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  • 學位論文

慢性腎病篩檢方法的趨勢與床邊檢測的新型技術

Trends in screening tests for chronic kidney diseases and emerging technologies for smart point-of-care tests

指導教授 : 林啟萬

摘要


慢性腎病(chronic kidney disease, CKD)已成為世界性的公共衛生議題超過數十年,台灣也深受影響。依據美國腎病資料系統(United States Renal Dada System (USRDS)的統計資料顯示,台灣的慢性腎病末期(end-stage renal disease, ESRD)的盛行率及發生率高居全球40個國家與區域的第一名多年。相對的,慢性腎病的自知率(awareness)僅約8%,這可能是慢性末期腎病在台灣的高盛行率與發生率的主因之一。 造成慢性腎病的主要原因之一是腎絲球持續受損,腎臟切片的病理檢查是最為直接的證據。然而,此項高度侵入性的檢驗並不可能常規持續的執行,在日後的追蹤檢查通常僅依賴尿液或血液的常規檢查輔助。現有的檢驗方法中,對於腎功能的評估,最常見的檢查是血清中肌酸酐(serum creatinine)、尿液中尿蛋白的濃度與尿沉渣鏡檢。然而,此三項檢查並非直接檢驗腎絲球,而且這三項檢驗也大多集中在專業的醫學檢驗實驗室中執行,造成病患想進行檢驗的不便,這可能是造成慢性腎病的自知率偏低的主因。此外,尿沉渣鏡檢報告的現有品質,尚未達到臨床醫師的期望。 基於前述原因,吾人開發與改良四項檢驗方法與裝置以期提高檢驗的專一性、可親性與敏感性。第一項,以表面電漿子共振法(surface plasmon resonance, SPR),用於直接量測尿液中腎絲球之足細胞(podocyte)的表面蛋白分子podocalyxin。以Au/ZnO晶片配合GWC SPRimager可以量測到尿中podocalyxin濃度為1 ng/mL。第二項,以微流道(microfluidic channel)與毛細重力閥門(capillary-gravitational valve)開發出不含主動原件且可達成檢體定量與試劑分注及混合的可攜式尿中肌酸酐檢測晶片。與尿中肌酸酐的標準檢驗法比較,本晶片的檢驗偏差在正負10%以內。第三項,利用類神經網路(artificial neural networks, ANN)的人工智慧協助判斷尿液試紙中的檢驗結果,研判受測者的腎功能預測值(estimated glomerular filtration rate, eGFR),增加尿液化學分析法對於腎功能評估指標的運用方式。檢驗結果的正確性達0.879,類神經網路的評估指標(area under the curve, AUC)達0.928,敏感度(sensitivity)達0.83,專一性(specificity)達0.88。第四項,改善現有尿沉渣鏡檢的檢驗程序,提高腎絲球出血型紅血球(dysmorphic red blood cell, dysmorphic RBC)的回收率,由34.7%提升至42.0% (p <0.001),進而增加尿沉渣中的發現率,並以Sternheimer染色法增加尿沉渣中的細胞間對比度,以增進對於腎小管上皮細胞(renal tubular epithelial cell, RTEC)的鑑別度。 以表面電漿子共振法檢測尿液中之podocalyxin濃度,未來仍需以量化的製造法完成檢驗晶片,並找出可重複檢測的最佳反應條件,以達成檢驗方法的校正與量測的功能與應用。以微流道與毛細重力閥門開發出不具備主動原件且可達成檢體定量與試劑分注及混合的可攜式尿中肌酸酐檢測晶片,已具備發展成床邊檢驗(point-of-care test, POCT)的潛力,並已獲得中華民國專利I-446958。類神經網路用於尿液試紙中的檢驗結果,輔助研判受測者的腎功能預測值,增加尿液化學分析法對於腎功能評估指標的運用方式,並獲得中華民國專利M518332與日本專利6012781。關於尿沉渣鏡檢法的改良,目前已成為台灣醫檢驗驗學會的尿沉渣鏡檢指引TSLM-GP-U01(1)。

並列摘要


Chronic kidney disease (CKD) has been a has been a worldwide public issue for decades. Taiwanese also suffer from this disease. According to the data from United States Renal Data System (USRDS), the prevalence and incidence of end-stage renal disease (ESRD) are the highest in 40 countries and areas for years. In contrast, the awareness rate of CKD patients is only about 8%, which is relatively low. This depressed result might be a key factor for the high prevalence and incidence of ESRD. The major cause for CKD is a progressive damage to glomeruli. Currently, the renal biopsy is the most solid evidence for glomerular damage. However, this extremely invasive examination is with potential risks thus the renal biopsy is neither a regular examination nor an examination for follow-up. In practice, the follow-up for glomerular damage is usually based on urinary and blood tests. The most popular tests for the evaluation of kidney function are serum creatinine, urinary protein, and urine sediment microscopy. However, these tests are not direct methods to the glomerular damage, which are usually carried out in medical laboratories. In such a scenario, it could be a barrier to patients for a CKD screening examination, which would be a major cause for a low CKD awareness. Also, the current report quality of urine sediment microscopy is not meet the clinical need for physicians. Based on the previous reasons, we had developed and continuously improved four examinations and devices in order to increase the specificity, accessibility, and sensitivity of examinations for CKD screening. First, we applied the principle of surface plasmon resonance (SPR) to detect the urinary podocalyxin which is a surface protein of podocyte originating from the glomerulus. By an Au/ZnO chip with GWC SPRimager, the lower detection limit is 1 ng/mL. Second, we utilized microfluidic channels with capillary-gravitational valves to design a portable chip without active parts for the detection of urinary creatinine, which had multiple functions for sample quantifying, regent adding, and mixing. Compared with the standard method for the measurement of urinary creatinine, the result errors by this chip were within ±10%. Third, artificial neuro networks (ANN) was applied to judge the result of a urinary dipstick, which could estimate the estimated glomerular filtration rate (eGFR). This approach could generate a new application for the kidney function evaluation by a urinary dipstick. The accurate rate was 0.879 and the performance of ANN by area under the curve (AUC) was 0.928. The sensitivity and the specificity were 0.83 and 0.88, respectively. Fourth, we improved the procedures for urine sediment microscopy, which increased the recovery rate of dysmorphic red blood cell (RBC) from 34.7% to 42.0% ( p <0.001). This improvement increased the probability of finding dysmorphic RBC in urine sediment microscopy. Also, we applied the Sternheimer stain to increase the contrast between formed elements in urine sediment, which increased the capability to correctly identify renal tubular epithelial cell (RTEC) in urine sediment microscopy. For future works, the SPR sensor chip for urinary podocalyxin will need to be advanced studied for an optimal condition. This approach will establish a reusable chip, which can be carried out for the calibration procedure in order to measure the level of urinary podocalyxin. The portable chip with microfluidic channels and capillary-gravitational valves has serial functions for sample quantifying, reagent adding, and mixing, which has a potential for a point-of-care test (POCT) developing. And this innovation has a patent by Republic of China, I-446958. ANN has developed a new application for eGFR by a urinary dipstick, which has a good performance and gets two patents by Republic of China M518332 and Japan 6012781. About the improvement of urine sediment microscopy, this modified protocol for urine sediment microscopy has been adopted by Taiwan Society of Laboratory Medicine as the guideline of urine sediment microscopy, TSLM-GP-U01(1).

參考文獻


[12] "Medicalcare statistics of Taiwan Bureau of National Health Insurance " Taiwan Bureau of National Health Insurance, 2013.
[1] A. M. El Nahas and A. K. Bello, "Chronic kidney disease: the global challenge," The Lancet, vol. 365, pp. 331-340, 2005.
[2] "USRDS 2010 Annual Data Report," United States Renal Data System, pp. 223-238.
[3] " USRDS 2015 Annual Data Report," United States Renal Data System, pp. 291-334.
[5] A. S. Levey, K.-U. Eckardt, Y. Tsukamoto, A. Levin, J. Coresh, J. Rossert, D. D. Zeeuw, T. H. Hostetter, N. Lameire, and G. Eknoyan, "Definition and classification of chronic kidney disease: a position statement from Kidney Disease: Improving Global Outcomes (KDIGO)," Kidney international, vol. 67, pp. 2089-2100, 2005.

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