21 research outputs found
κ°μΈν¬μ μ μ 체μ λ©νΈν λΆμ: νμμ μ°κ΅¬
νμλ
Όλ¬Έ(λ°μ¬)--μμΈλνκ΅ λνμ :μκ³Όλν μνκ³Ό,2019. 8. κΉνμ .Introduction: Hepatocellular carcinoma (HCC) is one of the most common cancers and epigenetics have been recognized to play a key role in its pathogenesis. This research aimed to evaluate the predictive value of DNA methylation profiles for late recurrence in HCC after resection.
Methods: A total of 184 patients who underwent curative resection at a single institute from 2011 to 2016 were prospectively enrolled. Illumina Infinium HumanMethylation EPIC 850K BeadChip (Illumina, CA, USA) arrays were used to examine DNA methylation profiles in HCC tumors and adjacent nontumorous liver tissues.
Results: Among the initial 184 patients, we excluded two, for whom tumor tissue was inadequate to perform tumor DNA extraction, in addition to 42 patients who presented with disease recurrence within 1 year after surgery. Of the remaining 140 patients, two tumor subgroups (methylation group 1 and 2) were identified based on methylation profiles using consensus clustering. Interestingly, methylation group 1 (N = 81, 57.8%) and 2 (N = 59, 42.2%) were different from each other, and the methylation profile of group 2 was most distinct from nontumorous liver tissues. In contrast, group 1 had similar methylation profiles to nontumorous liver tissues. At the time of analysis, 28 (23.5%) patients had experienced recurrence. In methylation group 1 and 2, this was observed in 12 (14.8%) and 16 (27.1%) patients, respectively. Moreover, the median relapse-free survival (RFS) of methylation group 1 was longer than that of methylation group 2 (not reached vs 1505 days, p = 0.036). Based on univariate analysis, patients with preoperative thrombocytopenia (plateletβ<β100β Γβ 109/L) had worse RFS than patients without thrombocytopenia (921 days vs not reached, p = 0.045). However, by multivariate analysis, the methylation profile was the only significant predictor of late recurrence.
Conclusions: The major finding of the present study is that late recurrence in patients who received curative resection for HCC can be predicted based on DNA methylation. Methylation group 2 was found to be associated with poorer RFS. Our data could be used to provide more personalized therapy for patients at higher risk of late recurrence.μλ‘ : κ°μΈν¬μμ μ μΈκ³μ μΌλ‘ κ°μ₯ ννκ² λ°μνλ μμ’
μ€μ νλμ΄λ©°, μ΄λ¬ν κ°μΈν¬μμ λ°λ³ κΈ°μ μλ νμ±μ μ 체μ λ³νκ° μ€μν μν μ νλ κ²μΌλ‘ μλ €μ Έ μλ€. λ³Έ μ°κ΅¬μμλ κ°μΈν¬μ μ μ 체 λ©νΈν λ³μ΄κ° μμ ν 1λ
μ΄νμ μΌμ΄λλ μ¬λ°κ³Όμ κ΄λ ¨μ±μ λν΄μ μμλ³΄κ³ μ νλ©°, μ΄λ¬ν μ μ 체 λ©νΈνκ° μ¬λ°μ μμΈ‘νλ μΈμλ‘μ μν μ ν μ μλμ§ μμλ³΄κ³ μ νλ€.
λ°©λ²: 2011λ
λΆν° 2016λ
κΉμ§ μμΈλνκ΅λ³μμμ κ·ΌμΉμ λͺ©μ μΌλ‘ μμ μ μνλ°μ μ΄ 184λͺ
μ κ°μΈν¬μ νμλ₯Ό μ ν₯μ μΌλ‘ λ±λ‘νμ¬ κ²μ²΄λ₯Ό μμ§νμλ€. Illumina Infinium HumanMethylation EPIC 850K BeadChip (Illumina, CA, USA)μΌλ‘ κ°μ μ‘°μ§κ³Ό μΈκ·Ό μ μ κ° μ‘°μ§μμ μ μ μ λ©νΈνλ₯Ό λΆμνμλ€.
κ²°κ³Ό: μ΄ 184λͺ
μ νμ μ€μμ, μ‘°μ§μμ μ μ μ μΆμΆμ μ€ν¨ν 2λͺ
μ νμ λ° μμ ν 1λ
μ΄μ μ μ¬λ°ν 42λͺ
μ νμλ λΆμμμ μ μΈλμλ€. λλ¨Έμ§ 140λͺ
μ νμμμ consensus clustering κΈ°λ²μ μ΄μ©νμ¬ λ©νΈν λ³νλ₯Ό λ°νμΌλ‘ ν¬κ² λ κ·Έλ£ΉμΌλ‘ λλμλ€. λ©νΈν
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κ·Έλ£Ή 1μ 81λͺ
(57.8%), κ·Έλ£Ή 2λ 59λͺ
(42.2%)μ΄μκ³ , λ κ·Έλ£Ήκ° λ©νΈνμ μ°¨μ΄λ λͺ
λ°±νμλ€. κ·Έλ£Ή 1μ μ μ μ λ©νΈνλ μ μ κ° μ‘°μ§κ³Ό μ μ¬ν κ²½ν₯μ 보μμΌλ, κ·Έλ£Ή 2μ λ©νΈνλ μ μ κ°μ‘°μ§κ³Ό κ·Ήλͺ
ν μ°¨μ΄κ° μμμΌλ©°, μ μ κ°μ‘°μ§μ λΉκ΅νμ¬ λ©νΈλ μ΄μ
μ΄ κ°μλ κ²½ν₯μ 보μλ€. λ
Όλ¬Έμ κ²°κ³Ό λΆμ μμ μμλ μ΄ 28λͺ
(23.5%)μ νμκ° μ¬λ°ν μνμλ€. κ·Έλ£Ή 1μμλ 12λͺ
(14.8%), 2μμλ 16λͺ
(27.1%)μ΄ μ¬λ°μ κ²½ννμλ€. 무λ³μμ‘΄κΈ°κ°μ λν λΆμμμλ κ·Έλ£Ή 1μ 무λ³μμ‘΄κΈ°κ°μ μμ§ μ€μκ°μ λλ¬νμ§ μμμΌλ©°, κ·Έλ£Ή 2μ 무λ³μμ‘΄κΈ°κ° μ€μκ°μ 1505μΌλ‘ λ³΄κ³ λμ΄ κ·Έλ£Ή 1μ΄ ν΅κ³μ μΌλ‘ μ μνκ² λ κΈ΄ 무λ³μμ‘΄κΈ°κ°μ 보μλ€(μ μνλ₯ 0.036). κ·Έ μΈμ, μμ μ νμν κ°μ(10λ§/L λ―Έλ§)κ° μλ νμλ€μ 무λ³μμ‘΄κΈ°κ° μ€μκ°μ΄ 921μΌλ‘, νμν κ°μκ° μλ νμλ€μ λΉν΄μ 짧μλ€(μ μνλ₯ 0.045). κ·Έλ¬λ, 무λ³μμ‘΄κΈ°κ°μ λν λ€λ³λ λΆμμμλ μ μ μ λ©νΈν μ°¨μ΄λ§μ΄ μμ 1λ
μ΄ν μ¬λ°μ μμΈ‘νλ μ μν μΈμμλ€.
κ²°λ‘ : λ³Έ μ°κ΅¬μμλ κ°μΈν¬μμ μ μ 체 λ©νΈν λ³νκ° μμ 1λ
μ΄νμ μ¬λ°κ³Ό κ΄λ ¨μ±μ΄ μμμ νμΈνμλ€. λ©νΈν
μ΄μ
κ·Έλ£Ή 2λ κ·Έλ£Ή 1μ λΉκ΅ν΄μ 짧μ 무λ³μμ‘΄κΈ°κ°μ 보μλ€. λ³Έ μ°κ΅¬ κ²°κ³Όλ₯Ό λ°νμΌλ‘ 1λ
μ΄ν μ¬λ°μνμ΄ λμ κ²μΌλ‘ μμλλ νμλ€μ μ μ νμ¬, μνλμ λ°λ₯Έ λ§μΆ€μΉλ£λ₯Ό νλλ° κ·Όκ±°λ₯Ό μ μν μ μμ κ²μΌλ‘ κΈ°λλλ€.Introduction 1
Material and Methods 6
Results 17
Discussion 40
References 46
Abstract in Korean 54Docto
μ 2ν λΉλ¨λ³ νμμ νλΉ λΉμ‘°μ κ΄λ ¨ μμΈ λΆμ : μ 5κΈ° κ΅λ―Όκ±΄κ°μμμ‘°μ¬μλ£(2010-2012)λ₯Ό μ΄μ©νμ¬
보건λνμ/μμ¬λΉλ¨λ³μ μ μΈκ³μμ μ£Όμμ¬λ§μμΈμ μ°¨μ§νλ μ§νμ΄λ©°, λΉλ¨λ³ νμμ μλ μ§μμ μΌλ‘ μ¦κ°νλ μΆμΈμ΄λ€. λΉλ¨λ³μ λ€μν ν©λ³μ¦μ μ λ°νλ μ§νμΌλ‘ μ μ ν νλΉ κ΄λ¦¬κ° μ΄λ£¨μ΄μ§μ§ μμΌλ©΄, λ―ΈμΈνκ΄ λ° λνκ΄ ν©λ³μ¦μ΄ λ°μν μ μμΌλ―λ‘ ν©λ³μ¦ μλ°©μ μν΄ μ격νκ³ μ κ·Ήμ μΈ νλΉ μ‘°μ μ΄ νμμ μ΄λ€. κ·Έλ¬λ μ°λ¦¬λλΌ λΉλ¨λ³ νμλ€μ νλΉ μ‘°μ λ₯ κ³Ό κ΄λ ¨λ ν΅κ³μΉλ₯Ό μ΄ν΄λ³΄λ©΄, κ΅λ΄μ νλΉ μ‘°μ κΈ°μ€μΈ λΉννμμ 6.5% λ―Έλ§, λ―Έκ΅ κΈ°μ€μΈ λΉννμμ 7% λ―Έλ§μ κΈ°μ€μ μ μ©νμμ λ, μ‘°μ λ₯ μ΄ κ°κ° 27.9%, 43.4% μμ€ λ°μ λμ§ μμ μ 체 λΉλ¨λ³ νμμ μ λ° μ΄μμ΄ νλΉ κ΄λ¦¬κ° λΆμ μ ν κ²μΌλ‘ λνλ¬λ€. λΉλ¨λ³ κ΄λ¦¬λ λΉλ¨ μ νμ λ°λΌ μ κ·Ό λ°©μμ λ€μ μ°¨μ΄κ° μμΌλ©°, κ·Έ μ€ μ 2ν λΉλ¨λ³μ 경ꡬ νλΉκ°νμ , μΈμλ¦°, λΉμ½λ¬Όμλ² λ± μ¬λ¬ κ°μ§ μΉλ£λ₯Ό λ¨λ
λλ λ³νν μ μλ μ νμ΄λ€. λ°λΌμ λ³Έ μ°κ΅¬μμλ μ±μΈμ 90-95%κ° μκ³ μλ μ νμ΄λ©°, νλΉ κ΄λ¦¬ λ°©λ²μ΄ λΉκ΅μ λ€μν μ 2ν λΉλ¨λ³ νμμ νλΉ λΉμ‘°μ μ μν₯μ μ£Όλ μμΈμ λΆμν¨μΌλ‘μ¨, ν₯ν μ 2ν λΉλ¨λ³ νμλ₯Ό μ μ νκ² κ΄λ¦¬νκΈ° μν 보건μλ£μ μ±
μ λ§λ ¨νλλ° κΈ°μ΄μλ£λ₯Ό μ μνκ³ μ νμλ€.
λ³Έ μ°κ΅¬λ μ 5κΈ° κ΅λ―Όκ±΄κ°μμμ‘°μ¬(2010-2012) μλ£λ₯Ό μ΄μ©νμμΌλ©°, μ€λ¬Έ μ‘°μ¬μ μ°Έμ¬ν μ 체 λμμ 25,534λͺ
μ€ μμ¬μκ² λΉλ¨λ³μ μ§λ¨λ°μ μ μ΄ μκ³ , μ§λ¨ μ°λ Ή λ° μΉλ£ μ 보 λ±μ λ°νμΌλ‘ μ 2ν λΉλ¨λ³μ΄λΌ λ³Ό μ μλ 1,233λͺ
μ λμμΌλ‘ λΆμνμλ€. μ νμ°κ΅¬λ₯Ό ν΅ν΄ νλΉ λΉμ‘°μ μ μν₯μ μ£Όλ μμΈμ νμ
νμ¬ λ³μλ‘ μ μ νμκ³ , ν΅κ³ λΆμμ SAS version 9.4λ₯Ό μ¬μ©νμμΌλ©°, κΈ°μ λΆμ λ° Survey νΉμ±μ λ°μν Rao-scott chi-square, logistic regression λΆμ λ°©λ²μ μννμλ€.
λ³Έ μ°κ΅¬μ κ²°κ³Ό μ 2ν λΉλ¨λ³ νμ 1,233λͺ
μ€ μ μ νκ² νλΉμ΄ μ‘°μ λμ§ μλβνλΉ λΉμ‘°μ κ΅°βμ 648λͺ
μΌλ‘ μ 체 λμμμ 52.6%μ΄μλ€. Rao-scott chi-square λΆμ κ²°κ³Ό, κΈ°νΌμΈ κ²½μ°, λΉλ¨ μ λ³κΈ°κ°μ΄ 15λ
μ΄μμΈ κ΅°, μΈμλ¦° λ° νλΉκ°νμ λ³ν©μλ²μΌλ‘ μΉλ£νλ κ΅°, κ³ νμ λΉμ λ³κ΅°, κ³ μ½λ μ€ν
λ‘€νμ¦ μ λ³κ΅°, κ³ μ€μ±μ§λ°©νμ¦ μ λ³κ΅°, νμ¬ν‘μ°κ΅°, μλ©΄μκ° 9μκ° μ΄μμΈ κ΅°μμ νλΉ λΉμ‘°μ λ₯ μ΄ λμλ€. Survey νΉμ±μ λ°μν logistic regression κ²°κ³Ό, μ΄μ‘Έ μ΄νμ λΉν΄ κ³ μ‘Έμμ νλΉ λΉμ‘°μ κ΅μ°¨λΉκ° 1.87μκ³ , μμΈμ λΉν΄ μΆ©μ²μ κ΅μ°¨λΉλ 1.99, μ λΌ/μ μ£Όμ κ΅μ°¨λΉλ 1.72μΌλ‘ λνλ¬λ€. λΉλ¨ μ λ³κΈ°κ°μ 5λ
λ―Έλ§μΈ κ΅°μ λΉν΄ 5-14λ
μΈ κ΅°μ κ΅μ°¨λΉλ 2.00, 15λ
μ΄μμΈ κ΅°μ κ΅μ°¨λΉλ 3.15μ΄μλ€. λΉλ¨ λΉμΉλ£κ΅°μ λΉν΄ μΈμλ¦° λ° νλΉκ°νμ λ³ν©μλ²μΌλ‘ μΉλ£νλ κ΅°μ κ΅μ°¨λΉλ 4.23μ΄μμΌλ©°, κ³ μ€μ±μ§λ°©νμ¦ λΉμ λ³κ΅°μ λΉν΄ μ λ³κ΅°μ κ΅μ°¨λΉλ 2.78λ‘ λνλ¬λ€. λΉμμ£Όκ΅°μ λΉν΄ μ€κ°μνκ΅°μ κ΅μ°¨λΉλ 0.56μ΄μκ³ , λΉν‘μ°κ΅°μ λΉν΄ κ³Όκ±°ν‘μ°κ΅°μ κ΅μ°¨λΉλ 0.62μ΄μμΌλ©°, μλ©΄μκ° 6μκ° λ―Έλ§μΈ κ΅°μ λΉν΄ 9μκ° μ΄μμΈ κ΅°μ κ΅μ°¨λΉλ 1.81μ΄μλ€.
μ΄ μ°κ΅¬λ μ 2ν λΉλ¨λ³ νμλ₯Ό λμμΌλ‘ μΈκ΅¬μ¬νμ μμΈ λ° κ±΄κ°ννμμΈμ λͺ¨λ 보μ νμ¬ λΆμν μ°κ΅¬μ΄λ©°, νΉν κ΅λ΄ μ°κ΅¬μμ λ€μ λ―Έν‘νλ λΉλ¨λ³ μ νμ λ°λ₯Έ μ°κ΅¬λμμ ꡬλΆμ μλνμκ³ , νλΉ λΉμ‘°μ κ³Ό κ΄λ ¨νμ¬ κ±°μ£Ό μ§μ μ λν΄ μ§λ¦¬νμ ꡬλΆμ λ°λ₯Έ λΆμμ μλνμλ€λ μ μμ μμκ° μλ€.
λ³Έ μ°κ΅¬μ κ²°κ³Όλ₯Ό λΉμΆμ΄ λ³Ό λ, μ 2ν λΉλ¨λ³ νμλ₯Ό μν νλΉ μ‘°μ κ΄λ¦¬ νλ‘κ·Έλ¨μ κ°λ°ν μ νλΉ λΉμ‘°μ μ κ΅μ°¨λΉκ° λμλ κ΅°μ λν΄ μ κ·Ήμ μΈ κ΄μ¬μ κ°μ ΈμΌ νκ³ , κ·Έ μ€ κ±°μ£Όμ§μμ λ°λ₯Έ νλΉ λΉμ‘°μ μ μ°¨μ΄κ° μμμ μΈμνμ¬μΌ νλ€. λν λΉλ¨λ³μ μκ° κ΄λ¦¬κ° νμμ μΈ μ§νμ΄κΈ΄ νλ μ΄λ₯Ό κ°μΈμκ²λ§ κ°μ‘°ν κ²μ΄ μλλΌ κ΅κ°μ μ°¨μμμ κ΄λ¦¬κ° νμν΄μΌν¨μ μΈμνκ³ , κ°μΈ λ° μ§μμ νκ²½μ μ°¨μ΄λ₯Ό κ³ λ €νλ©΄μ κ΅κ°μμ ν΅ν©μ μΌλ‘ κ΄λ¦¬ν μ μλ ν¬κ΄μ μΈ λ³΄κ±΄μλ£μ μ±
μ λ§λ ¨ν νμκ° μλ€.ope
Synthetic studies on lasonolide A analogues
Thesis (master`s)--μμΈλνκ΅ λνμ :ννλΆ μ κΈ°ννμ 곡,2004.Maste
μ μ΄μ± λμ₯μμμ cetuximab μΉλ£ν¨κ³Όμ μμΈ‘μΈμλ‘μ KRAS, BRAF, PTEN, IGF1R, EGFR intron 1 CA statusμ λΆμ
νμλ
Όλ¬Έ (μμ¬)-- μμΈλνκ΅ λνμ : μνκ³Ό, 2011.8. κΉνμ .Maste
무μ μΈμ§ ν΅μ κ³Ό κΈ°κΈ°κ° μ§μ ν΅μ μ μ±λ₯ λΆμ λ° μ΅μ ν
νμλ
Όλ¬Έ (λ°μ¬)-- μμΈλνκ΅ λνμ : 곡과λν μ κΈ°Β·μ»΄ν¨ν°κ³΅νλΆ, 2018. 2. μ΄μ¬ν.Cognitive radio (CR) and device-to-device (D2D) communications are promising technologies to enhance high spectral efficiency and regarded as key technologies for the upcoming fifth generation (5G) wireless communications.
In CR, unlicensed users, a.k.a. secondary users, are allowed to opportunistically reuse underutilized spectrum bands which are allocated to licensed users, a.k.a. primary users.
In D2D communications, D2D users directly communicate each other without going through a base station, typically by using the cellular spectrum.
Since both CR and D2D communications take place in the spectrum band already occupied by legacy users, interference management is necessary.
Especially, in CR, reducing mutual interference between primary and secondary network is one of the most important factors to improve network reliability. In D2D communications, a comprehensive interference management scheme is needed which limits not only the mutual interference between them but also the interference to cellular users.
The dissertation consists of two main results. First, we investigate an underlay CR network consisting of a single-hop secondary network co-existing with a multi-hop primary network. In the secondary network, the secondary destination receives the same interference signals from primary terminals over different time. To improve reliability of the secondary destination, it cancels the interference by using successive interference cancellation. We analyze the outage probability of the primary network in an integral expression and obtain its closed form for a special case. Also, we approximate the outage probability of the secondary network in a closed form. The validity of our analysis is verified by computer simulations. It is shown that the analytical results for the outage probability of the primary network perfectly match the simulation results. Also, it is shown that approximate outage probability of the secondary network is close to the simulation results.
Second, we investigate underlay D2D communications in cellular networks where D2D transmitters transmit data to their receivers using cellular spectrum. We analyze the average sum throughput of D2D receivers in interference-limited channels. To enhance it, we propose a semi-distributed spectrum access scheme which consists of two stages. In the first stage, a cellular base station divides whole D2D transmitters into multiple groups and assigns a different subchannel to each group. In the second stage, D2D transmitters in each group randomly access the subchannel assigned to it with predetermined access probability. We formulate an optimization problem to find the groups and access probabilities which maximize the average sum throughput. To overcome the prohibitive computational complexity to obtain its optimal solution, we decompose it into two subproblems: one to find groups and one to find access probabilities. A heuristic grouping algorithm is adopted to solve the former, and a branch-and-bound based algorithm is proposed to solve the latter. The validity of the branch-and-bound based algorithm is shown by performance comparison with an exhaustive search. It is shown that the heuristic grouping and the branch-and-bound based algorithm achieve higher average sum throughput than conventional methods.1 Introduction 1
1.1 Background and Related Work 2
1.1.1 Cognitive Radio 2
1.1.2 Device-to-Device Communication 4
1.2 Outline of Dissertation 7
1.3 Notations 8
2 Underlay Cognitive Radio Networks with Multi-Hop Primary Transmission 11
2.1 System Model 13
2.2 Performance Analysis of Primary Network 17
2.2.1 Outage Probability for DF Relays 17
2.2.2 Outage Probability for AF Relays 22
2.2.3 Optimal Number of Hops 23
2.3 Outage Probability of Secondary Network 24
2.3.1 With DF Primary Relays 24
2.3.2 With AF Primary Relays 31
2.4 Numerical Results 33
2.4.1 Outage Probability of Primary Network 33
2.4.2 Outage Probability of Secondary Network 34
2.5 Summary and Application 35
3 Semi-Distributed Spectrum Access for Underlay D2D Communications 51
3.1 System Model 56
3.1.1 Interference Constraint 57
3.1.2 Signaling Overhead 58
3.2 Average Sum Throughput of D2D Receivers 59
3.3 Problem Formulation and Grouping Algorithm 64
3.3.1 Problem Formulation 64
3.3.2 Grouping Algorithm for D2D Transmitters 66
3.4 Optimal Access Probability 71
3.4.1 Problem Formulation 71
3.4.2 Branch-and-Bound Based Algorithm 72
3.5 Numerical Results 77
3.6 Summary 98
4 Conclusion 99
4.1 Summary 99
4.2 Future Works 100
A Derivation of (3.40) 102
B Proof of Non-Convexity of Problem (3.42) 104
C Classication of a Set in Branch-and-Bound Based Algorithm 106
D Maximum of Average Sum Throughput of D2D Receivers 108
Bibliography 109
Korean Abstract 119Docto
μ¬λ μ κ²½λͺ¨μΈν¬μ’ μμ μΌμ΄λλ μν¬ν μμ€μ μ΄μ§μ cAMPκ° λ―ΈμΉλ μν₯μ λν μ°κ΅¬
Thesis (master`s)--μμΈλνκ΅ λνμ :νλκ³Όμ μ μ 곡νμ 곡02000.Maste
(A) Study on scan RTA effect on the MILC and Polycrystalline-Silicon Solar cells fabrication
νμλ
Όλ¬Έ(μμ¬) --μμΈλνκ΅ λνμ :μ¬λ£κ³΅νλΆ,2010.2.Maste