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우리나라의 구강보건서비스에 관한 연구 : 일본, 덴마크, 호주 비교제도론적 고찰을 중심으로
No full textMasterⅠ. 서론 1
A. 연구의 배경 및 필요성 1
B. 연구의 목적 4
Ⅱ. 연구 방법 5
Ⅲ. 결과 9
A. 일본 9
(1) 노인 인구 및 노인 구강보건 현황 9
(2) 노인 대상 구강보건서비스 10
(3) 재원조달 및 전달체계 13
(4) 노인 대상 구강보건서비스 성과 및 한계 15
B. 덴마크 16
(1) 노인 인구 및 노인 구강보건 현황 16
(2) 노인 대상 구강보건서비스 17
(3) 재원조달 및 전달체계 19
(4) 노인 대상 구강보건서비스 성과 및 한계 21
C. 호주 23
(1) 노인 인구 및 노인 구강보건 현황 23
(2) 노인 대상 구강보건서비스 24
(3) 재원조달 및 전달체계 25
(4) 노인 대상 구강보건서비스 성과 및 한계 26
D. 대한민국 27
(1) 노인 인구 및 노인 구강보건 현황 27
(2) 노인 대상 구강보건서비스 28
(3) 재원조달 및 전달체계 29
(4) 노인 대상 구강보건서비스 성과 및 한계 30
E. 국가별 비교분석 결과 32
(1) 노인 인구 및 노인 구강보건현황 비교 32
(2) 노인 대상 구강보건서비스 내용 및 범위 비교 33
(3) 구강보건서비스 재원조달방식 비교 34
(4) 구강보건서비스 전달체계 비교 34
(5) 구강보건서비스 성과 및 한계 비교 35
(6) 소결 37
Ⅳ. 고찰 38
Ⅴ. 결론 49
참고문헌 52
ABSTRACT 5
전신마취 및 회복에 관한 모바일 웹 기반 동영상 교육 프로그램이 위절제술 환자의 불확실성과 불안, 간호 교육 만족도에 미치는 효과
No full textMasterⅠ. 서론 1
1. 연구의 필요성 1
2. 연구 목적 4
3. 연구 가설 4
4. 용어 정의 5
Ⅱ. 문헌 고찰 7
1. 전신마취 하 수술 환자의 불확실성과 불안 7
2. 수술 환자 교육의 중요성 10
3. 모바일 웹 기반 동영상 매체를 활용한 환자 교육의 효과 12
Ⅲ. 연구 방법 15
1. 연구 설계 15
2. 연구 대상자 및 표본 크기 16
3. 측정 도구 17
4. 연구 진행 절차 20
5. 자료 수집 과정 25
6. 분석 방법 27
7. 연구 윤리 27
Ⅳ. 결과 29
1. 일반적 특성에 대한 실험군과 대조군의 동질성 검증 29
2. 종속 변수에 대한 실험군과 대조군의 동질성 검증 32
3. 가설 검증 33
Ⅴ. 논의 39
Ⅵ. 결론 및 제언 45
참고문헌 47
ABSTRACT 7
Application of a common data model-based multimodal integration method for the development of clinical prediction models in psychiatry
No full textDoctorI. Introduction 1
A. Background 1
1. Clinical prediction model in psychiatry 1
2. Multimodal integration method 4
3. Observational Medical Outcome Partnership Common Data Model 7
B. Objectives 9
II. Materials and Methods 10
A. Data sources 10
B. Feature extraction 11
C. Prediction tasks 19
D. Model development 21
E. External validation 23
F. Feature importance 27
III. Results 28
A. Demographic and clinical characteristics for study populations 28
B. Model specification 31
C. Model performance 35
D. Multimodal model performance 40
E. External validation 46
F. Feature importance 52
IV. Discussion 56
A. Main findings 56
1. Feature extraction pipeline for multi-modality based on the CDM 57
2. Model development through specific and diverse tasks 59
3. Performance evaluation of the multimodal integrative model using multiple modalities 59
4. External validation using multicenter data 62
5. Feature importance based on task and integration methods 63
B. Limitations 65
C. Further research 67
V. Conclusion 69
References 70
Appendix 80
국문요약 8
DNA Polymerase β 및 ATM 불활성화에 의해 유도되는 신호 전달 경로와 소뇌성 운동 실조와의 연관성
No full textMasterI. Introduction 1
II. Materials and Methods 4
1. Animal maintenance 4
2. Immunohistochemistry 5
3. Percoll gradient and Purkinje cell enrichment 6
4. Western blot 7
5. SMARTer RNA-seq 7
6. RNAseq analysis 8
7. Gene Ontology (GO) and Pathway enrichment analysis 8
8. Quantitative real-time polymerase chain reaction (qRT-PCR) 9
III. Results 10
1. p53 deficiency could not rescue ataxic phenotypes in PolbNes-CreAtm-/- mice. 10
2. p53 deficiency dose not contribute to anatomical defects in the cerebellum of PolbNes-CreAtm-/- mice. 15
3. Purkinje cells were efficiently enriched from the cerebellum of PolbNes-CreAtm-/- mice using Percoll gradient centrifugation. 18
4. Gene expression analysis of enriched Purkinje cells from PolbNes-CreAtm-/- mice revealed upregulation of inflammation-associated genes. 21
5. Gene expression analysis of enriched Purkinje cells from PolbNes-CreAtm-/- mice revealed downregulation of genes involved in the calcium homeostasis pathway. 31
6. Differential gene expressions were confirmed by qRT-PCR and Western blotting. 37
7. Immunohistochemistry shows the defects of gene expression in Purkinje cells regardless of p53 status, which the animal models display cerebellar ataxia. 41
IV. Discussion 44
V. References 47
국문 초록 5
척추체에서 에코 선택적 자기공명 T2* 이완과 전산화단층촬영 감쇠계수 간 상관관계 분석 : 후향적 예비 연구
No full textMaster제 1 장. 서론 1
제 2 장 대상과 방법 2
제 1절. 연구 대상자 2
제 2절. MRI 프로토콜 2
제 3절. 컴퓨터 단층 촬영 3
제 4절. 영상 분석 3
제 5절. T2* 신호의 계산 4
제 6절. 통계 분석 5
제 3 장. 결과 6
제 4 장. 고찰 8
제 5 장. 그림 차례 10
가. 그림 1. 연구 대상자 선정 기준: 포함 및 제외 조건 10
나. 그림 2. 다중 에코 딕슨 영상데이터, 전산화단층촬영(CT)에서 ROI 선정 및 T2* 신호값, 감쇠 계수(HU) 측정 11
다. 그림 3. L3 에서의 다중 에코 딕슨 영상데이터에서의 신호 강도 측정 및 각 에코에서의 신호 강도 그래프 12
라. 그림 4. 각 에코 쌍 선택에 따른 T2* 이완값과 CT 감쇠계수 간의 산포도 13
제 6 장. 표 차례 14
가. 표 1. MRI 프로토콜 14
나. 표 2. CT 감쇠계수와 에코 쌍 선택에 따른 T2* 이완값의 상관 관계 15
다. 표 3 각 척추체 위치에 따른 CT 감쇠계수와 에코 쌍 선택에 따른 T2* 이완값의 상관 관계 16
제 7 장. 참고문헌 17
제 8 장. 영어 초록 2
코로나바이러스 감염증 2019(COVID- 19) 완화를 위한 소독제의 안전성 평가
No full textMaster서론 1
연구 방법 4
결과 6
토의 9
결론 13
참고 문헌 1
Development and Validation of a Korean Trauma and Injury Severity Score (K-TRISS) Model for Predicting Trauma Outcomes
No full textBACKGROUND: Since the Trauma and Injury Severity Score (TRISS) was first developed in 1987, it has been widely used to predict trauma outcomes. However, numerous attempts have been made to adjust coefficients or develop new prediction models, as TRISS may not align with the local conditions. This study aimed to develop a Korean-TRISS (K-TRISS) model suitable for the Korean population. METHODS: We analyzed data from adult patients with blunt trauma in the Korea Trauma Data Bank (KTDB) from January 2017 to December 2021. A new set of TRISS coefficients (K-TRISS-1) was derived from the study data using a logistic regression method. We compared the predictive ability of the K-TRISS-1 with the previous Major Trauma Outcome Study (MTOS) in 1987 and the National Trauma Data Bank (NTDB) model in 2009. The predictive power of the models was evaluated with the area under receiver operating characteristic (AUROC) curves, and the validity was evaluated with the C-statistic and bootstrap verification methods. Additionally, we enhanced the model construction (K-TRISS-2) by converting the age variable from a categorical format to a continuous one. RESULTS: Overall, 128,534 cases were included in the statistical analysis. The comparison of AUROC values indicated that K-TRISS-1 (0.9232) outperformed MTOS (0.9210) and NTDB (0.9190), with their 95% confidence intervals showing some tendency (0.9196-0.9267, 0.9200-0.9230, and 0.9180-0.9210, respectively). However, K-TRISS-2 (0.9336, 0.9305-0.9367) had a significantly higher AUROC value compared to those of the others and showed excellent predictive power in the C-statistic and bootstrap tests. CONCLUSION: This study proposes the K-TRISS model, derived from the KTDB, which does not significantly differ from the previous models in terms of predictive power. Furthermore, we were able to construct a model demonstrating improved predictive power when converting age to a continuous variable
Age- and Sex-Specific Gut Microbiota Signatures Associated with Dementia-Related Brain Pathologies: An LEfSe-Based Metagenomic Study
No full textBACKGROUND/OBJECTIVES: Emerging evidence suggests that gut microbiota composition is influenced by both age and sex and may contribute to dementia-related brain pathologies. However, comprehensive microbiome-based biomarker discovery stratified by these factors remains limited. METHODS: We performed a metagenomic analysis of the gut microbiota of participants stratified by sex (female vs. male) and age (<75 vs. >/=75 years). Alpha diversity (observed operational taxonomic unit, Chao1, Shannon, and Simpson) and linear discriminant analysis effect size analyses were conducted to identify dominant taxa associated with Alzheimer's pathology, vascular pathology, and dementia-related structural brain changes. RESULTS: Females and non-elderly participants (aged < 75 years) exhibited higher gut microbial diversity, characterized by an increased abundance of Bifidobacterium spp. and Blautia spp., whereas males and elderly participants (aged >/= 75 years) exhibited increased levels of Bacteroides spp. and Bacteroidia, which have been associated with inflammation and dysbiosis. Several taxa, including Bifidobacterium spp. were consistently identified as potential protective biomarkers, while Bacteroides spp. was linked to a higher risk of dementia-related brain pathologies. CONCLUSIONS: Our findings demonstrate distinct age- and sex-specific differences in gut microbiota composition that may be closely associated with the pathophysiology of dementia-related brain pathologies. These results demonstrate that gut microbiota may serve as potential biomarkers for monitoring cerebrovascular conditions, potentially contributing to the development of personalized therapeutic strategies
A depression-associated protein FKBP5 functions in autophagy initiation through scaffolding the VPS34 complex
No full textCommon variants in the FKBP5 gene have been implicated in recurrence of major depressive disorder (MDD) and response to antidepressant treatment. Although the relationship between FKBP5 and MDD has been revealed through several studies, the detailed molecular mechanisms by which FKBP5 regulates responsiveness to antidepressants have not been fully understood. Here, we aimed to elucidate the molecular mechanisms of FKBP5 in autophagy initiation and its potential role in the antidepressant response. We found that FKBP5 deficiency impaired the initiation of basal and stress-induced autophagy, accompanied by reduced protein levels of the PIK3C3/VPS34 complex, which is essential for autophagy initiation. Mechanistically, we demonstrated that FKBP5 physically binds to the VPS34 complex components, facilitating their assembly and subsequent autophagy initiation. Particularly, our study revealed that FKBP5 mediates antidepressant-induced autophagy by promoting the VPS34 complex assembly. These findings were consistent in neuronal cells, where FKBP5 depletion resulted in decreased autophagy and impaired the VPS34 complex assembly. Understanding the interplay between FKBP5, autophagy, and MDD may provide new insights into more effective treatments for MDD and related disorders
A Deep Learning-Based Artificial Intelligence Model Assisting Thyroid Nodule Diagnosis and Management: Pilot Results for Evaluating Thyroid Malignancy in Pediatric Cohorts
No full textPurpose: Artificial intelligence (AI) models have shown promise in predicting malignant thyroid nodules in adults; however, research on deep learning (DL) for pediatric cases is limited. We evaluated the applicability of a DL-based model for assessing thyroid nodules in children. Methods: We retrospectively identified two pediatric cohorts (n = 128; mean age 15.5 +/- 2.4 years; 103 girls) who had thyroid nodule ultrasonography (US) with histological confirmation at two institutions. The AI-Thyroid DL model, originally trained on adult data, was tested on pediatric nodules in three scenarios axial US images, longitudinal US images, and both. We conducted a subgroup analysis based on the two pediatric cohorts and age groups (>/=14 years vs. < 14 years) and compared the model's performance with radiologist interpretations using the Thyroid Imaging Reporting and Data System (TIRADS). Results: Out of 156 nodules analyzed, 47 (30.1%) were malignant. AI-Thyroid demonstrated respective area under the receiver operating characteristic (AUROC), sensitivity, and specificity values of 0.913-0.929, 78.7-89.4%, and 79.8-91.7%, respectively. The AUROC values did not significantly differ across the image planes (all p > 0.05) and between the two pediatric cohorts (p = 0.804). No significant differences were observed between age groups in terms of sensitivity and specificity (all p > 0.05) while the AUROC values were higher for patients aged <14 years compared to those aged >/=14 years (all p < 0.01). AI-Thyroid yielded the highest AUROC values, followed by ACR-TIRADS and K-TIRADS (p = 0.016 and p < 0.001, respectively). Conclusion: AI-Thyroid demonstrated high performance in diagnosing pediatric thyroid cancer. Future research should focus on optimizing AI-Thyroid for pediatric use and exploring its role alongside tissue sampling in clinical practice