231 research outputs found
Dupilumab Therapy Improves Stratum Corneum Hydration and Skin Dysbiosis in Patients With Atopic Dermatitis
Purpose: We aimed to investigate the effects of dupilumab on 1) the permeability and antimicrobial barrier, 2) the composition of the skin microbiome, and 3) the correlation between changes in skin barrier properties and microbiota in atopic dermatitis (AD) patients.
Methods: Ten patients with severe AD were treated with dupilumab for 12 weeks. Disease severity was assessed using the Eczema Area and Severity Index (EASI). Skin barrier function was evaluated by measuring transepidermal water loss, stratum corneum (SC) hydration, and pH. The following parameters were analyzed in the pre- and post-treatment SC samples; 1) skin microbiota using 16S rRNA gene sequencing, 2) lipid composition using mass spectrometry, and 3) human Ξ²-defensin 2 (hBD-2) expression using quantitative reverse transcription polymerase chain reaction.
Results: SC hydration levels in the lesional and non-lesional skin increased after 12-week dupilumab therapy (24.2%, P < 0.001 and 59.9%, P < 0.001, respectively, vs. baseline) and correlated with EASI improvement (r = 0.90, P < 0.001 and r = 0.85, P = 0.003, respectively). Dupilumab increased the long-chain ceramide levels in atopic skin (118.4%, P = 0.028 vs. baseline) that correlated with changes in SC hydration (r = 0.81, P = 0.007) and reduced the elevated hBD-2 messenger RNA levels (-15.4%, P = 0.005 vs. baseline) in the lesional skin. Dupilumab decreased the abundance of Staphylococcus aureus. In contrast, the microbial diversity and the abundance of Cutibacterium and Corynebacterium species increased, which were correlated with an increase in SC hydration levels (Shannon diversity, r = 0.71, P = 0.027; Cutibacterium, r = 0.73, P = 0.017; Corynebacterium, r = 0.75, P = 0.012). Increased abundance of Cutibacterium species was also correlated with EASI improvement (r = 0.68, P = 0.032).
Conclusions: Th2 blockade-induced normalization of skin microbiome in AD patients is associated with increased SC hydration.ope
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Όλ¬Έ(λ°μ¬)--μμΈλνκ΅ λνμ :μκ³Όλν μκ³Όνκ³Ό,2019. 8. μ₯μ±νΈ.Dendritic spines are small postsynaptic protrusions on a dendrite that receive most of the excitatory synaptic input in the brain. The cytoskeleton of the dendritic spines is predominately composed of actin filaments which form the structural and functional network associating specialized substructures like postsynaptic density (PSD). The morphology of spines is highly variable and dynamically regulated with neuronal activity by actin-regulating proteins in PSD.
An increasing number of postsynaptic proteins such as SAPAPs and SHANKs are implicated in different forms of mood disorders such as bipolar disorders, autism spectrum disorders, obsessive-compulsive disorders, and schizophrenia although their underlying mechanisms have not been fully understood. Accumulating evidence from recent studies suggests that the structural remodeling of dendritic spines is critical for synaptic plasticity and the mechanisms regulating actin cytoskeleton may contribute to spine pathology in these neuropsychiatric disorders.
nArgBP2 was originally identified as a protein that directly interacts with SAPAP3, and. the previous study found that ArgBP2/nArgBP2 controls the balance between adhesion and motility by coordinating multiple signaling pathways converging on the actin cytoskeleton. A recent study found that genetic deletion of ArgBP2/nArgBP2 (SORBS2) in mice is known to cause behavioral phenotypes resembling human intellectual disability (ID). It has been, however, mostly unknown that how nArgBP2 deficiency leads to phenotypes observed in ID and more importantly, how nArgBP2 functions at postsynapses and its relevance to the underlying cellular and molecular mechanisms that might be related to ID.
To investigate the roles of nArgBP2 at synapses, based on the results from previous studies, I set up the following research hypotheses in my dissertation. 1) nArgBP2 is one of the key protein that regulates actin cytoskeleton at postsynapses, 2) nArgBP2 regulates the morphological changes of dendritic spines, 3) given that dendritic spines are major sites that receive most of the excitatory synaptic inputs, nArgBP2 controls the formation of excitatory synapses, 4) since excitatory-inhibitory synaptic balance(E/I balance) is the key mechanism that maintains homeostatic functional properties of nervous system, E/I imbalance caused by nArgBP2 deficiency might be the underlying factor associated with synaptic dysfunction observed in ID.
I found that the knockdown (KD) of nArgBP2 by specific shRNA resulted in a dramatic change in dendritic spine morphology. The nArgBP2 KD also impaired the formation of excitatory synapses which largely terminated at dendritic shafts instead of dendritic spine heads in spiny neurons. The aberrant formation of excitatory synapses resulted in a reduced mean frequency of miniature excitatory postsynaptic currents. I also found that the morphological changes were associated with increased WAVE1/PAK/cofilin phosphorylation, and this effect was rescued by either inhibiting PAK or activating cofilin combined to sequestration of WAVE. Using live-cell imaging technique, I confirmed that a marked increase of actin cytoskeleton dynamics resulted in a significant increase in the motility of dendritic spines in nArgBP2 KD neurons.
Surprisingly, nArgBP2 KD did not cause any morphological defect in the mature stage when the dendritic spines were stabilized. I inferred that nArgBP2 may be needed when significant structural remodeling is needed, such as developing stage. To test this idea, I decided to induce chemically-induced Long Term Potentiation (cLTP) in mature neurons. It is known to mimic many features of developing stages, including dramatic remodeling of pre-and postsynaptic structures in mature neurons. The cLTP significantly increased the size of the spine heads of control neurons while remained almost the same in nArgBP2 KD neurons. I also measured the 3D morphological features of dendritic spines under the same conditions and found that cLTP in nArgBP2 KD neurons could not induce normal head enlargement in dendritic spines. These results support my idea that nArgBP2 controls the actin cytoskeleton dynamics also in mature neurons.
Together, my research suggests that nArgBP2 functions to regulate the actin cytoskeleton dynamics in dendritic spines. It plays a particularly important role when active structural remodeling is needed, such as spine morphogenesis and subsequent spine-synapse formation in developing stages and during synaptic plasticity in mature stages. The results also raise the possibility that the aberrant regulation of synaptic actin dynamics caused by reduced nArgBP2 expression may contribute to the synaptic excitatory/inhibitory imbalance observed in ID.μμλκΈ°κ°μλ λμμ λλΆλΆμ ν₯λΆμ± μλ
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κ·Έ λ°λ³ κΈ°μμ΄ μμ ν λ°νμ§μ§ μμμ§λ§, SAPAP λ° SHANKμ κ°μ μλ
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μ΄μ μ°κ΅¬μ κ²°κ³Όλ₯Ό λ°νμΌλ‘ μλ
μ€μμ nArgBP2μ μν μ κ·λͺ
νκΈ° μν΄ λ€μκ³Ό κ°μ΄ κ°μ€μ μ€μ νμλ€. 1)nArgBP2λ μλ
μ€νμΈν¬μμ μ‘ν΄ κ³¨κ²©κ΅¬μ‘°λ₯Ό μ‘°μ νλ μ£Όμ λ¨λ°±μ§ μ€ νλμ΄λ©°, 2) nArgBP2λ μμλκΈ°κ°μμ ννλ³νλ₯Ό μ‘°μ νκ³ , 3) μμλκΈ°κ°μλ λλΆλΆμ ν₯λΆμ± μ νΈλ₯Ό λ°λ νΉμ΄κ΅¬μ‘°μ΄λ―λ‘ nArgBP2κ° ν₯λΆμ± μλ
μ€μ νμ±μ μ‘°μ ν κ²μ΄λ©°, 4) ν₯λΆ-μ΅μ μ± μλ
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μ€ μ΄μκ³Ό κ΄λ ¨λ κ·Όλ³Έμ μΈ μμΈμΌ μ μλ€.
RNA κ°μμ μν nArgBP2μ λ°ν μ ν΄λ μμλκΈ°κ°μ νμ±μ μν₯μ μ£Όμ΄ μ μμ μΈ ννκ° μλ νλ‘ν¬λμ(filopodia)μ νμ±μ μ¦κ°μμΌ°λ€. λν μ μμ μΈ μμλκΈ°κ°μ νμ±μ κ²°ν¨μ μ΅μ μ± μλ
μ€ νμ±μ μν₯μ΄ μμλ λ°λ©΄, ν₯λΆμ± μλ
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μ€μ μ΄μμ λ―Έλμ΄μ² ν₯λΆμ± μλ
μ€ ν μ λ₯μ νκ· λΉλλ₯Ό κ°μμμΌ°λ€.
nArgBP2κ° μ ν΄λ μ κ²½μΈν¬μμ μμλκΈ°κ°μμ ννλ³νλ WAVE1/PAK/cofilinμ μΈμ°νμ κ΄λ ¨μ΄ μμΌλ©°, WAVEμμ μνΈμμ©μ΄ μ ν΄λ μνμμ PAKμ μ΅μ νκ±°λ cofilinμ νμ±νμν΄μΌλ‘μ¨ nArgBP2 μ ν΄ ν¨κ³Όκ° μμλ¨μ λ°κ²¬νμλ€. λν μ€μκ°μΈν¬μμκΈ°λ²μ μ΄μ©νμ¬ nArgBP2μ κΈ°λ₯μ΄ μ ν΄λ λ°λ¬ λ¨κ³μ μ κ²½μΈν¬μμ μ‘ν΄κ³¨κ²© μνμ νμ ν μ¦κ°κ° μμλκΈ°κ°μμ μ΄λμ±μ ν¬κ² μ¦κ°μν΄μ νμΈνμλ€.
λλκ²λ, μ κ²½μΈν¬μ μμλκΈ°κ°μκ° νμ±λμ΄ μμ νλ νμΈ μ±μλ¨κ³μμλ nArgBP2μ λ°ν μ ν΄λ‘ μΈν μμλκΈ°κ°μ ννμ κ²°ν¨μ΄ λ°κ²¬λμ§ μμλ€. λ°λ¬λ¨κ³μ μ κ²½μΈν¬μ κ°μ΄ ꡬ쑰μ 리λͺ¨λΈλ§μ΄ μΌμ΄λ λ nArgBP2κ° νμν μ μμ κ²μ΄λΌκ³ μΆλ‘ νμκ³ , μ΄λ₯Ό νμΈνκΈ° μν΄ μ±μν μ κ²½μΈν¬μ μλ‘μ΄ μλ
μ€ νμ±μ μΌμΌν€λ ννμ μ₯κΈ° κ°ν (cLTP: chemical long-term potentiation)λ₯Ό μ λν΄λ³΄μλ€. ννμ μ₯κΈ° κ°νλ μ±μλ¨κ³μ λ΄λ°μμ μλ
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ν©ν΄λ³΄λ©΄, μ΄ μ°κ΅¬λ nArgBP2κ° μμλκΈ°κ°μμμ μ‘ν΄ κ³¨κ²©κ΅¬μ‘°μ νλμ±μ μ‘°μ νλ κΈ°λ₯μ λ΄λΉν¨μ λ°νλ€. λν nArgBP2κ° μ κ²½μΈν¬μ λ°λ¬λ¨κ³μμ μμλκΈ°κ°μ νμ± λ° μ€νμΈ-μλ
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μ€ μ‘ν΄ κ³¨κ²©κ΅¬μ‘°μ λΉμ μμ μΈ μ‘°μ μ΄ μ§μ μ₯μ μμ κ΄μ°°λλ μλ
μ€ E/I λΆκ· νμ κΈ°μ¬ν μ μμ κ²μΌλ‘ μ¬λ£λλ€.Abstract . β
°
Contents . v
List of Figures and Tables β
΅
List of Abbreviations . β
·
Introduction 1
Material and Methods . 5
Results . 19
Discussion 60
References . 67
Abstract in Korean . 75Docto
Baumann Skin Type in the Korean Male Population
Background: Research into the Baumann skin type (BST) has
recently expanded, with growing interest in the development
of an efficient and effective skin type classification system
for better understanding of this skin condition. Objective:
We aimed to identify male-specific skin type characteristics
with investigation into the distribution of BST by age and region
in the Korean male population and to determine the intrinsic
and extrinsic factors related to skin type. Methods: A
questionnaire was administered to collect information about
age, region, working behavior, drinking behavior, smoking
behavior, usual habit of sun protection, medical history, and
the BST which consisted of four parameters; oily (O) or dry
(D), sensitive (S) or resistant (R), pigmented (P) or non-pigmented
(N), and wrinkled (W) or tight (T). Results: We surveyed
1,000 Korean males aged between 20 and 60 years
who were divided equally by age and region. Of the total respondents,
OSNW type accounted for the largest percentage
and ORPW type the lowest. In terms of Baumann parameters,
O type was 53.5%, S type was 56.1%, N type was 84.4%
and W type was 57.5%. Several behavioral factors were
found to have various relationships with the skin type.
Conclusion: The predominant skin type in the Korean male
respondents was OSNW type, and the distribution of skin
types with regards to age and region was reported to be
distinct. Therefore, skin care should be customized based on
detailed skin types considering the various environmental
factors.ope
Development and External Validation of a Deep Learning Algorithm for Prognostication of Cardiovascular Outcomes
BACKGROUND AND OBJECTIVES:
We aim to explore the additional discriminative accuracy of a deep learning (DL) algorithm using repeated-measures data for identifying people at high risk for cardiovascular disease (CVD), compared to Cox hazard regression.
METHODS:
Two CVD prediction models were developed from National Health Insurance Service-Health Screening Cohort (NHIS-HEALS): a Cox regression model and a DL model. Performance of each model was assessed in the internal and 2 external validation cohorts in Koreans (National Health Insurance Service-National Sample Cohort; NHIS-NSC) and in Europeans (Rotterdam Study). A total of 412,030 adults in the NHIS-HEALS; 178,875 adults in the NHIS-NSC; and the 4,296 adults in Rotterdam Study were included.
RESULTS:
Mean ages was 52 years (46% women) and there were 25,777 events (6.3%) in NHIS-HEALS during the follow-up. In internal validation, the DL approach demonstrated a C-statistic of 0.896 (95% confidence interval, 0.886-0.907) in men and 0.921 (0.908-0.934) in women and improved reclassification compared with Cox regression (net reclassification index [NRI], 24.8% in men, 29.0% in women). In external validation with NHIS-NSC, DL demonstrated a C-statistic of 0.868 (0.860-0.876) in men and 0.889 (0.876-0.898) in women, and improved reclassification compared with Cox regression (NRI, 24.9% in men, 26.2% in women). In external validation applied to the Rotterdam Study, DL demonstrated a C-statistic of 0.860 (0.824-0.897) in men and 0.867 (0.830-0.903) in women, and improved reclassification compared with Cox regression (NRI, 36.9% in men, 31.8% in women).
CONCLUSIONS:
A DL algorithm exhibited greater discriminative accuracy than Cox model approaches.
TRIAL REGISTRATION:
ClinicalTrials.gov Identifier: NCT02931500.ope
Food environment factors affecting food consumption of households in a Korean urban-rural complex region
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ν μνκ°μ§μκ°, λμ΄μ§μμ μκ°μ¬λ°°λ μΉμ§λ‘λΆν°μ μ μ
λλ μνκ°μ§μκ° μ μμ μΌλ‘ λμλ€.
κ°κ΅¬μ μ΄μ© κ°λ₯ν μνλκ³Ό μνμλΉλμ μν νκ²½ μμΈλ³ λ¨λ³λ λΆμν κ²°κ³Ό, λμμ§μμ μ¬νκ²½μ μ νΉμ±μ΄, λμ΄μ§μμ μ¬νκ²½μ μ νΉμ±κ³Ό μ§μ λ΄ μνꡬ맀 μ©μ΄μ±μ΄ κ΄λ ¨μ΄ μμλ€. μ΄λ€ κ΄λ ¨ μμΈλ€μ ν¬ν¨ν λ€μ€νκ·λΆμμμλ λμ΄μ§μμμλ§ μνꡬ맀μμ κ΅μ‘μμ€μ΄ κ°κ΅¬μ 1μ£ΌμΌ λμμ μ΄μ© κ°λ₯ν μ΄ μνλ(μ΄μ‘Έ vs. μ€μ‘Έ: =16.5, p=0.013)κ³Ό μ μμ μΈ κ΄λ ¨μ±μ΄ μμκ³ , μ§μ λ΄ μνꡬ맀 μ©μ΄μ±μ΄ λλ€κ³ ν κ°κ΅¬μ 1μ£ΌμΌ λμμ μ΄ μνμλΉλ(=1.03, p=0.006)μ΄ λ λ§μλ€. λν λμμ λμ΄μ§μ λͺ¨λμμ κ°κ΅¬μ μ΄μ© κ°λ₯ν μ΄ μνκ°μ§μκ° λ§μμλ‘ κ°κ΅¬μ 1μ£ΌμΌ λμμ μ΄ μνμλΉλ(λμ: =0.38, p<0.001, λμ΄: =0.29, p<0.001)λ λ§μ κ²μΌλ‘ λνλ¬λ€.
κ°κ΅¬μμ μνκ΅° μ μ(DDS)μ μν νκ²½ μμΈλ³ λ¨λ³λ λΆμν κ²°κ³Ό, λμμ§μμ κ²½μ° μνμμ κΉμ§μ νκ· μ΄λμκ°μ΄, λμ΄μ§μμμλ κ°κ΅¬μ μ¬νκ²½μ μ νΉμ±, κ°κ΅¬μ μνꡬ맀 νκ²½, κ°κ΅¬μ μ΄μ© κ°λ₯ν μ΄ μνλ λ±μ μμΈλ€μ΄ κ΄λ ¨λ κ²μΌλ‘ λνλ¬λ€. ννΈ κ°κ΅¬μ μν μ΄μ© κ°λ₯μ±κ³Ό κ°κ΅¬μμ μνκ΅° μ μ(DDS)μμ κ΄λ ¨μ±μ μ΄ν΄λ³Έ κ²°κ³Ό, λμμ§μμμλ κ°κ΅¬μ 1μ£ΌμΌ λμμ μ μ
μν μλΉλμ΄ μμ μκ΄κ΄κ³(r=-0.121, p<0.05)λ₯Ό, λμ΄μ§μμμλ κ°κ΅¬μ 1μ£ΌμΌ λμμ μ μ
μνλ, μ΄μ© κ°λ₯ν μ΄ μνλ, μ μ
μν μλΉλ, κ·Έλ¦¬κ³ μ΄ μν μλΉλμ΄ μμ μκ΄κ΄κ³λ₯Ό 보μλ€. μ΄λ€ κ΄λ ¨ μμΈλ€μ ν¬ν¨ν λ€μ€νκ·λΆμ κ²°κ³Όμμλ λμμ§μμ μΈλμ ν, μνμμ κΉμ§ νκ· μ΄λμκ°(=0.045, p<0.001), νκ· μ΄λ거리(=-0.077, p=0.025)κ°, λμ΄μ§μμμλ κ°κ΅¬μ μ΅κ·Ό νλ¬ μΈμλΉ, μνꡬ맀μμ κ΅μ‘μμ€, μ§μ λ΄ μνꡬ맀 μ©μ΄μ±(=0.114, p=0.030), μνμμ κΉμ§μ νκ· μ΄λ거리(=0.071, p<0.001), κ°κ΅¬μ μ΄μ© κ°λ₯ν μ΄ μνκ°μ§μ(=0.036, p=0.005)κ° κ°κ΅¬μμ μνκ΅° μ μ(DDS)μ μ μμ μΈ κ΄λ ¨μ±μ λνλλ€.
λ³Έ μ°κ΅¬μμλ λμμ λμ΄μ§μμ κ°κ΅¬μ κ°κ΅¬μμ μν νκ²½μ μ°¨μ΄κ° μμμΌλ©°, λμ΄μ§μ κ°κ΅¬μ κ°κ΅¬μμ μμνμ΄ λ λ€μν μν νκ²½ μμΈλ€κ³Ό κ΄λ ¨μ΄ μλ κ²μΌλ‘ λνλ¬λ€. μ΄λ μ§μ νΉμ±μ λ°λΌ κ°κ΅¬μ κ°κ΅¬μμ μμνμ μν₯μ λ―ΈμΉλ μν νκ²½ μμΈμ΄ λ€λ₯Ό μ μμμ 보μ¬μ€λ€. λ°λΌμ μ§μ κΈ°λ°μ μμμ€μ¬μ μ μ±
μ μ립ν λλ μ§μμ νΉμ±κ³Ό μν νκ²½ μ°¨μ΄λ₯Ό κ³ λ €νλ κ²μ΄ νμνλ©°, μ΄λ₯Ό μν΄ κ°κ΅¬μ κ°κ΅¬μμ μμνκ³Ό μ£Όλ³ μν νκ²½μ λν΄ μΈ‘μ λ° νκ°ν μ μλ λ€μνκ³ ν¨μ¨μ μΈ λꡬμ κ°λ°μ΄ μκΈν μ΄λ€μ ΈμΌ νλ€κ³ μκ°λλ€. μ΄λ₯Ό λ°νμΌλ‘ μν νκ²½μ΄ μ·¨μ½ν μ§μμ μ λ³νκ³ μ΄λ€ μ§μμ μμν νκ²½μ κ°μ νκΈ° μν λ
Έλ ₯μ΄ μ΄λ€μ ΈμΌ ν κ²μ΄λ€.κ΅λ¬Έμ΄λ‘ i
νλͺ©μ°¨(List of Tables) ix
κ·Έλ¦Όλͺ©μ°¨(List of Figures) xiv
λΆλ‘λͺ©μ°¨(List of Appendices) xv
I. μλ‘ 1
1. μ°κ΅¬ λ°°κ²½ 1
2. μ°κ΅¬ λͺ©μ 5
II. λ¬Ένκ³ μ°° 8
1. μμν νκ²½ 8
(1) μμν νκ²½μ μ μ λ° λ²μ£Ό 8
(2) μμν νκ²½ μ°κ΅¬ λͺ¨ν 11
2. κ°κ΅¬μ κ°κ΅¬μμ μμνκ³Ό κ΄λ ¨λ μν νκ²½ μ‘°μ¬ 15
(1) κ°κ΅¬μ κ°κ΅¬μ λ¨μμ μν νκ²½ μ‘°μ¬ 15
(2) μ§μ λ¨μμ μν νκ²½ μ‘°μ¬ 20
3. κ°κ΅¬μ κ°κ΅¬μμ μν νκ²½ μΈ‘μ 24
(1) κ°κ΅¬μ μ΄μ© κ°λ₯ν μν λ° μνμλΉ μΈ‘μ 27
(2) μνꡬ맀 νκ²½ μΈ‘μ 29
(3) μΈμ νκ²½ μΈ‘μ 31
4. κ°κ΅¬μμ μν μμ·¨ νκ° 32
5. μ°λ¦¬λλΌ κ°κ΅¬μ κ°κ΅¬μμ μν νκ²½ λ³ν 36
(1) μ°λ¦¬λλΌ μμν νκ²½ λ³ν 36
(2) λμμ λμ΄μ§μ κ°κ΅¬μ κ°κ΅¬μμ μν νκ²½ μ°¨μ΄ 39
(3) λβ’λ볡ν©μ 41
III. μ°κ΅¬ 1 : κ°κ΅¬μ μνꡬ맀 λ° μνμλΉμ κ΄λ ¨λ μν νκ²½ μμΈ λΆμ β μμΈ μΈκ·Ό λβ’λ볡ν©μ§μμ λμμΌλ‘ 44
1. μλ‘ 44
2. λ΄μ© λ° λ°©λ² 47
(1) μ‘°μ¬ λμ μ§μ λ° λμ κ°κ΅¬ μ μ 47
(2) μ‘°μ¬κ΅¬μ± λ° μ‘°μ¬ νλͺ© 50
(3) κ°κ΅¬μ μμν μ‘°μ¬ λ° μνꡬ맀 νκ²½ μ€λ¬Έμ‘°μ¬ 61
(4) ν΅κ³λΆμ 69
3. κ²°κ³Ό 69
(1) κ°κ΅¬μ μμν νκ²½ μμΈλ€μ μΌλ°μ νΉμ± 69
(2) κ°κ΅¬μ μν νκ²½ μμΈκ³Ό κ°κ΅¬μ μ΄μ© κ°λ₯ν μνλ 75
(3) κ°κ΅¬μ μν νκ²½ μμΈκ³Ό κ°κ΅¬μ μνμλΉλ 90
(4) κ°κ΅¬μ μν νκ²½κ³Ό κ°κ΅¬μ μ΄μ© κ°λ₯ν μνλκ³Ό μνμλΉλ 103
4. κ³ μ°° 109
V. μ°κ΅¬ 2: κ°κ΅¬μμ μν μμ·¨μ κ΄λ ¨λ μν νκ²½ μμΈ λΆμ β μμΈ μΈκ·Ό λβ’λ볡ν©μ§μμ λμμΌλ‘ 115
1. μλ‘ 115
2. μ°κ΅¬ λ΄μ© λ° λ°©λ² 117
(1) μ°κ΅¬ λμμ 117
(2) μ‘°μ¬κ΅¬μ± λ° μ‘°μ¬ νλͺ© 118
(3) κ°κ΅¬μμ μμν μ‘°μ¬ λ° μΈμ νκ²½ μ€λ¬Έμ‘°μ¬ 118
(4) ν΅κ³λΆμ 121
3. κ²°κ³Ό 122
(1) κ°κ΅¬μμ μν νκ²½ μμΈ 122
(2) μν μμ·¨ λΉκ΅ λ° νκ° 131
4. κ³ μ°° 146
VI. μ’
ν©κ³ μ°° 153
VII. μμ½ λ° μ μΈ 161
1. μμ½ 161
2. μ μΈ 163
VIII. μ°Έκ³ λ¬Έν 165
λΆ λ‘(Appendices) 177
Abstract 187Docto
μ¬μ₯μ μ기곡λͺ μμ μ견과 ν¬λͺ νλ μ체 λ° λ§μ°μ€ μ¬μ₯μ λΉκ΅λ₯Ό ν΅ν μ기곡λͺ μμμ μ¬κ·Ό μ£Όν λ°©ν₯ κ°λ³μ μ νλ νκ°
Dept. of Medicine/λ°μ¬Verifying the microarchitecture of the heart can improve understanding of the fundamental heart structure-function relationships both in normal development and cardiovascular disease progression. The current study demonstrate a novel approach to characterize the microstructural response of the myocardium to cardiovascular disease by interrogating intact, un-sectioned myocardium with 3-dimensional (3D) histological imaging using a tissue-clearing technique and quantifying myocardial fiber orientation. In the same samples, diffusion magnetic resonance imaging, a clinically translatable non-invasive imaging technique, was also applied to demonstrate its potential in yielding a surrogate marker for myocardial fiber orientation. Both 3D histological imaging and diffusion magnetic resonance imaging were significantly correlated in verifying the helical architecture of the normal myocardium and that this normal helical structure is perturbed in both ischemic and non-ischemic heart failure model.
μ¬κ·Όμ μΌμ ν λ°©ν₯μ±μ κ°μ§κ³ λ°°μ΄λμ΄ μμΌλ©°, μ΄λ μ§λ³μ μ§νμ λ°λΌ λ³νν κ²μΌλ‘ μμΈ‘λλ, μ΄λ₯Ό λΉμΉ¨μ΅μ μΌλ‘ νκ°ν μ μλ λ°©λ²μ μμλ€. λ³Έ μ°κ΅¬λ, μλ‘ κ°λ°λ μ기곡λͺ
μμ κΈ°λ²μ΄ μ¬κ·Όμ μ£Όνλ°©ν₯μ μ νν μΈ‘μ ν μ μλ μ§μ λνμ¬ μ¬κ·Όμ ν¬λͺ
ν κΈ°λ²μ μ¬μ©νμ¬ μ기곡λͺ
μμμ μ νλλ₯Ό νκ°νκ³ μ νλ€. 7λ§λ¦¬μ λμ‘°κ΅°, 8λ§λ¦¬μ ννμ± μ¬λΆμ λͺ¨λΈ, 7λ§λ¦¬μ λΉννμ± μ¬λΆμ λͺ¨λΈ λ§μ΄μ€λ₯Ό λμμΌλ‘ μ€νμ μ§ννμμΌλ©°, μ기곡λͺ
μμμΌλ‘ νλν μ¬κ·Όμ μ£Όνλ°©ν₯μ΄ λ³λ¦¬νμ μΌλ‘ μ²λ¦¬λ ν¬λͺ
νλ μ¬μ₯μμ μ»μ μ¬κ·Όμ μ£Όνλ°©ν₯κ³Ό μΌμΉν¨μ λ°νλ€. λ³Έ μ°κ΅¬λ₯Ό ν΅νμ¬ μ¬μ₯ μ기곡λͺ
μμμ΄ μ¬κ·Ό μ£Όν λ°©ν₯μ μ νν κ°λ³ν μ μμμ΄ λ°νμ‘μΌλ©°, μΆν μμμ μμ©λ μ μλ μ¦κ±°λ‘μ μ μλ μ μμ κ²μΌλ‘ κΈ°λνλ€.ope
μ΄μν μλλ²μ΄ ννΌ νμλ£¨λ‘ μ° ν©μ± λ° CD44 λ°νμ λ―ΈμΉλ μν₯
Dept. of Medicine/μμ¬[νκΈ]
νμλ£¨λ‘ μ°μ μΈν¬μΈ κΈ°μ§μ μ€μν μ±λΆμΌλ‘ νΌλΆμμλ λ§μ μμ νμλ£¨λ‘ μ°μ΄ μ§νΌμ κ²°ν©μ‘°μ§μ μ‘΄μ¬νκ³ μλ€κ³ μλ €μ Έ μμΌλ κ·Όλμ λ€μ΄ ννΌμλ μλΉλμ νμλ£¨λ‘ μ°μ΄ κ°μ§νμ±μΈν¬μ¬μ΄μ κΈ°μ§μ μ‘΄μ¬νκ³ μμμ΄ λ°νμ‘λ€. νμλ£¨λ‘ μ°μ μ£Όμ μΈν¬νλ©΄μμ©μ²΄μΈ CD44μ κ²°ν©νμ¬ μΈν¬λ΄ μ νΈμ λ¬μ μ λ°νκ³ λ€μν μννμ ν¨κ³Όλ₯Ό λνλ΄λ©° ννΌμμμ νμλ£¨λ‘ μ°κ³Ό CD44μ μνΈμμ©μ κ°μ§νμ±μΈν¬μ λΆνμ μ½λ μ€ν
λ‘€ ν©μ±μ μ¦κ°, μΈ΅νμ체μ νμ±κ³Ό λΆλΉλ₯Ό μ‘°μ ν¨μΌλ‘μ¨ ννΌν¬κ³Όμ₯λ²½μ νμμ±μ μ μ§νλλ° κΈ°μ¬νλ€.νμλ£¨λ‘ μ°μ κΈμ± μ₯λ²½ μμμμ ννΌμμ κ·Έ λ°νμ΄ μ¦κ°λλ©° μμμ λ°μ ννΌμμλ νμλ£¨λ‘ μ° ν©μ±ν¨μ(hyaluronic acid synthase, HAS)2μ CD44κ° λμκ°λμ κΈ°μ μΈ΅κ³Ό 과립측μμ μ¦κ°νλ κ²μ΄ λ³΄κ³ λμλ€. μ΄λ μμ²μΉμ κ³Όμ μμ λΆλΉκ° μ¦κ°νλ ννΌμ±μ₯μΈμ, κ°μ§νμ±μΈν¬ μ±μ₯μΈμ, IL-1Ξ±, IFN- λ±μ μ±μ₯ μΈμμ μΈμ΄ν μΉ΄μΈμ΄ νμλ£¨λ‘ μ° ν©μ±ν¨μμ λ°νμ μ¦κ°μμΌ νμλ£¨λ‘ μ°μ ν©μ±μ μ¦κ°μν¨λ€κ³ μκ°λλ€.κ·Έλ¬λ μ΅κ·Ό μ°κ΅¬μ μνλ©΄ ννλ°νΌμ μ μ¬μ©λλ κΈλ¦¬μ½μ°(glycolic acid)μ κ΅μ λν¬μ ννΌμ μ§νΌ λͺ¨λμμ νμλ£¨λ‘ μ°μ μ¦κ°κ° λ³΄κ³ λμλλ°, κ·Έ μμ©κΈ°μ μ μ νν μλ €μ Έ μμ§ μμΌλ κΈλ¦¬μ½μ°μ νΌλΆμ₯λ²½ κΈ°λ₯μ μμ μμ΄λ μΉΌμ μ΄μ¨μ λ³νμ μΈ΅ν μ체μ λΆλΉ μ΄μ§μ μ λ°νλ©° μΈμ΄ν μΉ΄μΈμ λ³νλ₯Ό μ΄λν μ μμμ΄ λͺλͺ μ°κ΅¬μμ λ³΄κ³ λ λ° μμ΄ μ΄λ νμλ£¨λ‘ μ°μ μ¦κ°κ° νΌλΆμ₯λ²½μμμ΄ μλ λ€λ₯Έ κΈ°μ μ ν΅ν΄ μ΄λ£¨μ΄μ§ μλ μμ κ²μ΄λΌλ μꡬμ¬μ κ°μ§κ² νλ€. μ΅κ·Ό μ΄μν μλλ²μ΄ νΌλΆ μ₯λ²½μ λ―ΈμΉλ μν₯μ΄ κΈλ¦¬μ½μ°κ³Ό κ°μ κΈ°μ , μ¦ νΌλΆ μ₯λ²½ κΈ°λ₯μ μμ μμ΄ μΉΌμ μ΄μ¨ λ³νμ μΈμ΄ν μΉ΄μΈμ λ³ν, μΈ΅ν μ체μ λΆλΉλ₯Ό μ λνμ¬ νΌλΆ μ₯λ²½ ν볡 κΈ°μ μ μ λ°ν¨μ΄ λ³΄κ³ λκ³ μμ΄ λ³Έ μ°κ΅¬μμλ ννΌμ₯λ²½ κΈ°λ₯μ μμ μμ΄ μ΄μν μλλ²μ μ€μνμμ λ tape strippingμ μ΄μ©νμ¬ ννΌ μμμ μ λ°ν κ²½μ°μ λ§μ°¬κ°μ§λ‘ ννΌ νμλ£¨λ‘ μ°κ³Ό CD44μ λ°νμ΄ μ¦κ°νλμ§λ₯Ό μμλ³΄κ³ νμλ£¨λ‘ μ° ν©μ±μ μ‘°μ κΈ°μ μ μμλ³΄κ³ μ νμλ€. μ΄μν μλλ²μ 무λͺ¨μ₯μ μ€μν λ€ 6μκ° ν ννΌ νμλ£¨λ‘ μ°κ³Ό HAS3, CD44μ λ°νμ λͺ¨λ μ μλμ‘°κ΅°μ λΉν΄ μ¦κ°λ μμμ 보μμΌλ©° μ΄λ tape strippingμΌλ‘ ννΌ μμμ μΌμΌν¨ κ²½μ°μ μ μ¬νμλ€. λν μ΄μν μλλ² μνμ λΆλΉκ° μ¦κ°νλ λνμ μΈ μΈμ΄ν μΉ΄μΈμΈ TNF- , IL-1 κ° νμλ£¨λ‘ μ°κ³Ό CD44μ λ°νμ κΈ°μ κ³Ό μ°κ΄μ΄ μλμ§λ₯Ό μμ보기 μνμ¬ μμ©μ± TNF μμ©μ²΄ μ΅ν© λ¨λ°±κ³Ό IL-1 μμ©μ²΄ κΈΈνμ λ₯Ό μ¬μ©νμ¬ κ° μΈμ΄ν μΉ΄μΈμ μ΅μ ν ν μ΄μν μλλ²μ μνν κ²°κ³Ό νμλ£¨λ‘ μ°κ³Ό HAS3, CD44μ λ°νμ΄ λͺ¨λ μ μ²μΉ μμ΄ μ΄μν μλλ²λ§ μνν κ΅°μ λΉν΄ μλ―Έμκ² μ΅μ λ μμμ 보μλ€. TNF- λλ IL-1 μ μ¦κ°κ° μ΄μν μλλ²μ μν ννΌ νμλ£¨λ‘ μ°κ³Ό CD44μ λ°νμ κ΄μ¬νλμ§λ₯Ό μ§μ μ μΌλ‘ μ¦λͺ
νκΈ° μνμ¬ λ¬΄λͺ¨μ₯μ TNF- λλ IL-1 λ₯Ό λ€μν λλλ‘ μ§μ μ£Όμ
νκ³ 6μκ° νμ νμλ£¨λ‘ μ°κ³Ό HAS3, CD44μ λ°νμ΄ κ° μΈμ΄ν μΉ΄μΈμ ν¬μ¬ μ©λμ μμ‘΄μ μΌλ‘ μ¦κ°νλ μμμ 보μλ€. κ·Έλ¦¬κ³ μ΄μν μλλ²μ μν ννΌλ΄ μΉΌμμ΄μ¨ κΈ°μΈκΈ°μ λ³νκ° νμλ£¨λ‘ μ°κ³Ό CD44μ λ°νμ κΈ°μ κ³Ό μ°κ΄μ΄ μλμ§λ₯Ό μμ보기 μνμ¬ 1.5mMμ μΉΌμμ ν¬ν¨ν μ €κ³Ό μΉΌμμ΄ μ ν ν¬ν¨λμ§ μμ μ €μ κ°κ° λν¬νκ³ μ΄μν μλλ²μ μννμ¬ λ³Έ κ²°κ³Ό 1.5mMμ μΉΌμμ ν¬ν¨ν μ €μ μ¬μ©νμ¬ μ΄μν μλλ²μ μνν κ΅°μμλ μ΄μν μλλ²μμ κ΄μ°°λλ μΉΌμ μ΄μ¨ κΈ°μΈκΈ°μ λ³νκ° μ΅μ λλ μμμ΄ κ΄μ°°λμμΌλ©° νμλ£¨λ‘ μ°κ³Ό HAS3, CD44μ λ°νμ΄ μλ―Έμλ κ°μλ₯Ό 보μλ€. κ²°λ‘ μ μΌλ‘ κΈμ± μ₯λ²½μ μμμ΄ μμ΄λ μ΄μν μλλ²μ μν΄μ ννΌμ νμλ£¨λ‘ μ°κ³Ό κ·Έ μμ©μ²΄μΈ CD44μ λ°νμ΄ μ¦κ°λ¨μ νμΈνμκ³ , κ·Έ κΈ°μ μΌλ‘ μ΄μν μλλ²μ μν ννΌμ μΉΌμ μ΄μ¨ κΈ°μΈκΈ°μ λ³νμ μ΄μ μν TNF- μ IL-1 μ μ¦κ°κ° HAS3μ λ°νμ μ¦κ°μμΌ ννΌμ νμλ£¨λ‘ μ°μ ν©μ±μ μ¦κ°μν€λ©° λν κ·Έ μμ©μ²΄μΈ CD44 λ°νμ μ¦κ°μλ κ΄μ¬νλ€κ³ μΆμ ν΄ λ³Ό μ μλ€.
[μλ¬Έ]Hyaluronic acid (HA) is a major extracellular matrix component in the epidermis that plays a role in cellular migration, proliferation and differentiation through itβs major cell surface receptor, CD44. HA has been demonstrated to be accumulated in the epidermis by permeability barrier disruption. Previously we demonstrated that sonophoresis can modulate the epidermal calcium gradient and stimulate epidermal cytokine expressions. We performed this study to identify whether sonophoresis could increase the expression of HA and CD44 in mouse epidermis without barrier disruption and to uncover the mechanisms involved in the upregulation of HA and CD44 expression following sonophoresis. Sonophoresis without transepidermal water loss change significantly increased the HA expression in mouse epidermis at 6 h after sonophoresis compared to untreated skin as well as in tape-stripped skin used as a positive control. The increased expression of HA was temporally and locally associated with increased expression of hyaluronic acid synthase (HAS)3 and CD44. To test whether TNF-Ξ± and IL-1Ξ± may have a functional role in sonophoresis-induced increase of HA and CD44, we used TNF-Ξ± and IL-1 specific inhibitors and the expression of HA, HAS3, and CD44 showed significant inhibition at 6 h after sonophoresis in each cytokine inhibitor pretreated skin compared to the skin without pretreatment. To determine whether the epidermal calcium gradient changes may involve in the upregulation of HA and CD44 following sonophoresis, we compared HA, HAS3, and CD44 expression in epidermis treated with sonophoresis of Ca2+-free gel vs. Ca2+-containing gel. Ion capture cytochemistry revealed that sonophoresis of Ca2+-containing gel prevented the epidermal calcium gradient change by excess calcium at all levels of the epidermis. The expression of HA, HAS3, and CD44 mRNA and immunohistochemical protein staining decreased in the epidermis after sonophoresis of Ca2+-containing gel vs. Ca2+-free gel, suggesting that the change in calcium ion can stimulate the expression of HA, HAS3, and CD44 in epidermis. To determine whether the upregulation of HA and CD44 is stimulated by TNF-Ξ± and IL-1Ξ± directly, three different concentrations of TNF-Ξ± (50ng, 100ng, 300ng in 0.1ml PBS) or IL-1Ξ± (50ng, 100ng, 300ng in 0.1ml PBS) were injected intradermally into the flanks of hairless mouse. The results showed a dose dependent stimulation of HA, HAS3, and CD44 expression by both cytokines. From these results we can suggest that epidermal calcium gradient change and sequentially induced TNF-Ξ± and IL-1Ξ± by sonophoresis could upregulate the HA expression through HAS3 induction and CD44 expression without barrier impairment.ope
Diagnostic Accuracy of a Novel On-site Virtual Fractional Flow Reserve Parallel Computing System
PURPOSE:
To evaluate the diagnostic accuracy of a novel on-site virtual fractional flow reserve (vFFR) derived from coronary computed tomography angiography (CTA).
MATERIALS AND METHODS:
We analyzed 100 vessels from 57 patients who had undergone CTA followed by invasive FFR during coronary angiography. Coronary lumen segmentation and three-dimensional reconstruction were conducted using a completely automated algorithm, and parallel computing based vFFR prediction was performed. Lesion-specific ischemia based on FFR was defined as significant at β€0.8, as well as β€0.75, and obstructive CTA stenosis was defined that β₯50%. The diagnostic performance of vFFR was compared to invasive FFR at both β€0.8 and β€0.75.
RESULTS:
The average computation time was 12 minutes per patient. The correlation coefficient (r) between vFFR and invasive FFR was 0.75 [95% confidence interval (CI) 0.65 to 0.83], and Bland-Altman analysis showed a mean bias of 0.005 (95% CI -0.011 to 0.021) with 95% limits of agreement of -0.16 to 0.17 between vFFR and FFR. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 78.0%, 87.1%, 72.5%, 58.7%, and 92.6%, respectively, using the FFR cutoff of 0.80. They were 87.0%, 95.0%, 80.0%, 54.3%, and 98.5%, respectively, with the FFR cutoff of 0.75. The area under the receiver-operating characteristics curve of vFFR versus obstructive CTA stenosis was 0.88 versus 0.61 for the FFR cutoff of 0.80, respectively; it was 0.94 versus 0.62 for the FFR cutoff of 0.75.
CONCLUSION:
Our novel, fully automated, on-site vFFR technology showed excellent diagnostic performance for the detection of lesion-specific ischemia.ope
Health behaviors influencing depressive symptoms in older Koreans living alone: secondary data analysis of the 2014 Korean longitudinal study of aging
BACKGROUND:
Geriatric depression is a societal problem, specifically in those living alone in Korea. This study aims are to investigate (1) how sociodemographic factors, health status, and health behaviors are differently associated with depressive symptoms in older Koreans living alone compared to those living with others and (2) how living arrangements attenuated or strengthened the associations between four types of health behaviors and depressive symptoms.
METHODS:
This secondary data analysis was conducted using data from the 2014 Korean Longitudinal Study of Aging. A structured survey assessing sociodemographic factors, health status, and health behaviors was conducted with people aged 65 or older who lived alone (nβ=β1359) and living with others (nβ=β2864). A multiple linear regression with interaction terms was conducted between mean-centered health behaviors and the status of living alone. All statistical analyses were performed using SPSS Statistics 23.0, and the two-tailed level of significance was set at 0.05.
RESULTS:
Those living alone reported higher levels of depressive symptoms than those living with others (Mdiffβ=β2.129, SEβ=β0.005, pβ<β 0.001). The variance of depressive symptoms explained by 13 variables was 18.1% for those living alone compared to 23.7% for those living with others. Compared to health behaviors, sociodemographic factors and health status more explained depressive symptoms, specifically with psychiatric disorders, pain, and impaired functionality as risk factors. Smoking, alcohol abstinence, physical inactivity, and social inactivity were associated with more depressive symptoms. Living arrangements moderated the association between depressive symptoms and each health behavior, except for physical inactivity (all p values <β0.001).
CONCLUSIONS:
Older Koreans living alone were exposed to different risk factors for depressive symptoms compared to those living with others. Non-modifiable sociodemographic and health status factors were highly associated with depressive symptoms relative to health behaviors; thus, it is important to conduct early assessment and classification of vulnerable subgroups regarding geriatric depression. Specific assessment instruments should be prepared in practice according to living arrangements among older Koreans. Targeted interventions are essential to addressing living arrangements and modifying health behaviors to reduce smoking, alcohol consumption, and social inactivity, specifically in those living alone.ope
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