https://ir.lib.ncu.edu.tw/handle/987654321/364 Search the Channel s https://ir.lib.ncu.edu.tw/simple-search https://ir.lib.ncu.edu.tw/handle/987654321/110375 title: Photo-Responsive Oxidase-like Metal Nanocluster Nanozymes for Antioxidant Sensing and Redox Monitoring;Photo-Responsive Oxidase-like Metal Nanocluster Nanozymes for Antioxidant Sensing and Redox Monitoring abstract: 活性氧（Reactive Oxygen Species， ROS）所造成之氧化還原失衡與細胞損傷及多種疾病之發生與進展密切相關，因此，建立具高靈敏度與高選擇性的抗氧化劑檢測方法，對於生物醫學研究與臨床診斷具有重要意義。然而，由於現有分析方法對含硫醇類抗氧化劑（thiol-containing antioxidants）之響應能力不足，且在複雜生物環境中的應用受到限制，使得抗氧化劑含量之精確定量分析（尤其是氧化壓力監測）仍面臨重大挑戰。近年來，奈米酵素（nanozymes）因具有優異的穩定性、可調控之催化活性及多功能特性，已成為天然酵素之極具潛力的替代方案。 本論文旨在開發並系統性評估以奈米酵素為基礎之光響應催化平台，利用硫胺素（thiamine）之光催化氧化反應建立高效能抗氧化劑檢測方法，著重於提升分析效能、擴展檢測範圍，以及實現生物樣品與實際樣品之驗證應用。 第一部分研究建立了一套以二硫蘇糖醇（DTT）輔助之牛血清白蛋白穩定化金奈米簇（DTT@BSA-AuNCs）為基礎的光活化檢測平台，可於中性條件下進行抗氧化劑分析。該平台可有效檢測含硫醇及非含硫醇類抗氧化劑，並成功應用於癌細胞內抗氧化能力之分析。機制研究結果顯示，此系統遵循第二型（Type II）光敏化反應機制，以單態氧（¹O₂）為主要活性氧物種，負責催化硫胺素氧化反應。 第二部分研究進一步利用雙金屬金–銀奈米簇（AuAgNCs）擴展系統之分析能力與功能性應用，其中銀元素之導入可調控催化特性並提升催化效率。此平台可檢測多種具有生理相關性的抗氧化劑，並成功應用於模擬唾液、人類唾液及維生素 C 錠與果汁等市售抗氧化產品之總抗氧化能力（Total Antioxidant Capacity， TAC）分析。於人類唾液中進行加標回收試驗（spike-and-recovery）獲得近乎定量之回收率，顯示該方法受基質效應影響極低；此外，與傳統鐵離子還原抗氧化能力分析法（Ferric Reducing Antioxidant Power， FRAP）所得結果具有高度相關性（R² = 0.99），進一步驗證本方法之定量分析可靠性。同時，本研究亦將平台應用於細胞內氧化壓力變化、抗氧化劑介入、氧化壓力恢復過程，以及與非酒精性脂肪肝疾病（Non-Alcoholic Fatty Liver Disease， NAFLD）相關之脂肪變性病理模型分析。機制分析結果顯示，此系統遵循第一型（Type I）光敏化反應機制，並以過氧自由基（ROO•）作為主要活性氧物種。 綜上所述，本論文展示了奈米酵素光催化抗氧化劑檢測平台由概念驗證逐步發展至兼具廣泛分析功能與生物應用價值之完整研究歷程。所建立之奈米酵素催化平台具有優異之穩定性、高靈敏度及廣泛適用性，不僅可作為抗氧化劑分析之有效工具，亦可為生物體氧化還原調控機制及相關疾病研究提供重要之分析策略與應用價值。;Reactive oxygen species (ROS)-mediated oxidative imbalance is closely associated with cellular damage and the progression of various diseases， making the sensitive and selective detection of antioxidants essential for biomedical research and diagnostic applications. However， accurate quantification of antioxidant levels， which is critical for monitoring oxidative stress， remains challenging due to limitations of existing analytical methods， including poor responsiveness toward thiol-containing antioxidants and restricted applicability in complex biological environments. In this context， nanozyme-based catalytic systems have emerged as promising alternatives to natural enzymes owing to their superior stability， tunable catalytic activity， and functional versatility. This thesis focuses on development and systematic evaluation of advanced nanozyme-mediated catalytic platforms for efficient antioxidant detection through photo-responsive catalytic oxidation of thiamine， with emphasis on improving analytical performance， expanding detection scope， and enabling biological and real-sample validation. In the first study， a light-activated DTT-assisted BSA-stabilized gold nanocluster (DTT@BSA-AuNCs) system was developed for antioxidant detection under neutral conditions. The platform enabled efficient detection of both thiol and non-thiol antioxidants and was further validated for intracellular antioxidant detection in cancer cells. Mechanistic investigations revealed a Type II photosensitization pathway， where singlet oxygen (¹O₂) served as the dominant reactive species responsible for catalytic oxidation. In the second study， the analytical scope and functional applicability of the system were further expanded using bimetallic gold–silver nanoclusters (AuAgNCs)， where Ag incorporation modulated the catalytic properties and enhanced catalytic efficiency. This platform enabled the detection of a wide range of physiologically relevant antioxidants and total antioxidant capacity (TAC) assessment in complex matrices， including simulated saliva， human saliva， and commercial antioxidant-containing products such as vitamin C tablets and fruit juices. Analytical reliability was confirmed through spike-and-recovery experiments in human saliva with near-quantitative recoveries， indicating minimal matrix effects， while strong correlation with the Ferric Reducing Antioxidant Power (FRAP) assay (R² = 0.99)， a conventional standard antioxidant assay， validated its quantitative robustness. In addition， intracellular applications were extended to evaluate oxidative stress alterations， antioxidant intervention， recovery processes， and steatosis-associated pathological conditions relevant to non-alcoholic fatty liver disease (NAFLD). Mechanistic analysis indicated a Type I photosensitization pathway， with peroxyl radicals (ROO•) identified as the dominant reactive species. Overall， this thesis demonstrates a progressive evolution from feasibility demonstration to broadened analytical functionality and biological relevance. The developed nanozyme-based platforms provide robust， sensitive， and versatile tools for antioxidant detection and offer valuable insights into redox regulation in biological and disease-relevant systems. <br> Tue, 20 Oct 2026 07:35:33 GMT https://ir.lib.ncu.edu.tw/handle/987654321/110373 title: 顴骨植體與傳統牙科植體周圍微生物組差異及其臨床意義之研究;Comparative Analysis of Peri-Implant Microbial Composition and Diversity in Zygomatic and Conventional Dental Implants with Clinical Implications abstract: 牙科植體廣泛應用於重建缺牙患者之口腔功能與美觀。對於上顎嚴重萎縮或腫瘤切除後造成之骨缺損患者，顴骨植體（zygomatic implant， GZI）為重要之替代治療選項。然而，植體置入會改變口腔微生物生態，進而影響種植體周圍組織健康及相關併發症之發生。由於GZI常穿越或鄰近上頷竇，其微生物組成與傳統牙科植體（general dental implant， GDI）有所不同。本研究旨在比較GZI與GDI周圍微生物群落之差異。本研究招募於台北慈濟醫院牙科部接受GZI或GDI治療且整體健康狀況良好之成人受試者。在無菌條件下收集植體表面生物膜樣本，進行微生物DNA萃取，並採用16S核糖體核糖核酸(16S ribosomal RNA， 16S rRNA)基因擴增子定序分析菌相組成。透過生物資訊分析流程進行分類鑑定及多樣性分析，比較兩組間之微生物群落差異。結果顯示，GZI與GDI之微生物組成存在顯著差異。GZI組中Fusobacterium、Actinomyces、Streptococcus及Rothia之相對豐度顯著增加；而GDI組則以Fusobacterium及Actinomyces為主要優勢菌屬，顯示其菌相較為多樣。值得注意的是，與鼻竇感染相關之菌屬Rothia於GZI組中顯著增加，暗示與鼻竇相關微生物環境改變有關。綜上所述，植體類型會顯著影響種植體周圍之微生物組成。GZI因其特殊解剖位置，呈現不同於傳統植體之微生物特徵，並與鼻竇相關病理機制有關。未來仍需進一步研究以釐清其在種植體周圍疾病及鼻竇疾病發生中的角色與臨床意義。;Dental implants are extensively utilized to restore oral function and esthetics in edentulous patients. In cases of severe maxillary atrophy or post-resection bone defects， zygomatic implants (GZIs) represent a valuable alternative to conventional approaches. Nevertheless， implant placement may disrupt the oral microbial balance， potentially influencing peri-implant tissue health and the risk of associated complications. Given that GZIs frequently penetrate or lie in close proximity to the maxillary sinus， their microbiological profile may differ from that of conventional dental implants (general dental implants， GDIs). This study aimed to characterize and compare the peri-implant microbiota associated with GZIs and GDIs.Systemically healthy adult patients who underwent GZI or GDI placement at the Department of Dentistry， Taipei Tzu Chi Hospital， were enrolled. Biofilm samples were aseptically collected from implant surfaces， followed by microbial DNA extraction. Microbial communities were profiled using 16S rRNA gene amplicon sequencing. Downstream bioinformatics analyses were performed for taxonomic assignment and diversity assessment to evaluate differences between groups.Significant variations in microbial composition were observed between GZIs and GDIs. The GZI group exhibited increased relative abundances of Fusobacterium， Actinomyces， Streptococcus， and Rothia， whereas the GDI group was characterized by the predominance of Fusobacteriumand Actinomyces， reflecting comparatively higher microbial diversity. Of note， Rothia， which is commonly associated with sinus-related infections， was enriched in the GZI group， suggesting a potential association with sinus-linked microbial alterations.In summary， implant type plays a critical role in shaping peri-implant microbial communities. Owing to their distinct anatomical positioning， GZIs harbor a unique microbiological profile that may be connected to sinus-related pathophysiological processes. Further investigation is warranted to clarify their implications in peri-implant and sinus-associated diseases. <br> Tue, 20 Oct 2026 07:35:01 GMT https://ir.lib.ncu.edu.tw/handle/987654321/110370 title: 年輕軍人綜合性物質使用與胰島素抗性指數對新發代謝症候群之預測： CHIEF世代研究;Prediction of Incident Metabolic Syndrome by Combined Substance Use and Insulin Resistance Indices in Young Military Personnel: The CHIEF Cohort Study abstract: 代謝症候群 (metabolic syndrome， MetS) 是一組相互關聯的代謝異常危險因子群，包括腹部肥胖、血脂異常、高血壓及高血糖，顯著增加心血管疾病及第二型糖尿病的風險。然而，針對年輕且體能活躍之成年族群，其發展機制及預測因子仍缺乏充分研究。 本研究以心肺適能與東部軍人健康世代研究 (Cardiorespiratory Fitness and Health in Eastern Armed Forces， CHIEF) 為基礎，納入 2，890 名年齡 18 至 39 歲、基線無代謝症候群之軍人，追蹤至 2020 年底，探討綜合性物質使用（酒精、菸草、檳榔）及六種非胰島素測量胰島素抗性指數 (non-insulin-based insulin resistance indices， NI-IR indices) 與中年前新發代謝症候群之關聯性。 物質使用分析顯示，相較於未使用者，使用一、兩、三種物質者新發代謝症候群之風險比 (hazard ratio， HR) 分別為 1.27 (95%信賴區間：1.06-1.54)、1.38 (1.12-1.69) 及 1.78 (1.37-2.32)，呈劑量反應關係 (p 趨勢值<0.001)；單獨飲酒及吸菸分別增加 24%及 46%風險。胰島素抗性指數分析顯示，六種指數均與新發代謝症候群顯著正相關，其中胰島素抗性代謝評分 (METS-IR) 預測效能最佳，受試者工作特徵曲線下面積 (AUROC) 為 0.782 (0.762-0.801)，依序為三酸甘油酯/高密度脂蛋白膽固醇比值 (0.752)、浙江大學指數 (0.743)、三酸甘油酯葡萄糖指數 (0.734)、總膽固醇/高密度脂蛋白膽固醇比值 (0.731) 及丙氨酸轉氨酶/天門冬氨酸轉氨酶比值 (0.674)。性別分層分析顯示，女性以總膽固醇/高密度脂蛋白膽固醇比值預測效能最佳 (AUROC: 0.793)。 本研究證實綜合性物質使用與中年前新發代謝症候群呈劑量反應正相關，METS-IR 為預測年輕族群代謝症候群之最佳 NI-IR 指數。結果強調針對年輕軍人進行物質使用衛教及代謝風險早期篩檢之重要性，可作為制定預防策略之實證依據。;Metabolic syndrome (MetS) is a cluster of interrelated metabolic abnormalities， including abdominal obesity， dyslipidemia， hypertension， and hyperglycemia， which significantly increase the risk of cardiovascular disease and type 2 diabetes mellitus. The global prevalence of MetS has been rising steadily in recent years， making it a major public health concern. However， research on the developmental mechanisms and predictive factors of MetS in young， physically active adult populations remains insufficient. This study aimed to investigate the associations of combined substance use (alcohol， tobacco， and betel nut) and various non-insulin-based insulin resistance (NI-IR) indices with incident MetS before midlife among young military personnel. The study population was derived from the Cardiorespiratory Fitness and Health in Eastern Armed Forces (CHIEF) cohort study， which enrolled 2，890 military personnel aged 18-39 years without baseline MetS， with follow-up from 2014 to the end of 2020. The substance use analysis yielded significant findings. A clear dose-response relationship was demonstrated between combined substance use and incident MetS. Compared with non-users， the hazard ratios (HRs) for incident MetS were 1.27 (95% confidence interval [CI]: 1.06-1.54) for one substance use， 1.38 (95% CI: 1.12-1.69) for two substances use， and 1.78 (95% CI: 1.37-2.32) for three substances use (p for trend < 0.001). Individual substance analyses revealed that alcohol consumption increased the risk by 24%， while cigarette smoking increased the risk by 46%. Subgroup analysis showed that the association between substance use and MetS was more pronounced among participants without baseline low high-density lipoprotein cholesterol (HDL-C) (p for interaction < 0.05). Results from the insulin resistance indices analysis showed that all six NI-IR indices were significantly positively associated with incident MetS. In the predictive performance comparison， the Metabolic Score for Insulin Resistance (METS-IR) demonstrated the highest predictive capacity， with an area under the receiver operating characteristic curve (AUROC) of 0.782 (95% CI: 0.762-0.801)， significantly superior to other indices. The ranking order was: triglyceride to HDL-C ratio (0.752)， Zhejiang University index (0.743)， triglyceride-glucose index (0.734)， total cholesterol to HDL-C ratio (0.731)， and alanine transaminase to aspartate transaminase ratio (0.674). Sex-stratified analysis revealed that the total cholesterol to HDL-C ratio had the best predictive performance in women (AUROC: 0.793). In conclusion， this study confirms that combined substance use is significantly associated with incident MetS before midlife in a dose-response manner， and METS-IR is the optimal NI-IR index for predicting new-onset MetS in young populations. These findings emphasize the importance of substance use education and early metabolic risk screening among young military personnel， providing evidence-based references for the development of prevention strategies. <br> Tue, 20 Oct 2026 07:34:49 GMT https://ir.lib.ncu.edu.tw/handle/987654321/110368 title: CNEBat：提升非標靶代謝體學批次效應校正之液相層析雜訊消除前處理框架;CNEBat：提升非標靶代謝體學批次效應校正之液相層析雜訊消除前處理框架 abstract: 代謝體是生命體細胞代謝過程中所產生的小分子集合，能反映生物體在特定生理或病理狀態下的體內生化反應。近年來，代謝體已廣泛應用於生物標記開發、藥物代謝機轉分析與疾病特徵分析等領域，顯示其在生物醫學研究領域中的重要地位。在眾多代謝體分析平台中，液相層析串聯質譜儀（Liquid Chromatography-Tandem Mass Spectrometry， LC-MS/MS）因其具高靈敏度、對多樣性代謝物之良好分析能力等特點，已成為非標靶代謝體學中最重要的分析工具之一。然而，在使用液相層析串聯質譜儀分析大規模跨批次樣本時，資料常受到批次效應（Batch effect）的影響，導致實驗結果的可比較性與準確性大幅降低，因此如何有效控制批次效應的影響在代謝體質譜儀資料分析中尤為重要。在現有的批次效應校正流程中，已經有許多批次效應校正演算法被開發出來，但是並不是每個演算法的校正效果都非常好。為了提升批次效應校正效果，本研究提出一套液相層析雜訊消除演算法CNEBat（Chromatographic Noise Elimination for Batch Effect Correction），以去除液相層析過程因批次差異所造成之系統性偏差，並作為批次效應校正前之處理步驟，以提升後續批次效應校正之表現。CNEBat在雜訊估計與校正中，使用單一樣本之mzML原始資料分別建立各樣本之雜訊校正回歸模型，而非以整批樣本共同建立單一校正模型。為了評估CNEBat之效能，本研究使用多組不同性質之代謝體資料進行分析，包括脂質代謝體品質控制資料集、惡性瘧原蟲細胞代謝體資料集、老鼠大腦脂質代謝體與老鼠大腦極性代謝體資料集。於主成分分析（Principal Components Analysis，PCA）結果中，經CNEBat處理並結合現有批次效應校正方法後，樣本於批次間之分群現象明顯降低；在單一特徵峰強度分析中，不同批次之內部標準品及已知代謝物之強度分布穩定性在特定演算法中能獲得提升；在品質控制樣本皮爾森積差相關係數分析中，不同批次樣本之間之相關性明顯提升。前述結果顯示，經CNEBat前處理並結合現有批次效應校正方法後，資料之批次效應校正效果獲得有效改善。 綜合上述結果，CNEBat 能有效降低液相層析過程所造成之系統性雜訊，並可作為批次效應校正前處理之方法，提升後續分析之穩定性與可靠性。;Metabolomics represents the ultimate outcome of biological gene expression， providing the most intuitive reflection of biochemical reactions occurring within an organism. Compared to upstream products that are influenced by epigenomics， this characteristic has led to the increasing significance of metabolomics. Among various metabolomics analysis platforms， liquid chromatography-tandem mass spectrometry (LC-MS/MS) plays a crucial role in untargeted metabolomics research. However， when analyzing large-scale cross-batch samples using LC-MS/MS， batch effects often pose a significant challenge. Effectively correcting batch effects to ensure the accuracy of experimental data has thus become a critical issue. To address this challenge， we have proposed a liquid chromatography noise elimination algorithm named CNEBat (Chromatographic Noise Elimination for Batch Effect Correction)， designed to eliminate batch-induced errors occurring during the liquid chromatography (LC) process. Implemented in Java， CNEBat supports cross-platform operation and provides a highly compatible， user-friendly solution for liquid chromatography noise elimination. It performs noise calculation and removal using mzML data from a single sample， thereby preventing error correction caused by extreme values and ensuring optimal correction for each sample. After correction with CNEBat， users can further integrate the method with other batch effect correction algorithms as needed to achieve enhanced correction performance. Moreover， CNEBat operates solely based on mzML files and a user-defined Internal Standard List， allowing users to store computation coefficients from a single dataset. If users subsequently process data with other quantitative software， they can apply the same set of computation coefficients for rapid calculations， thereby improving compatibility across different quantification platforms and significantly reducing computational time. <br> Tue, 20 Oct 2026 07:34:48 GMT