8 research outputs found
3D νλ¦°ν μ μ΄μ©ν νμμ λ°©μΆ μλ ν¨μΉμ μ‘°μ§ μ¬μ ν¨κ³Όμ λν μ°κ΅¬
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Όλ¬Έ(λ°μ¬) -- μμΈλνκ΅λνμ : μκ³Όλν μνκ³Ό, 2021.8. μ μμ¬.Esophageal defects can cause exposure of the fistula site to various bacterial species, which could lead to a severe inflammatory response. We designed and manufactured a 3D-printed patch consisting of a lattice pattern and thin-film with biodegradable polycaprolactone (PCL), that released the antibiotic, tetracycline (TCN). We reconstructed an artificial defect in the rat esophagus using this patch. The efficacy and availability of 3D-printed antibiotic-releasing patches were evaluated using both quantitative and qualitative assessment methods.
PCL was used to print the lattice pattern on a pre-manufactured thin film with approximately 100ΞΌm resolution, which had been mixed with tetracycline (TCN) at 100Β°C and 1000Β°C to release the antibiotic evenly. In vitro tests showed that TCN was released for more than 1 month. In addition, in vitro cytotoxicity test demonstrated excellent cell compatibility. 3D-printed antibiotic-releasing patches were applied on the defect sites after creating artificial partial esophageal defects in rats. Four weeks after the surgery, leakage was checked using micro-computed tomography with an oral contrast agent injected into the rat mouth. No leakage was evident in any part of the esophagus. For analyzing tissue regeneration, immunohistochemistry was performed. M1 and M2 macrophage activation was verified by immunohistochemistry of CD-68 and CD-163. Desmin immunohistochemistry showed significant muscle layer regeneration in the TCN (1% and 3%) patch groups. Moreover, sufficient re-epithelialization and neo-vascularization were affirmed in TCN (1% and 3%) patch groups.
In this study, we confirmed that 3D-printed antibiotic-releasing patches not only have anti-microbial effects but also have tissue regeneration ability in the area surrounding the esophageal fistula site. The results of this study can be applied in further studies on tissue-engineering.μλ κ²°μμ κ²°μλΆμ λν λ€μν μΈκ· μ΄μ λν λ
ΈμΆμ μ λ°νλ©°, μ΄λ μ¬ν μΌμ¦ λ°μμ μ λ°νλ€κ³ μλ €μ Έ μλ€. μ μ λ° μ°κ΅¬μ§μ μλΆν΄μ± polycaprolactone (PCL)μ μ¬μ©νμ¬ 3D νλ¦°ν°λ‘ 격μ λͺ¨μ λ° λ°λ§μΌλ‘ ꡬμ±λ ν¨μΉλ₯Ό μ μνμκ³ , μ¬κΈ°μ νμμ , tetracycline (TCN)μ΄ λ°©μΆλ μ μλλ‘ νμ¬ μΈκ³΅μ μΌλ‘ λ§λ μλ κ²°μμ λν μ¬κ±΄μ μλνμλ€. μ΄ν μ μ±μ , μ λμ λΆμμ ν΅ν΄ ν¨μΉμ νμ©λμ ν¨λ₯μ νκ°νμλ€.
PCLμ 미리 μμ
λ λ°λ§ μμ 100 ΞΌm ν΄μλμ 격μ λͺ¨μμΌλ‘ 3D νλ¦°ν
νμμΌλ©°, 100Β°Cμμ λ
ΉμΈ TCNμ PCL μ
μλ₯Ό νΌν©νμ¬ 3D νλ¦°ν
ν¨μΌλ‘μ¨ TCNμ΄ ν¨μΉμμ λ°©μΆλ μ μλλ‘ νμλ€. 체μΈμ€νμμ TCN μ 1λ¬ μ΄μ μ§μμ μΌλ‘ λ°©μΆλλ κ²μ νμΈν μ μμμΌλ©°, λν μΈν¬λ
μ±μ λν 체μΈμ€νμμ ν¨μΉ μΆμΆλ¬Όμ΄ λ°μ΄λ μΈν¬μ ν©μ±μ 보μμ νμΈν μ μμλ€. μ΄ν μ₯μμ λΆλΆ μλκ²°μμ μ λ°ν ν μ μλ ν¨μΉλ₯Ό μ μ©νμκ³ 3μ£Ό ν micro CTλ₯Ό μ΄μ©ν΄ λμΆ μ¬λΆλ₯Ό νμΈνμλ€. μ₯μ ꡬκ°μ ν΅ν΄ μ‘°μμ λ₯Ό μ£Όμ¬νμ¬ νμΈν κ²°κ³Ό λͺ¨λ ν¨μΉ μ΄μκ΅°μμ μλ μ μ₯μ λμΆμ κ΄μ°°λμ§ μμλ€. μμ ν 4μ£Ό λ° 12μ£Όμ κ°κ°μ κ΅°μ ν¬μνμ¬ μ‘°μ§νμ μ¬μ μ λλ₯Ό νμΈν κ²°κ³Ό, 1% μ 3% TCN ν¨μΉ κ·Έλ£Ήμμ μ μλ―Έν κ·Όμ‘μΈ΅ μ¬μμ΄ κ΄μ°°λμκ³ , λ©΄μμ‘°μ§ννμΌμ λΆμ κ²°κ³Ό, 1% λ° 3% TCN ν¨μΉ κ·Έλ£Ήμμ μ¬ μνΌν λ° μ΄μλΆμ μ£Όλ³ μ μνκ΄ μμ±μ΄ μ μ΄λ£¨μ΄μ§ κ²μ νμΈν μ μμλ€.
λ³Έ μ°κ΅¬μμλ 3D νλ¦°ν
μ μ΄μ©ν νμμ λ°©μΆ ν¨μΉλ₯Ό νμ©νμμ λ, TCNμ νκ· ν¨κ³Όμ λλΆμ΄ λ곡 μ£Όμμ μλ κ·Όμ‘μΈ΅μ λΉλ‘―ν μ‘°μ§ μ¬μμλ μΆ©λΆν ν¨κ³Όκ° μμμ νμΈνμμΌλ©° μ΄λ μΆνμ μ°κ΅¬μμ λ§€μ° νμ©μ±μ΄ λμ κ²μΌλ‘ μκ°λλ€.Chapter 1. Introduction 1
Chapter 2. Materials and Methods 4
Chapter 3. Results 15
Chapter 4. Discussion 24
Chapter 5. Conclusion 29
Tables and Figures 30
Bibliography 59
Abstract in Korean 64λ°
ꡬκ°νΈνμΈν¬μμμ μμ€κΈ°μΈν¬νμμΈμ(CD24,44,133)κ³Ό μμ ν μνμ κ΄κ³ λΆμ
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Όλ¬Έ (μμ¬)-- μμΈλνκ΅ λνμ μκ³Όλν μνκ³Ό, 2017. 8. μμν.Introduction: Many studies have focused on the prognostic roles of cancer stem cell markers, but the results remain unclear. CD44 is the most well-known cancer stem cell marker in head and neck cancers, and CD24 and CD133 are representative cancer stem cell markers in many solid tumors. The aim of this study was to gain insight into the relationships between expression of CD24, CD44, and CD133, either alone or in combination, and prognostic parameters of oral squamous cell carcinoma (OSCC).
Methods: Patients with OSCC who underwent successful surgical resection from January 2003 to December 2011 in a single tertiary hospital were included in this study. Tissue arrays composed of 67 primary tumor tissues were generated and used for immunohistochemistry (IHC) against CD24, CD44, and CD133. IHC was graded by a semiquantitative histologic scoring system (H score) that considered the extent and intensity of the staining. IHC results were correlated with clinicopathological characteristics and with clinical outcomes such as relapse-free, disease-free, and overall survivals.
Results: In the 67 cases, the oral tongue was the most frequently affected primary site (56.7%). In tumor-lymph node-metastasis (TNM) staging, stage IV (34.3%) was most frequent, followed by stages I (26.9%), II (25.4%), and III (13.4%). Despite successful resection, there was 28.3% recurrence. TNM stage IV was highly related with the recurrence rate (p = 0.002). None of the 3 cancer stem cell markers (CD24, CD44, and CD133) had a statistically significant relationship with lymph node metastasis, TNM stage, or microscopic invasion into adjacent tissues. High expression of CD44 alone was associated with relapse-free survival (p=0.049), as were combined high expression of CD44 and CD133 (p=0.046) and CD44 and CD24 (p=0.015). CD44 expression also tended towards correlation with disease-free survivalhowever, this was not statistically significant (p=0.071).
Conclusions: Overall, the expression of CD44 had the strongest correlation with tumor recurrence. Additionally, when CD44 expression was combined with CD24 expression, CD24+CD44+ patients had the poorest chance of relapse-free survival. Thus, CD44 expression alone, and also in combination with CD24, should be considered when evaluating the prognosis for relapse-free survival of OSCC.I. Introduction 1
II. Materials and Methods 2
III. Results 6
IV. Discussion 10
V. Conclusion 15
VI. References 37
VII. Abstract in Korean 41Maste
ν-EU, ν-λ―Έ 무μꡬ쑰 λΉκ΅λΆμ
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Όλ¬Έ (μμ¬)-- μμΈλνκ΅ λνμ : κ΅μ νκ³Ό(κ΅μ μ§μνμ 곡), 2011.2. λ¬Έμ°μ.Maste
건μμμΉμ΄ νΌλ§λ‘μ΄ (Ni81Fe19) λ°λ§μ μκΈ°μ νΉμ±μ λ―ΈμΉλ μν₯
Thesis (doctoral)--μμΈλνκ΅ λνμ :κΈμ곡νκ³Ό,1998.Docto
Maximum entropy model-based intra-sentence segmentation for efficient syntactic analysis
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Όλ¬Έ(λ°μ¬)--μμΈλνκ΅ λνμ :μ»΄ν¨ν°κ³΅νκ³Ό,1999.Docto