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| 1 | A Markov chain model for subsurface characterization: Theory and applications显示文摘 | Elfeki A Dekking M | 2001 | Mathematical Geology2001，33，5： | 1 |
| 2 | A Markov Chain Model for subsurface characterization theory and applications 显示文摘 | Elfeki A Dekking M | | Mathematical Geology0，33，5： | 1 |
| 3 | Incremental, online, and merge mining of partial periodic patterns in time-series databases 显示文摘 | AREF W G ELFEKY M G ELMAGARMID A K | 2004 | IEEE Trans on Knowledge and Data Engineering2004，16，3： | 1 |
| 4 | Periodicity Detection in Time Series Databases显示文摘 | Elfeky M Aref W Elmagarmid A K | 2005 | IEEE Transactions on Knowledge and Data Engineering2005，17，7： | 1 |
| 5 | Incremental, Online, and Merge Mining of Partial Periodic Patterns in Time-Series Databases 显示文摘 | Aref W G Elfeky M G Elmagamlid A K | 2004 | IEEE Transactions on Knowledge and Data Engineering2004，16，3： | 1 |
| 6 | A Markov chain model for subsurface characterization :Theory and applications 显示文摘 | Elfeki A Dekking M | 2001 | Mathematical Geology2001，33，5： | 1 |
| 7 | Geostatistical analysis using GIS for mapping groundwater quality:case study in the recharge area of Wadi Usfan,western Saudi Arabia显示文摘 | MARKO K AL-AMRI N S ELFEKI A M M | 2013 | Arabian Journal of Geosciences2013，13，： | 1 |
| 8 | A Markov chain model for subsurface characterization: theory and application显示文摘 | Elfeki A Dekking M | 2001 | Mathematical Geology2001，33，5： | 1 |
| 9 | A Markov Chain model for subsur- face characterization: Theory and applications显示文摘 | Elfeki A Dekking M | 2001 | Mathe- matical Geology2001，33，5： | 1 |
| 10 | A Markov chain model for subsurface characterization:Theory and applications显示文摘 | Elfeki A Dekking M | 2001 | Mathematical Geology2001，33，5： | 1 |
| 11 | A Markov chain model for subsurface char- acterization: theory and applications 显示文摘 | Elfeki A Dekking M | 2001 | Math Geol2001，33，5： | 1 |
| 12 | A Markov chain model for subsurface characterization:Theory and applications显示文摘 | Elfeki A Dekking M | 2001 | Mathematical Geology2001，33，5： | 1 |
| 13 | A Markov chain model for subsurface characterization:Theory and applications显示文摘 | ELFEKI A DEKKING M | 2001 | Math Geol2001，33，5： | 1 |
| 14 | Incremental,Online,and Merge Mining of Partial Periodic Patterns in Time-series Databases显示文摘 | Aref W G Elfeky M G Elmagarmid A K | 2004 | IEEE Transactions on Knowledge and Data Engineering2004，16，3： | 1 |
| 15 | Incremental, Online, and Merge Mining of Partial Periodic Patterns in Time-series Databases显示文摘 | ArefW G Elfeky M G Elmagarmid A K | 2004 | IEEE Transactions on Knowledge and Data Engineering2004，16，3： | 1 |
| 16 | A pyridinium ca- tion-r~ interaction sensor for the fluorescent detection of alkyl halides显示文摘 | Chen W B Elfeky S A Nonne Y | 2011 | Chem Commun2011，47，： | 1 |
| 17 | ODMQL:Object data mining query language显示文摘 | Elfeky M G Saad A A Fouad S A | 2000 | Lecture Notes in Computer Science2000，1944，： | 1 |
| 18 | Incremental, Online and Merge Mining of Partial Periodic Patterns in Time Series Databases显示文摘 | Aref W G Elfeky M G Elmagarmid A K | 2004 | IEEE Transactions on Knowledge and Data Engineering2004，16，3： | 1 |
| 19 | Periodicity Detection in Time Series Databases显示文摘 | Elfeky M Aref W Elmagarmid A | 2005 | IEEE Transactions on Knowledge and Data Mining2005，17，7： | 1 |
| 20 | Role of endoscopic ultrasound and cyst fluid tumor markers in diagnosis of pancreatic cystic lesions显示文摘BACKGROUND Pancreatic cystic lesions(PCLs) are common in clinical practice. The accurate classification and diagnosis of these lesions are crucial to avoid unnecessary treatment of benign lesions and missed opportunities for early treatment of potentially malignant lesions.AIM To evaluate the role of cyst fluid analysis of different tumor markers such as cancer antigens [e.g., cancer antigen(CA)19-9, CA72-4], carcinoembryonic antigen(CEA), serine protease inhibitor Kazal-type 1(SPINK1), interleukin 1 beta(IL1-β), vascular endothelial growth factor A(VEGF-A), and prostaglandin E2(PGE2)], amylase, and mucin stain in diagnosing pancreatic cysts and differentiating malignant from benign lesions.METHODS This study included 76 patients diagnosed with PCLs using different imaging modalities. All patients underwent endoscopic ultrasound(EUS) and EUS-fine needle aspiration(EUS-FNA) for characterization and sampling of different PCLs.RESULTS The mean age of studied patients was 47.4 ± 11.4 years, with a slight female predominance(59.2%). Mucin stain showed high statistical significance in predicting malignancy with a sensitivity of 87.1% and specificity of 95.56%. It also showed a positive predictive value and negative predictive value of 93.1% and 91.49%, respectively(P < 0.001). We found that positive mucin stain, cyst fluid glucose, SPINK1, amylase, and CEA levels had high statistical significance(P < 0.0001). In contrast, IL-1β, CA 72-4, VEGF-A, VEGFR2, and PGE2 did not show any statistical significance. Univariate regression analysis for prediction of malignancy in PCLs showed a statistically significant positive correlation with mural nodules, lymph nodes, cyst diameter, mucin stain, and cyst fluid CEA. Meanwhile, logistic multivariable regression analysis proved that mural nodules, mucin stain, and SPINK1 were independent predictors of malignancy in cystic pancreatic lesions.CONCLUSION EUS examination of cyst morphology with cytopathological analysis and cyst fluid analysis could improve the differentiation between malignant and benign pancreatic cysts. Also, CEA, glucose, and SPINK1 could be used as promising markers to predict malignant pancreatic cysts. | Hussein Hassan Okasha Abeer Abdellatef Shaimaa Elkholy Mohamad-Sherif Mogawer Ayman Yosry Magdy Elserafy Eman Medhat Hanaa Khalaf Magdy Fouad Tamer Elbaz Ahmed Ramadan Mervat E Behiry Kerolis Y William Ghada Habib Mona Kaddah Haitham Abdel-Hamid Amr Abou-Elmagd Ahmed Galal Wael A Abbas Ahmed Youssef Altonbary Mahmoud El-Ansary Aml E Abdou Hani Haggag Tarek Ali Abdellah Mohamed A Elfeki Heba Ahmed Faheem Hani M Khattab Mervat El-Ansary Safia Beshir Mohamed El-Nady | 2022 | World Journal of Gastrointestinal Endoscopy2022，14，6： | 1 |
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