligence literacy positively affects the tendency for lifelong learning and negatively impacts the fear of innovation. Lifelong Learning Trends also negatively influences the fear of innovation. Furthermore, artificial intelligence literacy was found to indirectly reduce fear of innovation through lifelong learning tendencies. The findings from cluster analysis and structural equation modeling provide significant insights into understanding university students' artificial intelligence literacy, lifelong learning tendencies, and fear of innovation. Developing customized education and support programs tailored to each profile's characteristics and the relationships among the study variables can help students enhance their competencies in these areas
