利用質譜儀資料快速檢測金黃色葡萄球菌之抗藥性

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姓名

翁菁美(Jing-Mei Weng) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

利用質譜儀資料快速檢測金黃色葡萄球菌之抗藥性

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摘要(中)

金黃色葡萄球菌（Staphylococcus aureus）是一種革蘭氏陽性球型細菌，為常見感染菌種之一，其中，有「超級細菌」之稱的耐甲氧西林金黃色葡萄球菌對大部分的青黴素類抗生素皆會產生抗藥性，如未能及早施用正確抗生素治療，嚴重可能導致死亡。傳統微生物檢驗方法檢測抗藥性需要數天，無法及時給予最適當之抗生素，因此，及時提供抗藥性之訊息，並施用適當的藥物治療，可降低死亡率以及避免抗藥性之發生。臨床微生物檢驗近年來已廣泛採用基質輔助雷射脫附電離飛行時間質譜法（Matrix-Assisted Laser Desorption Ionization-Time-of- Flight Mass Spectrometry，MALDI-TOF MS）進行微生物之鑑定，許多研究亦依此資料辨識細菌之抗藥性，然而，目前仍缺乏以大量臨床數據建構辨識金黃色葡萄球菌抗藥模型，本研究藉由長庚醫院多年蒐集之臨床金黃色葡萄球菌質譜資料，結合機器學習快速分類此菌株對苯唑青黴素、克林達黴素與紅黴素的抗藥情形。本研究採用決策樹、隨機森林與支持向量機建構辨識模型，其中，隨機森林分類器輔以向前特徵選取法所建構之模型準確率最高。辨識苯唑青黴素、克林達黴素與紅黴素抗藥性在獨立測試集的準確率分別為80.56%、82.42%與74.71%。本研究根據質譜資料所建立之辨識金黃色葡萄球菌抗藥模型可及時提供臨床醫師施用抗生素之相關資訊，進而提供病人更適切的照護。

摘要(英)

Staphylococcus aureus is a Gram-positive globular bacterium that is a flora common to the epidermis, but often causes opportunistic infections and is one of the common infectious strains. Among them, methicillin-resistant Staphylococcus aureus, known as "superbug", is a common infectious strain and is resistant to most penicillin antibiotics. Traditional identification of drug resistance often takes about three days, which is usually dependent on the physician′s empirical therapy. If the drug resistance is identified early and treated with appropriate drugs, it will greatly improve the effect of treatment. The purpose of this study was to use the mass spectrum of the strain obtained by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry, and combine the machine learning to quickly classify the resistance of this strain to oxacillin, clindamycin and erythromycin. This study uses decision trees, random forests and support vector machine classifiers to make prediction models, and finds that using random forest classifiers with sequential forward selection to select features can have the highest accuracy. The accuracy of predicting oxacillin, clindamycin and erythromycin resistance on independent test set were 80.56%, 82.42% and 74.71%, respectively. The results predicted by our model can provide a timely reference for clinicians, and provide more appropriate care for patients.

關鍵字(中)

★ 質譜分析
★ 金黃色葡萄球菌
★ 抗生素
★ 抗藥
★ 機器學習

關鍵字(英)

★ machine learning
★ MALDI-TOF MS
★ antibiotics
★ Staphylococcus aureus

論文目次

中文摘要 i
Abstract ii
致謝 iii
Table of Contents iv
List of Figures vi
List of Tables vii
Chapter 1 Introduction 1
1.1 Background 1
1.2 Motivation 4
1.3 Goal 4
1.4 Related Works 4
Chapter 2 Materials and Methods 6
2.1 Bacterial Isolates 6
2.2 MALDI-TOF MS Spectra Acquisition and Analysis 7
2.3 Data Processing 8
2.3.1 Kernel Density Estimation 8
2.3.2 Alignment 9
2.3.3 Intensity Information Processing 10
2.4 Models Building 10
2.4.1 Decision Tree 11
2.4.2 Random Forest 11
2.4.3 Linear Support Vector Machine 13
2.5 Measurements 14
2.5.1 Evaluation Metrics 14
2.5.2 Statistical Analysis 16
Chapter 3 Results and Discussions 17
3.1 Data Overview 17
3.1.1 Peak Counts 17
3.1.2 Peaks Distribution 19
3.1.3 Peaks Intensity 22
3.2 Determination of Parameters in KDE 25
3.3 Performances of Models 29
3.4 Forward Feature Selection 30
3.5 Investigation of Multidrug Resistance 43
Chapter 4 Conclusions 45
References 46

參考文獻

[1] M. Wolters et al., "MALDI-TOF MS fingerprinting allows for discrimination of major methicillin-resistant Staphylococcus aureus lineages," Int J Med Microbiol, vol. 301, no. 1, pp. 64-8, Jan 2011.
[2] O. Clerc et al., "Matrix-assisted laser desorption ionization time-of-flight mass spectrometry and PCR-based rapid diagnosis of Staphylococcus aureus bacteraemia," Clin Microbiol Infect, vol. 20, no. 4, pp. 355-60, Apr 2014.
[3] C. A. Mather et al., "Rapid detection of vancomycin-intermediate Staphylococcus aureus by matrix-assisted laser desorption ionization-time of flight mass spectrometry," J Clin Microbiol, vol. 54, no. 4, pp. 883-90, Apr 2016.
[4] C. K. Naber, "Staphylococcus aureus bacteremia: Epidemiology, pathophysiology, and management strategies," Clinical Infectious Diseases, vol. 48, no. Supplement_4, pp. S231-7, 2009.
[5] G. A. Noskin et al., "The burden of Staphylococcus aureus infections on hospitals in the United States: an analysis of the 2000 and 2001 nationwide inpatient Sample database," JAMA Internal Medicine, vol. 165, no. 15, pp. 1756-61, 2005.
[6] F. D. Lowy, "Antimicrobial resistance: the example of Staphylococcus aureus,", The Journal of clinical investigation, vol. 111, no. 9, pp. 1265-73, 2003.
[7] M. Josten et al., "Analysis of the matrix-assisted laser desorption ionization-time of flight mass spectrum of Staphylococcus aureus identifies mutations that allow differentiation of the main clonal lineages," J Clin Microbiol, vol. 51, no. 6, pp. 1809-17, Jun 2013.
[8] P. Lasch et al., "Insufficient discriminatory power of MALDI-TOF mass spectrometry for typing of Enterococcus faecium and Staphylococcus aureus isolates," J Microbiol Methods, vol. 100, pp. 58-69, May 2014.
[9] C. Ostergaard et al., "Rapid first-line discrimination of methicillin resistant Staphylococcus aureus strains using MALDI-TOF MS," Int J Med Microbiol, vol. 305, no. 8, pp. 838-47, Dec 2015.
[10] T. Zhang et al., "Analysis of methicillin-resistant Staphylococcus aureus major clonal lineages by matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS)," J Microbiol Methods, vol. 117, pp. 122-7, Oct 2015.
[11] M. Camoez et al., "Automated categorization of methicillin-resistant Staphylococcus aureus clinical isolates into different clonal complexes by MALDI-TOF mass spectrometry," Clin Microbiol Infect, vol. 22, no. 2, pp. 161 e1-7, Feb 2016.
[12] H. Y. Wang et al., "A new scheme for strain typing of methicillin-resistant Staphylococcus aureus on the basis of matrix-assisted laser desorption ionization time-of-flight mass spectrometry by using machine learning approach," PLoS One, vol. 13, no. 3, p. e0194289, 2018.
[13] V. E. Jones et al., "Rapid discrimination between methicillin-sensitive and methicillin-resistant Staphylococcus aureus by intact cell mass spectrometry," Medical Microbiology, vol. 49, no. 3, pp. 295-300, 2000.
[14] J. J. Lu et al., "Peptide biomarker discovery for identification of methicillin-resistant and vancomycin-intermediate Staphylococcus aureus strains by MALDI-TOF," Anal Chem, vol. 84, no. 13, pp. 5685-92, Jul 3 2012.
[15] Y. R. Wang et al., "Characterization of Staphylococcus aureus isolated from clinical specimens by matrix assisted laser desorption/ionization time-of-flight mass spectrometry," Biomed Environ Sci, vol. 26, no. 6, pp. 430-6, Jun 2013.
[16] K. Sogawa et al., "Rapid discrimination between methicillin-sensitive and methicillin-resistant Staphylococcus aureus using MALDI-TOF mass spectrometry," Biocontrol Science, vol. 22, no. 3, pp. 163-169, 2017.
[17] S. M. Lin et al., "Characterising phase variations in MALDI-TOF data and correcting them by peak alignment," Cancer informatics, vol. 1, no. 1, pp. 32-40, 2007.
[18] N. AlMasoud et al., "Optimization of matrix assisted desorption/ionization time of flight mass spectrometry (MALDI-TOF-MS) for the characterization of Bacillus and Brevibacillus species," Analytica chimica acta, vol. 840, pp. 49-57, 2014.
[19] S. J. Sheather et al., "A reliable data-based bandwidth selection method for kernel density estimation," Journal of the Royal Statistical Society. Series B (Methodological), vol. 53, no. 3, pp. 683-90, 1991.
[20] E. Jones et al., "SciPy: open source scientific tools for Python," 2001.
[21] B. A. Turlach, "Bandwidth selection in kernel density estimation: A review," CORE and Institut de Statistique, 1993.
[22] D. M. Bashtannyk et al., "Bandwidth selection for kernel conditional density estimation," vol. 36, no. 3, pp. 279-98, 2001.
[23] S. Baumann et al., "Standardized approach to proteome profiling of human Serum based on magnetic bead separation and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry," Clinical Chemistry, vol. 51, no. 6, p. 973, 2005.
[24] L. Breiman, Classification and regression trees. Routledge, 2017.
[25] F. Pedregosa et al., "Scikit-learn: machine learning in Python," vol. 12, no. Oct, pp. 2825-30, 2011.
[26] A. P. Bradley, "The use of the area under the ROC curve in the evaluation of machine learning algorithms," Pattern Recognition, vol. 30, no. 7, pp. 1145-59, 1997.
[27] K. Pearson, "X. On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling," The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, vol. 50, no. 302, pp. 157-75, 1900.
[28] J. F. Box, "Guinness, gosset, fisher, and small samples," Statist. Sci., vol. 2, no. 1, pp. 45-52, 1987.

指導教授

洪炯宗吳立青

審核日期

2019-7-10

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