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    Wyświetlanie 1-3 z 3
    Tytuł:
    Beyond clinical care: the pharmacoeconomic and research roles of pharmacists in Poland
    Autorzy:
    Pawłowska, Iga
    Krzyzaniak, Natalia
    Pawłowski, Leszek
    Kocić, Ivan
    Powiązania:
    https://bibliotekanauki.pl/articles/895697.pdf
    Data publikacji:
    2019-04-30
    Wydawca:
    Polskie Towarzystwo Farmaceutyczne
    Źródło:
    Acta Poloniae Pharmaceutica - Drug Research; 2019, 76, 2; 381-385
    0001-6837
    2353-5288
    Pojawia się w:
    Acta Poloniae Pharmaceutica - Drug Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    How to teach pharmacology to medical students during the COVID-19 pandemic? Students’ perceptions of novel, online forms of teaching
    Autorzy:
    Pawłowska, Iga J.
    Pawłowski, Leszek
    Krzyżaniak, Natalia
    Kocić, Ivan
    Powiązania:
    https://bibliotekanauki.pl/articles/2203185.pdf
    Data publikacji:
    2022-05-31
    Wydawca:
    Gdański Uniwersytet Medyczny
    Tematy:
    Undergraduate
    COVID-19 pandemic
    Medical education
    Online teaching
    Pharmacology
    Opis:
    BackgroundThe COVID-19 pandemic has forced the introduction of many changes into medical student education. The aim of the study was to evaluate medical students’ perceptions of a Pharmacology course delivered at a Polish medical university before and during the pandemic.Material and methodsA cross-sectional anonymous survey conducted among medical students.Results90 out 122 students participated in the study. The vast majority of students found pharmacology to be a difficult subject. The surveyed group of students preferred active methods of learning, including: teacher explanations (86.5%) and discussions (70.8%) during in-person classes, real-time student-teacher meetings via dedicated web-based platforms (73%) during online classes. Students most often described e-learning as interesting (58.9%) and timesaving (52.2%). Less than 30% described it as stressful, difficult, time-consuming and boring. The most commonly reported advantage was the possibility for students to adjust their pharmacology study-time to a more personalised schedule (82.5%). The main disadvantage included the loss of in-person face-to-face contact with the teacher (61.8%).ConclusionsOverall, students held positive attitudes towards the new teaching format and adapted well to the new conditions. Modern innovations enabling medical students to continue their studies efficiently and effectively during the pandemic must be developed and introduced into practice.
    Źródło:
    European Journal of Translational and Clinical Medicine; 2022, 5, 1; 33-39
    2657-3148
    2657-3156
    Pojawia się w:
    European Journal of Translational and Clinical Medicine
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Deep learning in pharmacology: opportunities and threats
    Autorzy:
    Kocić, Ivan
    Kocić, Milan
    Rusiecka, Izabela
    Kocić, Adam
    Kocić, Eliza
    Powiązania:
    https://bibliotekanauki.pl/articles/25728738.pdf
    Data publikacji:
    2022-09-06
    Wydawca:
    Gdański Uniwersytet Medyczny
    Tematy:
    machine learning
    pharmacology
    deep learning
    artificial intelligence
    drug research and development
    Opis:
    Introduction This review aims to present briefly the new horizon opened to pharmacology by the deep learning (DL) technology, but also to underline the most important threats and limitations of this method. Material and Methods We searched multiple databases for articles published before May 2021 according to the preferred reported item related to deep learning and drug research. Out of the 267 articles retrieved, we included 50 in the final review. Results DL and other different types of artificial intelligence have recently entered all spheres of science, taking an increasingly central position in the decision-making processes, also in pharmacology. Hence, there is a need for better understanding of these technologies. The basic differences between AI (artificial intelligence), DL and ML (machine learning) are explained. Additionally, the authors try to highlight the role of deep learning methods in drug research and development as well as in improving the safety of pharmacotherapy. Finally, future directions of DL in pharmacology were outlined as well as possible misuses of it. Conclusions DL is a promising and powerful tool for comprehensive analysis of big data related to all fields of pharmacology, however it has to be used carefully.
    Źródło:
    European Journal of Translational and Clinical Medicine; 2022, 5, 2; 88-94
    2657-3148
    2657-3156
    Pojawia się w:
    European Journal of Translational and Clinical Medicine
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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