3 research outputs found
μλμμ λμμΌλ‘ ν λμ§νΈ κΈ°λ° ν΄λΆν κ΅μ‘κ³Όμ κ°λ°κ³Ό κ΅μ‘ν¨κ³Όμ κ΄ν μ°κ΅¬
νμλ
Όλ¬Έ(λ°μ¬) -- μμΈλνκ΅λνμ : μκ³Όλν μνκ³Ό, 2023. 2. μ λν.μ ν΅μ μΈ μΉ΄λ°λ° ν΄λΆλ λ€μν μ΄μ λ‘ μΈν΄ κΈκ²©νκ² κ°μνμκ³ , μ΅κ·Ό λͺ λ
λμ κΈ°μ λ°μ μΌλ‘ μλ£ κ΅μ‘ λΆμΌμμλ λ€μν λμ§νΈ κΈ°κΈ°μ μννΈμ¨μ΄κ° μμ°λκ³ μλ€. λ³Έ λ
Όλ¬Έμ λμ§νΈ κΈ°μ μ μ μ©ν κ΅μ‘κ³Όμ μ κ°λ°νκ³ λμ§νΈ κΈ°λ° ν΄λΆν κ΅μ‘μ νμ΅ν¨κ³Όμ λ§μ‘±λλ₯Ό μμ보기 μν΄ λ κ°μ§ μ°κ΅¬λ‘ μ§νλμλ€.
첫λ²μ§Έ μ°κ΅¬μμλ 2019λ
μ½λ‘λ λ°μ΄λ¬μ€ λ°μμΌλ‘ μλ£ κ΅μ‘κ³Ό μλ£ μμ€ν
μ΄ μ½νλμλ€. λ°λΌμ λ³Έ μ°κ΅¬λ μ¨λΌμΈ μμ
μ λμ
κ³Ό 3μ°¨μ ν΄λΆν μ΄ν리μΌμ΄μ
μ ν΅ν μμ λ μΌμ μ΄ νμλ€μ νμ
μ±μ·¨λμ λ§μ‘±λμ λ―ΈμΉλ μν₯μ λΆμνμλ€. ν΄λΆν κ΅μ‘μ μ½λ‘λ19 λ²μ νμΌλ‘ μΈν΄ 3κ°μ νμλ¨μ(μν, λͺΈν΅, 머리μ λͺ©)λ‘ λλμλ€. μ¨λΌμΈ κ°μλ₯Ό μ μΈν μΉ΄λ°λ° ν΄λΆμ νκΈ° λ° μ€κΈ°μνμ κ°κ° 50μ¬λͺ
μ© 3κ°μ λ°μΌλ‘ λλμ΄ μ§νλλ€. λν, νμλ€μ νμ
μ±μ·¨λλ₯Ό 3κ°μ νμ λ¨μμμ νκΈ°μνκ³Ό μ€κΈ°μνμ ν΅νμ¬ νκ°νμκ³ , μμ λ ν΄λΆν μΌμ μ λν μ€λ¬Έμ§λ₯Ό μμ±νμλ€. νκΈ°μνκ³Ό μ€κΈ°μν μ μλ λλΆλΆ 2019λ
μ λΉν΄ 2020λ
μ ν¬κ² λ¨μ΄μ‘λ€. λ€λ§, κ°μν΄λΆν μ΄ν리μΌμ΄μ
μ νμ©ν λͺΈν΅ μΈμ
μμλ 2020λ
μ€κΈ°μν μ μκ° 2019λ
λ³΄λ€ μλ±ν λμλ€. 70% μ΄μ(νλ€λ¦¬μ λͺΈν΅ μΈμ
)κ³Ό 53% (머리μ λͺ© μΈμ
) νμλ€μ΄ λλ©΄ μ€μ΅μμ ν΄λΆνμ 곡λΆνλ λ° ν° μ΄λ €μμ΄ μλ€κ³ λ³΄κ³ νλ€. λν, 50% μ΄μμ νμλ€μ΄ λͺ¨λ μΈμ
μμ μ΄ν리μΌμ΄μ
μ μλΉν λμμ λ°μλ€.
λλ²μ§Έ μ°κ΅¬μμ μ€λλ μ λͺ¨λ μν λΆμΌλ λμ§νΈ μ νμ μν₯μ ν¬κ² λ°λλ€. λ³Έ μ°κ΅¬λ μνκ΅μ‘μμ λμ§νΈ μλμ ν΅ν© νμμ±μ μ€λͺ
νκ³ , νλΆ κ΅μ‘μμ μ΄λ¬ν μλμ ꡬνμ΄ μ΄λ»κ² μ΄λ£¨μ΄μ§ μ μλμ§ λμ§νΈ κΈ°λ° ν΄λΆν κ΅μ‘ 컀리νλΌμ μ μνλ€. μ΄ μ°κ΅¬λ κ΅μ°¨ 무μμ λμ‘° μνμ΄μλ€. μΈμ²΄ν΄λΆνκ³Ό μ κ²½ν΄λΆν μ€μ΅μ 3λΆλ° (Aλ°, Bλ°, Cλ°)μΌλ‘, 1νλ
μλμμ κ°μ ν΄λΆ μ§λ¨ (κ°μ ν΄λΆ --> μΉ΄λ°λ° ν΄λΆ)κ³Ό μΉ΄λ°λ° ν΄λΆ μ§λ¨ (μΉ΄λ°λ° ν΄λΆ --> κ°μ ν΄λΆ)μΌλ‘ 무μμ λΆλ₯λμλ€. κ°μ ν΄λΆμ€μ΅μ ν€λλ§μ΄ν°λ λμ€νλ μ΄, νλΈλ¦Ώ, μ€λ¬Ό ν¬κΈ°μ ν°μΉ μ€ν¬λ¦°μ μ¬μ©νλ€. ν΄μ¦ 1μ 첫λ²μ§Έ κ°μ ν΄λΆμ€μ΅ λλ μΉ΄λ°λ° ν΄λΆμ€μ΅ νμ ν΄λΆν μ§μμ λΉκ΅νκΈ° μν΄ μ§νλμλ€. ν΄μ¦ 2μ μ€λ¬Έμ‘°μ¬λ λͺ¨λ κ°μν΄λΆμ€μ΅κ³Ό μΉ΄λ°λ° ν΄λΆμ€μ΅μ΄ λλ λ μνλμλ€. μΈμ²΄ν΄λΆν μ€μ΅μ κ²½μ°, ν΄μ¦1μ νκ· μ΄μ μμλ μ μλ―Έν μ°¨μ΄κ° μμλ€. κ·Έλ¬λ, Cλ°μμλ κ°μ ν΄λΆ κ΅μ‘μ΄ μΉ΄λ°λ° κ΅μ‘λ³΄λ€ μλ±ν λμ νμ
μ±μ·¨λλ₯Ό 보μλ€. λμ§νΈ κΈ°κΈ°λ€ μ€μμ, λλΆλΆμ νμλ€μ νλΈλ¦Ώ κΈ°λ° νμ΅μ΄ ν¨κ³Όμ μΈ νμ΅ λ°©λ²μ΄λΌκ³ μκ°νλ€. μ κ²½ν΄λΆν μ€μ΅μμλ κ°μ ν΄λΆ κ΅μ‘μ΄ μΉ΄λ°λ° κ΅μ‘λ³΄λ€ ν΅κ³μ μΌλ‘ μ μνκ² λμ νμ
μ±μ·¨λλ₯Ό 보μ¬μ£Όμλ€. λλΆλΆμ νμλ€μ 3μ°¨μ λμ§νΈ κΈ°λ° νμ΅μ΄ μ체 ν΄λΆνμ λν μ΄ν΄λ₯Ό ν₯μμμΌ°λ€κ³ λ³΄κ³ νκ³ , λμ§νΈ μ€μ΅ κΈ°κΈ°λ₯Ό ν΅ν κ°μ ν΄λΆν μ€μ΅ κ²½νμ κ°μ₯ λ§μ‘±νλ€.
λ³Έ μ°κ΅¬λ μνκ΅μ‘μμ λμ§νΈ κΈ°λ° ν΄λΆν κ΅μ‘μ κ°λ₯μ±μ 보μ¬μ£Όκ³ λμ§νΈ κΈ°λ° ν΄λΆν κ΅μ‘μ μ ν΅μ μΈ μΉ΄λ°λ° κ΅μ‘μ κ°ννλ νμ μ μΈ νμ΅ κ²½νμ μ 곡ν μ μμ κ²μ΄λ€.Traditional cadaver dissection has been drastically reduced for various reasons, and technological advances in recent years have produced a variety of digital devices and software in medical education. This thesis was conducted in two studies to develop curriculums applying digital technologies and compare digital-based anatomy education with traditional anatomy education to find out the learning efficacy and satisfaction.
In the first study, the coronavirus disease 2019 (COVID-19) outbreak weakened medical education and healthcare systems. Therefore, the effect of the modified schedule with the introduction of online classes and a three-dimensional anatomy application on students' academic achievement and satisfaction was analyzed. Anatomy education was divided into three regional units (the upper and lower limbs, trunk, and head and neck) due to COVID-19. The schedule was mixed with simultaneous and rotating schedules. Except for online lectures, cadaver dissections, and written and practical examinations were conducted in three classes of approximately 50 students each. Furthermore, students' performance was assessed using three sets of written and practical examinations, and they completed a questionnaire regarding modified anatomy laboratory schedules. Most of the written and practical examination scores significantly decreased in 2020 compared to 2019. However, in the trunk session that used the virtual anatomy application, the score on the practical examination in 2020 was significantly higher than in 2019. Over 70% (upper and lower limbs and trunk sessions) and 53% (head and neck session) students reported no significant difficulty in the face-to-face anatomy laboratory. In addition, over 50% of students received considerable help with the anatomy application in all sessions.
In the second study, the digital revolution has impacted all medical disciplines. Therefore, the need for digital competencies in medical education and how to incorporate them into undergraduate training using a digital-based anatomy curriculum was addressed. This was a crossover randomized controlled trial. In both Human Anatomy and Neuroanatomy laboratories, there were three classes (class A, B, and C) in the first year of the Department of Medicine, and students were randomized into two groups: the virtual group (virtual dissection --> cadaver dissection) and the cadaver group (cadaver dissection --> virtual dissection). The virtual dissection laboratory was conducted via head-mounted displays, tablets, and a life-sized touchscreen. Quiz 1 (Q1) was tested following the first virtual or cadaver dissections. Quiz 2 (Q2) and a survey were conducted at the end of the final procedure in each training modality. Regarding the Human Anatomy laboratory, there was no significant difference in the Q1 mean total score. However, in class C, virtual education showed significantly higher academic achievement than cadaver education. Most students felt tablet-based learning was an effective study method among the digital lab resources. Regarding the Neuroanatomy laboratory, virtual education showed significantly higher academic achievement in Q1 than cadaver education. Most students reported that digital-based learning enhanced their understanding of cadaveric anatomy. Students were most satisfied with their experiences of virtual anatomy education through digital lab resources.
These studies demonstrate the potential for digital-based anatomy education in medical education. Digital-based anatomy education can provide innovative learning experiences augmenting traditional cadaver education.Chapter 1 The Metaverse: A New Challenge for the Anatomy Education 01
Challenges Facing Anatomy Education 02
Applications of Metaverse in Medical Education 05
The List of Devices for Anatomy Education 08
Mobile Devices 08
Virtual Dissection Tables 09
Head-Mounted Displays 10
Digital Anatomy Applications 13
Contents' Scenarios for Digital Anatomy Education 15
Chapter 2 Exploring Medical Students' Performance and Satisfaction of the Modified Anatomy Schedules and a Digital Software During COVID-19 Pandemic 17
Introduction 18
Study Goals and Questions 20
Materials and Methods 21
Results 28
Discussion 32
Chapter 3 Virtual Anatomy Laboratory Education: A Randomized Controlled Trial Compared to Cadaver Dissection 36
Introduction 37
Study Goals and Questions 39
Materials and Methods 40
Results 48
Discussion 64
Conclusion 69
References 71
Supporting Information 85
Abstract in Korean 94
Acknowledgement 96λ°
μ‘°μ§ ν¬λͺ νλ₯Ό μ΄μ©ν μ₯μ μ¬λμ μ 체 μ₯μ κ²½κ³ 3μ°¨μ μκ°ν λ° μ λν
νμλ
Όλ¬Έ (μμ¬) -- μμΈλνκ΅ λνμ : μκ³Όλν μνκ³Ό, 2021. 2. ν©μμΌ.BACKGROUND & AIMS: Nowadays, state-of-the-art tissue clearing methods enable visualization of the thicker tissue section or even whole organ imaging, by increasing tissue transparency and enhancing the antigen-antibody reaction. The goal of this research was to establish a 3D imaging method for the whole gastrointestinal (GI) tract that yields more information and insight about the enteric nervous system (ENS) than traditional 2D tissue section imaging. This approach will improve a comprehensive understanding of research purpose and diagnosis of human diseases, such as Hirschsprung disease, bowel motility disorders, or inflammatory bowel disease (IBD). The present study optimized a technique to image transgenic fluorescence mice and human ENS that express fluorescent neuron-specific class β
’ beta-tubulin (Tuj1), neuronal nitric oxide synthase (nNOS), choline acetyltransferase (ChAT), and RNA binding proteins (HuC/D) in 3 dimensions.
METHODS: Visualization and quantification of the digestive organs (e.g. esophagus, stomach, small intestine, and colon) in mice and humans were carried out through various techniques. A method, encompassing tissue clearing, immunohistochemistry (IHC), confocal microscopy, light sheet fluorescence microscopy (LSFM), and quantitative analysis of full-thickness bowel without tissue sections, had been established for 3D imaging at high resolution. Furthermore, using surface rendering, volume rendering for all channels, fluorescence thresholding, and background subtraction, tools from in IMARIS, cleared tissues could be visualized in an accurate 3D structure.
RESULTS: The multiscale structural decomposition of mouse and human ENS was clearly visualized in 3D. The tissue clearing method could image the complex ENS network structure of myenteric plexus, submucosal plexus, and mucosal nerves. Similarly, the 3D ENS network structure of the esophagus (16 Γ 14 Γ 5.3 mm) and colon (1.2 Γ 1.3 Γ 1.4 mm) samples were visualized in mouse and human, respectively. I investigated the cholinergic ENS structure through the whole GI tract and quantified the number of cell bodies and cell bodies per ganglion in myenteric and submucosal plexus in mouse (n=3). To identify the hubness of the myenteric plexus in mouse, I measured the number of ganglia and bridges without cell bodies that connected the ganglion. Quantitative data for myenteric plexus and submucosal plexus showed relatively different aspects.
CONCLUSIONS: This study was the first to visualize the mouse and human whole ENS in three dimensions with no sectioning and microanatomy. The cytoarchitecture of the mouse tissues could be quantitatively analyzed, preserving the tissue structure, and providing more accurate data with tissue clearing. A quantitative analysis method of structure phenotypes in mouse will illuminate the potential usefulness of this technology. For GI motility disorders, this novel technology will unravel the extensive spatial 3D network structure of neuro-immune interaction.λ°°κ²½ λ° λͺ©μ : μ€λλ μ μ΅μ²¨λ¨ μμ²΄μ‘°μ§ ν¬λͺ
ν κΈ°μ μ μ‘°μ§ ν¬λͺ
νμ νμ-ν체μ λ°μμ μ΄μ§μν΄μΌλ‘μ¨ λκΊΌμ΄ μ‘°μ§ λλ μ κΈ°κ΄μ μκ°ν ν μ μκ² νλ€. λ³Έ μ°κ΅¬λ μμ₯κ΄μ λν μ‘°μ§μ 2μ°¨μ μ΄λ―Έμ§λ³΄λ€ μ₯μ κ²½κ³μ λν μ 보μ ν΅μ°°λ ₯μ λ λ§μ΄ μ°μΆνλ 3μ°¨μ μμ λ°©λ²μ κ°λ°νλ κ²μ΄λ€. μ΄λ¬ν μ κ·Όλ°©μμ μΌμ¦μ± μ₯μ§νμ΄λ μ₯ μ΄λμ± μ₯μ μ κ°μ μΈκ° μ§λ³μ λν μ°κ΅¬ λͺ©μ κ³Ό μ§λ¨μ λν ν¬κ΄μ μΈ μ΄ν΄λ₯Ό ν₯μμν¬ μ μμΌλ©° λ°λΌμ νμ§μ ν μ₯μ μΈκ°μ μ₯μ κ²½κ³μμ λ² ν β
’ νλΈλ¦° (Tuj1), μ κ²½μ± μ°νμ§μ ν©μ±ν¨μ (nNOS)μ μ½λ¦°μμΈνΈνΈλμ€νΌλΌμμ (ChAT), νκ΄μ κ²½μ RNA κ²°ν© λ¨λ°±μ§ (HuC/D)μ 3μ°¨μμΌλ‘ μκ°ν νλ κΈ°λ²μ μ΅μ ννμλ€.
λ°©λ²: μ₯μ μ¬λμ μνκΈ°κ΄ (μλ, μ, μμ₯, λμ₯)μ μκ°ννκ³ μ λν νκΈ° μν΄ λ€μν κΈ°λ²μΌλ‘ μ°κ΅¬λ₯Ό μννμλ€. κ³ ν΄μλμ 3μ°¨μ μμμ μ»κΈ° μν΄ μ₯μ μ¬λμ μ‘°μ§μ μ¬μ©νμ¬ μ‘°μ§ ν¬λͺ
ν κΈ°λ², λ©΄μννμΌμ (IHC), 곡μ΄μ νλ―Έκ²½, μνΈ νκ΄ νλ―Έκ²½ (LSFM)κ³Ό μ 체 μμ₯κ΄ λκ»μ λν μ λμ λΆμμ μλ°ν λ°©λ²λ€μ΄ κ°λ°λμλ€. λμκ° IMARISμ ν΄μΈ νλ©΄ λ λλ§, λͺ¨λ μ±λμ λν λ³Όλ₯¨ λ λλ§, νκ΄ μκ³μΉ, λ°°κ²½ μ κ±° λ±μ μ¬μ©νμ¬ λ³Όλ₯¨μ μΈ‘μ ν μ μλ μ νν 3D ꡬ쑰λ₯Ό μ»μ μ μμλ€.
κ²°κ³Ό: μ₯μ μ¬λμ μ₯μ κ²½κ³λ λ€μν κ·λͺ¨μ 3μ°¨μμΌλ‘ μκ°ν λμλ€. ν° μ‘°μ§μμ 3μ°¨μμΌλ‘ λ©΄μμΌμκ³Ό μ΄λ―Έμ§μ΄ κ°λ₯ν μ‘°μ§ ν¬λͺ
ν λ°©λ²μ κ·Όμ‘μΈ΅μ κ²½μΌκΈ°, μ λ§νμ κ²½μ΄κ³Ό μ λ§ μ κ²½λ€μ λ€νΈμν¬λ₯Ό 보μ¬μ£Όμλ€. λ§μ°¬κ°μ§λ‘, μ₯μ μλ (16 Γ 14 Γ 5.3 mm) μ μ¬λμ λμ₯ (1.2 Γ 1.3 Γ 1.4 mm) μ‘°μ§ μνμμλ 3μ°¨μ μ₯μ κ²½ λ€νΈμν¬ κ΅¬μ‘°λ₯Ό μ 보μ¬μ£Όμλ€. μ₯(n=3)λ₯Ό μ¬μ©νμ¬ μ₯ μ 체μ λΆν¬νλ ChATμ μ κ²½ ꡬ쑰λ₯Ό μ°κ΅¬νκ³ , λκ°μ μ£Όμ μΈ΅μμ μΈν¬μ²΄μ μ κ²½μ λΉ μΈν¬μ²΄λ₯Ό μ λν νμλ€. κ·Όμ‘μ κ²½μΌκΈ°μ μ°κ²°λλ₯Ό νμΈνκΈ° μν΄ μ κ²½μ μ μμ μ κ²½μ κ³Ό μ κ²½μ μ μ΄λ λ€λ¦¬λ μΈ‘μ νμλ€. κ·Όμ‘μΈ΅μ κ²½μΌκΈ°μ μ λ§νμ κ²½μ΄μ μ λ΅μ λ°μ΄ν°λ λΉκ΅μ μλ‘ λ€λ₯Έ μμμ 보μ¬μ£Όμλ€. λ λμκ° μ₯μμ μ΅μ μ± μ κ²½κ³Ό ν₯λΆμ± μ κ²½μ λ©΄μ μΌμνλλ° μ±κ³΅νμλ€.
κ²°λ‘ : μ΄ μ°κ΅¬λ μ₯μ μ¬λμ μ‘°μ§ ν¬λͺ
ν κΈ°μ μ μ ν©νκ³ μ₯μ κ²½κ³μ ꡬ쑰μ μ κ²½νλ‘ μ°κ΅¬μ μ μ© κ°λ₯μ±μ λμΌ κ²μ΄λ€. νΉν, μ½λ¦° μλμ± λ΄λ°μ μλ₯Ό μ λννλ λ°©λ²μ λν κ²μ¦μ μ 곡νλ€. μ₯μ ꡬ쑰 νννμ μ λμ λΆμ λ°©λ²μ μ§λ¨ λ§μ»€λ‘μ μ΄ κΈ°μ μ μ μ¬μ μΈ μ μ©μ±μ μ‘°λͺ
νκ³ μ₯ μ΄λμ± μ₯μ μ κ²½μ° μ΄ μλ‘μ΄ κΈ°μ μ΄ μ κ²½λ©΄μμ μνΈμμ©μ λν κ΄λ²μν 3μ°¨μ λ€νΈμν¬ κ΅¬μ‘°λ₯Ό λ°ν κ²μ΄λ€.β
°. Abstract
β
³. Contents
β
΄. List of tables
β
΅. List of figures
β
Έ. List of abbreviations
1. Introduction
6. Materials and Methods
11. Results
21. Discussion
26. References
70. Abstract (Korean)Maste