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Współczesna terapia autoimmunizacyjnej choroby ucha wewnętrznego

100%
Narożny W., Kocić I.
Otorynolaryngologia - przegląd kliniczny
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2017
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vol. 16
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issue 1
1-8
EN
The authors have presented an overview of the latest literature on autoimmune inner ear disease (AIED), presenting, among other things, epidemiology, clinical picture, contemporary laboratory diagnostics, methods of recognition and treatment of this disease. Particular attention has been paid by the authors to the increasing possibilities of the contemporary therapy of AIED, including among others, multidirectional pharmacotherapy (steroids, immunosupressive drugs, anti-cytokine drugs) and plasmapheresis.
PL
Autorzy dokonali przeglądu najnowszego piśmiennictwa dotyczącego autoimmunizacyjnej choroby ucha wewnętrznego (AIED), przedstawiając m.in. epidemiologię, obraz kliniczny, współczesną diagnostykę laboratoryjną, sposoby rozpoznawania i leczenia tej choroby. Szczególną uwagę autorzy poświęcili wzrastającym możliwościom współczesnej terapii AIED, w tym m.in. wielokierunkowej farmakoterapii (sterydy, leki immunosupresyjne, blokery cytokin) oraz plazmaferezy.
2
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How to teach pharmacology to medical students during the COVID-19 pandemic? Students’ perceptions of novel, online forms of teaching

81%
Pawłowska I. J., Pawłowski L., Krzyżaniak N., Kocić I.
European Journal of Translational and Clinical Medicine
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2022
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vol. 5
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issue 1
33-39
EN
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.
3
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Beyond clinical care: the pharmacoeconomic and research roles of pharmacists in Poland

81%
Pawłowska I., Krzyzaniak N., Pawłowski L., Kocić I.
Acta Poloniae Pharmaceutica - Drug Research
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2019
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vol. 76
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issue 2
381-385
4
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Deep learning in pharmacology: opportunities and threats

71%
Kocić I., Kocić M., Rusiecka I., Kocić A., Kocić E.
European Journal of Translational and Clinical Medicine
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2021
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vol. 5
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issue 2
88-94
EN
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.
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