慢性腎臟病 (chronic kidney disease, CKD) 為全球重要的公共健康議題，患者因腎功能逐漸惡化，而無法有效排除體內各類代謝廢物，長期下來不斷累積之尿毒症毒素將對健康造成危害，進一步影響病患的預後及生活品質。中分子尿毒症毒素如β2-微球蛋白 (β2-microglobulin, β2-m) 與瘦素 (leptin)，因其分子量較大而難以有效地藉一般透析療法清除，因此需要具靈敏度之檢測分析技術以掌握其於透析過程中的變化。本研究應用表面增顯拉曼散射光譜 (surface-enhanced Raman scattering, SERS) 技術，結合纖維狀二氧化鋯基板 (Au NPs/fZrO2) 和碗狀二氧化鋯基板 (Au NPs/pZrO2)，及以共還原法製備並經化學修飾之銀奈米粒子 (mAg NPs) 膠體溶液的使用，進行β2-m 與 leptin 標準品及臨床透析廢液 (spent dialysate) 樣本之光譜特徵分析。經比較不同檢測系統下之訊號表現，確認 mAg NPs 於目標蛋白質的檢測中，除作為主要訊號增顯效果的貢獻來源之外，也進一步提升訊號的穩定性。並在針對β2-m 與 leptin 標準品光譜進行特徵峰歸納與主成分分析 (principal component analysis, PCA) 後，劃分具代表性之特徵區域，並以加標樣品 (spiked samples) 光譜資料建立判別目標蛋白質之機器學習分類模型。模型訓練策略包含全光譜與特徵區域兩種資料輸入方式，藉全光譜模型初步探討正規化方法、正負樣本比例以及演算法的不同對於模型表現的影響，並進一步觀察不同特徵區域模型之間的表現差異，篩選出具辨識能力之區域以確保判別不受分類表現較差之區域模型影響。研究結果顯示，雖然全光譜模型整體分類表現優於特徵區域模型，然而部分經由分析化學方法與統計分析歸納之特徵區域仍展現良好辨識能力，有助於提升機器學習模型之可解釋性。本研究驗證 SERS 與機器學習結合於目標蛋白質檢測之可行性，並為未來於臨床環境下對目標物進行檢測和分析提供檢測策略與應用基礎。

This study examines the detection of β2-microglobulin (β2-m) and leptin—important middle molecular weight uremic toxins—using Surface-Enhanced Raman Spectroscopy (SERS) paired with machine learning for clinical dialysis fluid analysis. Three SERS substrates were tested, with iodine-modified silver nanoparticles offering the best signal stability. Characteristic spectral peaks were identified through peak fitting and principal component analysis, which informed the classification models. Optimal performance was achieved with z-score normalization and a 2:1 positive-to-negative sample ratio. Models based on full spectra outperformed those from selected spectral regions, although narrower regions improved sensitivity for positive samples. Support vector machines (SVM) and k-nearest neighbors (KNN) yielded the best classification results, and KNN models could match or surpass traditional methods in detecting leptin using external clinical samples. This study highlights the challenges of complex clinical matrices while showing the promise of integrating SERS with machine learning to enhance detection and classification of uremic toxins in practical applications.

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