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6. Whole-lesion apparent diffusion coefficient histogram analysis: significance in T and N staging of gastric cancers.

Author

Song Liu, Yujuan Zhang, Ling Chen, Wenxian Guan, Yue Guan, Yun Ge, Jian He, Zhengyang Zhou, Liu, Song, Zhang, Yujuan, Chen, Ling, Guan, Wenxian, Guan, Yue, Ge, Yun, He, Jian, and Zhou, Zhengyang

Subjects
GASTRIC diseases, RECEIVER operating characteristic curves, MAGNETIC resonance imaging, CROSS-sectional imaging, DIAGNOSTIC imaging
Abstract

Background: Whole-lesion apparent diffusion coefficient (ADC) histogram analysis has been introduced and proved effective in assessment of multiple tumors. However, the application of whole-volume ADC histogram analysis in gastrointestinal tumors has just started and never been reported in T and N staging of gastric cancers.Methods: Eighty patients with pathologically confirmed gastric carcinomas underwent diffusion weighted (DW) magnetic resonance imaging before surgery prospectively. Whole-lesion ADC histogram analysis was performed by two radiologists independently. The differences of ADC histogram parameters among different T and N stages were compared with independent-samples Kruskal-Wallis test. Receiver operating characteristic (ROC) analysis was performed to evaluate the performance of ADC histogram parameters in differentiating particular T or N stages of gastric cancers.Results: There were significant differences of all the ADC histogram parameters for gastric cancers at different T (except ADCmin and ADCmax) and N (except ADCmax) stages. Most ADC histogram parameters differed significantly between T1 vs T3, T1 vs T4, T2 vs T4, N0 vs N1, N0 vs N3, and some parameters (ADC5%, ADC10%, ADCmin) differed significantly between N0 vs N2, N2 vs N3 (all P < 0.05). Most parameters except ADCmax performed well in differentiating different T and N stages of gastric cancers. Especially for identifying patients with and without lymph node metastasis, the ADC10% yielded the largest area under the ROC curve of 0.794 (95% confidence interval, 0.677-0.911). All the parameters except ADCmax showed excellent inter-observer agreement with intra-class correlation coefficients higher than 0.800.Conclusion: Whole-volume ADC histogram parameters held great potential in differentiating different T and N stages of gastric cancers preoperatively. [ABSTRACT FROM AUTHOR]

Published
2017
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7. Challenges and strategies for implementing genomic services in diverse settings: experiences from the Implementing GeNomics In pracTicE (IGNITE) network.

Author

Sperber, Nina R., Carpenter, Janet S., Cavallari, Larisa H., Damschroder, Laura J., Cooper-DeHoff, Rhonda M., Denny, Joshua C., Ginsburg, Geoffrey S., Guan, Yue, Horowitz, Carol R., Levy, Kenneth D., Levy, Mia A., Madden, Ebony B., Matheny, Michael E., Pollin, Toni I., Pratt, Victoria M., Rosenman, Marc, Voils, Corrine I., Weitzel, Kristen W., Wilke, Russell A., and Wu, R. Ryanne

Subjects
GENOMICS, PUBLIC health, MEDICAL care, STAKEHOLDERS
Abstract

Background: To realize potential public health benefits from genetic and genomic innovations, understanding how best to implement the innovations into clinical care is important. The objective of this study was to synthesize data on challenges identified by six diverse projects that are part of a National Human Genome Research Institute (NHGRI)-funded network focused on implementing genomics into practice and strategies to overcome these challenges. Methods: We used a multiple-case study approach with each project considered as a case and qualitative methods to elicit and describe themes related to implementation challenges and strategies. We describe challenges and strategies in an implementation framework and typology to enable consistent definitions and cross-case comparisons. Strategies were linked to challenges based on expert review and shared themes. Results: Three challenges were identified by all six projects, and strategies to address these challenges varied across the projects. One common challenge was to increase the relative priority of integrating genomics within the health system electronic health record (EHR). Four projects used data warehousing techniques to accomplish the integration. The second common challenge was to strengthen clinicians' knowledge and beliefs about genomic medicine. To overcome this challenge, all projects developed educational materials and conducted meetings and outreach focused on genomic education for clinicians. The third challenge was engaging patients in the genomic medicine projects. Strategies to overcome this challenge included use of mass media to spread the word, actively involving patients in implementation (e.g., a patient advisory board), and preparing patients to be active participants in their healthcare decisions. Conclusions: This is the first collaborative evaluation focusing on the description of genomic medicine innovations implemented in multiple real-world clinical settings. Findings suggest that strategies to facilitate integration of genomic data within existing EHRs and educate stakeholders about the value of genomic services are considered important for effective implementation. Future work could build on these findings to evaluate which strategies are optimal under what conditions. This information will be useful for guiding translation of discoveries to clinical care, which, in turn, can provide data to inform continual improvement of genomic innovations and their applications. [ABSTRACT FROM AUTHOR]

Published
2017
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8. Proceedings from the 9th annual conference on the science of dissemination and implementation: Washington, DC, USA. 14-15 December 2016

Author

Chambers, David, Simpson, Lisa, Neta, Gila, Schwarz, Ulrica von Thiele, Percy-Laurry, Antoinette, Aarons, Gregory A., Brownson, Ross, Vogel, Amanda, Stirman, Shannon Wiltsey, Sherr, Kenneth, Sturke, Rachel, Norton, Wynne E., Varley, Allyson, Vinson, Cynthia, Klesges, Lisa, Heurtin-Roberts, Suzanne, Massoud, M. Rashad, Kimble, Leighann, Beck, Arne, Neely, Claire, Boggs, Jennifer, Nichols, Carmel, Wan, Wen, Staab, Erin, Laiteerapong, Neda, Moise, Nathalie, Shah, Ravi, Essock, Susan, Handley, Margaret, Jones, Amy, Carruthers, Jay, Davidson, Karina, Peccoralo, Lauren, Sederer, Lloyd, Molfenter, Todd, Scudder, Ashley, Taber-Thomas, Sarah, Schaffner, Kristen, Herschell, Amy, Woodward, Eva, Pitcock, Jeffery, Ritchie, Mona, Kirchner, JoAnn, Moore, Julia E., Khan, Sobia, Rashid, Shusmita, Park, Jamie, Courvoisier, Melissa, Straus, Sharon, Blonigen, Daniel, Rodriguez, Allison, Manfredi, Luisa, Nevedal, Andrea, Rosenthal, Joel, Smelson, David, Timko, Christine, Stadnick, Nicole, Regan, Jennifer, Barnett, Miya, Lau, Anna, Brookman-Frazee, Lauren, Guerrero, Erick, Fenwick, Karissa, Kong, Yinfei, Aarons, Gregory, Lengnick-Hall, Rebecca, Henwood, Benjamin, Sayer, Nina, Rosen, Craig, Orazem, Robert, Smith, Brandy, Zimmerman, Lindsey, Lounsbury, David, Kimerling, Rachel, Trafton, Jodie A., Lindley, Steven, Bhargava, Rahul, Roberts, Hal, Gibson, Laura, Escobar, Gabriel J., Liu, Vincent, Turk, Benjamin, Ragins, Arona, Kipnis, Patricia, Gruszkowski, Ashley Ketterer, Kennedy, Michael W., Drobek, Emily Rentschler, Turgeman, Lior, Milicevic, Aleksandra Sasha, Hubert, Terrence L., Myaskovsky, Larissa, Tjader, Youxu C., Monte, Robert J., Sapnas, Kathryn G., Ramly, Edmond, Lauver, Diane R, Bartels, Christie M, Elnahal, Shereef, Ippolito, Andrea, Peabody, Hillary, Clancy, Carolyn, Cebul, Randall, Love, Thomas, Einstadter, Douglas, Bolen, Shari, Watts, Brook, Yakovchenko, Vera, Park, Angela, Lukesh, William, Miller, Donald R., Thornton, David, Drainoni, Mari-Lynn, Gifford, Allen L., Smith, Shawna, Kyle, Julia, Bauer, Mark S, Eisenberg, Daniel, Liebrecht, Celeste, Barbaresso, Michelle, Kilbourne, Amy, Park, Elyse, Perez, Giselle, Ostroff, Jamie, Greene, Sarah, Parchman, Michael, Austin, Brian, Larson, Eric, Ferreri, Stefanie, Shea, Chris, Smith, Megan, Turner, Kea, Bacci, Jennifer, Bigham, Kyle, Curran, Geoffrey, Frail, Caity, Hamata, Cory, Jankowski, Terry, Lantaff, Wendy, McGivney, Melissa Somma, Snyder, Margie, McCullough, Megan, Gillespie, Chris, Petrakis, Beth Ann, Jones, Ellen, Lukas, Carol VanDeusen, Rose, Adam, Shoemaker, Sarah J., Thomas, Jeremy, Teeter, Benjamin, Swan, Holly, Balamurugan, Appathurai, Lane-Fall, Meghan, Beidas, Rinad, Di Taranti, Laura, Buddai, Sruthi, Hernandez, Enrique Torres, Watts, Jerome, Fleisher, Lee, Barg, Frances, Miake-Lye, Isomi, Olmos, Tanya, Chuang, Emmeline, Rodriguez, Hector, Kominski, Gerald, Yano, Becky, Shortell, Stephen, Hook, Mary, Fleisher, Linda, Fiks, Alexander, Halkyard, Katie, Gruver, Rachel, Sykes, Emily, Vesco, Kimberly, Beadle, Kate, Bulkley, Joanna, Stoneburner, Ashley, Leo, Michael, Clark, Amanda, Smith, Joan, Smyser, Christopher, Wolf, Maggie, Trivedi, Shamik, Hackett, Brian, Rao, Rakesh, Cole, F. Sessions, McGonigle, Rose, Donze, Ann, Proctor, Enola, Mathur, Amit, Gakidou, Emmanuela, Gloyd, Stephen, Audet, Carolyn, Salato, Jose, Vermund, Sten, Amico, Rivet, Smith, Stephanie, Nyirandagijimana, Beatha, Mukasakindi, Hildegarde, Rusangwa, Christian, Franke, Molly, Raviola, Giuseppe, Cummings, Matthew, Goldberg, Elijah, Mwaka, Savio, Kabajaasi, Olive, Cattamanchi, Adithya, Katamba, Achilles, Jacob, Shevin, Kenya-Mugisha, Nathan, Davis, J. Lucian, Reed, Julie, Ramaswamy, Rohit, Parry, Gareth, Sax, Sylvia, Kaplan, Heather, Huang, Keng-yen, Cheng, Sabrina, Yee, Susan, Hoagwood, Kimberly, McKay, Mary, Shelley, Donna, Ogedegbe, Gbenga, Brotman, Laurie Miller, Kislov, Roman, Humphreys, John, Harvey, Gill, Wilson, Paul, Lieberthal, Robert, Payton, Colleen, Sarfaty, Mona, Valko, George, Bolton, Rendelle, Hartmann, Christine, Mueller, Nora, Holmes, Sally K., Bokhour, Barbara, Ono, Sarah, Crabtree, Benjamin, Gordon, Leah, Miller, William, Balasubramanian, Bijal, Solberg, Leif, Cohen, Deborah, McGraw, Kate, Blatt, Andrew, Pittman, Demietrice, Kales, Helen, Berlowitz, Dan, Hudson, Teresa, Helfrich, Christian, Finley, Erin, Garcia, Ashley, Rosen, Kristen, Tami, Claudina, McGeary, Don, Pugh, Mary Jo, Potter, Jennifer Sharpe, Stryczek, Krysttel, Au, David, Zeliadt, Steven, Sayre, George, Leeman, Jennifer, Myers, Allison, Grant, Jennifer, Wangen, Mary, Queen, Tara, Morshed, Alexandra, Dodson, Elizabeth, Tabak, Rachel, Brownson, Ross C., Sheldrick, R. Chris, Mackie, Thomas, Hyde, Justeen, Leslie, Laurel, Yanovitzky, Itzhak, Weber, Matthew, Gesualdo, Nicole, Kristensen, Teis, Stanick, Cameo, Halko, Heather, Dorsey, Caitlin, Powell, Byron, Weiner, Bryan, Lewis, Cara, Carreno, Patricia, Mallard, Kera, Masina, Tasoula, Monson, Candice, Swindle, Taren, Patterson, Zachary, Whiteside-Mansell, Leanne, Hanson, Rochelle, Saunders, Benjamin, Schoenwald, Sonja, Moreland, Angela, Birken, Sarah, Presseau, Justin, Ganz, David, Mittman, Brian, Delevan, Deborah, Hill, Jennifer N., Locatelli, Sara, Fix, Gemmae, Solomon, Jeffrey, Lavela, Sherri L., Scott, Victoria, Scaccia, Jonathan, Alia, Kassy, Skiles, Brittany, Wandersman, Abraham, Sales, Anne, Roberts, Megan, Kennedy, Amy, Khoury, Muin J., Sperber, Nina, Orlando, Lori, Carpenter, Janet, Cavallari, Larisa, Denny, Joshua, Elsey, Amanda, Fitzhenry, Fern, Guan, Yue, Horowitz, Carol, Johnson, Julie, Madden, Ebony, Pollin, Toni, Pratt, Victoria, Rakhra-Burris, Tejinder, Rosenman, Marc, Voils, Corrine, Weitzel, Kristin, Wu, Ryanne, Damschroder, Laura, Lu, Christine, Ceccarelli, Rachel, Mazor, Kathleen M., Wu, Ann, Rahm, Alanna Kulchak, Buchanan, Adam H., Schwartz, Marci, McCormick, Cara, Manickam, Kandamurugu, Williams, Marc S., Murray, Michael F., Escoffery, Ngoc-Cam, Lebow-Skelley, Erin, Udelson, Hallie, Böing, Elaine, Fernandez, Maria E., Wood, Richard J., Mullen, Patricia Dolan, Parekh, Jenita, Caldas, Valerie, Stuart, Elizabeth A., Howard, Shalynn, Thomas, Gilo, Jennings, Jacky M., Torres, Jennifer, Markham, Christine, Shegog, Ross, Peskin, Melissa, Rushing, Stephanie Craig, Gaston, Amanda, Gorman, Gwenda, Jessen, Cornelia, Williamson, Jennifer, Ward, Dianne, Vaughn, Amber, Morris, Ellie, Mazzucca, Stephanie, Burney, Regan, Ramanadhan, Shoba, Minsky, Sara, Martinez-Dominguez, Vilma, Viswanath, Kasisomayajula, Barker, Megan, Fahim, Myra, Ebnahmady, Arezoo, Dragonetti, Rosa, Selby, Peter, Farrell, Margaret, Tompkins, Jordan, Norton, Wynne, Rapport, Kaelin, Hargreaves, Margaret, Lee, Rebekka, Kruse, Gina, Deutsch, Charles, Lanier, Emily, Gray, Ashley, Leppin, Aaron, Christiansen, Lori, Schaepe, Karen, Egginton, Jason, Branda, Megan, Gaw, Charlene, Dick, Sara, Montori, Victor, Shah, Nilay, Korn, Ariella, Hovmand, Peter, Fullerton, Karen, Zoellner, Nancy, Hennessy, Erin, Tovar, Alison, Hammond, Ross, Economos, Christina, Kay, Christi, Gazmararian, Julie, Vall, Emily, Cheung, Patricia, Franks, Padra, Barrett-Williams, Shannon, Weiss, Paul, Hamilton, Erica, Marques, Luana, Dixon, Louise, Ahles, Emily, Valentine, Sarah, Shtasel, Derri, Parra-Cardona, Ruben, Northridge, Mary, Kavathe, Rucha, Zanowiak, Jennifer, Wyatt, Laura, Singh, Hardayal, Islam, Nadia, Monteban, Madalena, Freedman, Darcy, Bess, Kimberly, Walsh, Colleen, Matlack, Kristen, Flocke, Susan, Baily, Heather, Harden, Samantha, Ramalingam, NithyaPriya, Gold, Rachel, Cottrell, Erika, Hollombe, Celine, Dambrun, Katie, Bunce, Arwen, Middendorf, Mary, Dearing, Marla, Cowburn, Stuart, Mossman, Ned, Melgar, Gerry, Hopfer, Suellen, Hecht, Michael, Ray, Anne, Miller-Day, Michelle, BeLue, Rhonda, Zimet, Greg, Nelson, Eve-Lynn, Kuhlman, Sandy, Doolittle, Gary, Krebill, Hope, Spaulding, Ashley, Levin, Theodore, Sanchez, Michael, Landau, Molly, Escobar, Patricia, Minian, Nadia, Noormohamed, Aliya, Zawertailo, Laurie, Baliunas, Dolly, Giesbrecht, Norman, Le Foll, Bernard, Samokhvalov, Andriy, Meisel, Zachary, Polsky, Daniel, Schackman, Bruce, Mitchell, Julia, Sevarino, Kaitlyn, Gimbel, Sarah, Mwanza, Moses, Nisingizwe, Marie Paul, Michel, Catherine, Hirschhorn, Lisa, Choudhary, Mahrukh, Thonduparambil, Della, Meissner, Paul, Pinnock, Hilary, Barwick, Melanie, Carpenter, Christopher, Eldridge, Sandra, Grandes-Odriozola, Gonzalo, Griffiths, Chris, Rycroft-Malone, Jo, Murray, Elizabeth, Patel, Anita, Sheikh, Aziz, Taylor, Stephanie J. C., Guilliford, Martin, Pearce, Gemma, Korngiebel, Diane, West, Kathleen, Burke, Wylie, Hannon, Peggy, Harris, Jeffrey, Hammerback, Kristen, Kohn, Marlana, Chan, Gary K. C., Mafune, Riki, Parrish, Amanda, Beresford, Shirley, Pike, K. Joanne, Shelton, Rachel, Jandorf, Lina, Erwin, Deborah, Charles, Thana-Ashley, Baldwin, Laura-Mae, Ike, Brooke, Fickel, Jacqueline, Lind, Jason, Cowper, Diane, Fleming, Marguerite, Sadler, Amy, Dye, Melinda, Katzburg, Judith, Ong, Michael, Tubbesing, Sarah, Simmons, Molly, Harnish, Autumn, Gabrielian, Sonya, McInnes, Keith, Smith, Jeffrey, Ferrand, John, Torres, Elisa, Green, Amy, Bradbury, Angela R., Patrick-Miller, Linda J., Egleston, Brian L., Domchek, Susan M., Olopade, Olufunmilayo I., Hall, Michael J., Daly, Mary B., Grana, Generosa, Ganschow, Pamela, Fetzer, Dominique, Brandt, Amanda, Chambers, Rachelle, Clark, Dana F., Forman, Andrea, Gaber, Rikki S., Gulden, Cassandra, Horte, Janice, Long, Jessica, Lucas, Terra, Madaan, Shreshtha, Mattie, Kristin, McKenna, Danielle, Montgomery, Susan, Nielsen, Sarah, Powers, Jacquelyn, Rainey, Kim, Rybak, Christina, Seelaus, Christina, Stoll, Jessica, Stopfer, Jill, Yao, Xinxin Shirley, Savage, Michelle, Miech, Edward, Damush, Teresa, Rattray, Nicholas, Myers, Jennifer, Homoya, Barbara, Winseck, Kate, Klabunde, Carrie, Langer, Deb, Aggarwal, Avi, Neilson, Elizabeth, Gunderson, Lara, Gardner, Marla, O’Sulleabhain, Liam, and Kroenke, Candyce

Published
2017
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9. Apparent diffusion coefficient histogram shape analysis for monitoring early response in patients with advanced cervical cancers undergoing concurrent chemo-radiotherapy.

Author

Meng, Jie, Zhu, Lijing, Zhu, Li, Wang, Huanhuan, Liu, Song, Yan, Jing, Liu, Baorui, Guan, Yue, Ge, Yun, He, Jian, Zhou, Zhengyang, and Yang, Xiaofeng

Subjects
CANCER treatment, LONGITUDINAL method, MAGNETIC resonance imaging, SQUAMOUS cell carcinoma, CERVIX uteri tumors, TUMOR treatment
Abstract

Background: To explore the role of apparent diffusion coefficient (ADC) histogram shape related parameters in early assessment of treatment response during the concurrent chemo-radiotherapy (CCRT) course of advanced cervical cancers.Methods: This prospective study was approved by the local ethics committee and informed consent was obtained from all patients. Thirty-two patients with advanced cervical squamous cell carcinomas underwent diffusion weighted magnetic resonance imaging (b values, 0 and 800 s/mm2) before CCRT, at the end of 2nd and 4th week during CCRT and immediately after CCRT completion. Whole lesion ADC histogram analysis generated several histogram shape related parameters including skewness, kurtosis, s-sDav, width, standard deviation, as well as first-order entropy and second-order entropies. The averaged ADC histograms of 32 patients were generated to visually observe dynamic changes of the histogram shape following CCRT.Results: All parameters except width and standard deviation showed significant changes during CCRT (all P < 0.05), and their variation trends fell into four different patterns. Skewness and kurtosis both showed high early decline rate (43.10 %, 48.29 %) at the end of 2nd week of CCRT. All entropies kept decreasing significantly since 2 weeks after CCRT initiated. The shape of averaged ADC histogram also changed obviously following CCRT.Conclusions: ADC histogram shape analysis held the potential in monitoring early tumor response in patients with advanced cervical cancers undergoing CCRT. [ABSTRACT FROM AUTHOR]

Published
2016
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10. Whole-lesion apparent diffusion coefficient histogram analysis: significance in T and N staging of gastric cancers.

Author

Liu S, Zhang Y, Chen L, Guan W, Guan Y, Ge Y, He J, and Zhou Z

Subjects
Adult, Aged, Diagnosis, Differential, Female, Humans, Lymphatic Metastasis pathology, Male, Neoplasm Staging, ROC Curve, Stomach Neoplasms pathology, Stomach Neoplasms surgery, Diffusion Magnetic Resonance Imaging, Image Interpretation, Computer-Assisted, Lymphatic Metastasis diagnostic imaging, Stomach Neoplasms diagnostic imaging
Abstract

Background: Whole-lesion apparent diffusion coefficient (ADC) histogram analysis has been introduced and proved effective in assessment of multiple tumors. However, the application of whole-volume ADC histogram analysis in gastrointestinal tumors has just started and never been reported in T and N staging of gastric cancers., Methods: Eighty patients with pathologically confirmed gastric carcinomas underwent diffusion weighted (DW) magnetic resonance imaging before surgery prospectively. Whole-lesion ADC histogram analysis was performed by two radiologists independently. The differences of ADC histogram parameters among different T and N stages were compared with independent-samples Kruskal-Wallis test. Receiver operating characteristic (ROC) analysis was performed to evaluate the performance of ADC histogram parameters in differentiating particular T or N stages of gastric cancers., Results: There were significant differences of all the ADC histogram parameters for gastric cancers at different T (except ADC min and ADC max ) and N (except ADC max ) stages. Most ADC histogram parameters differed significantly between T1 vs T3, T1 vs T4, T2 vs T4, N0 vs N1, N0 vs N3, and some parameters (ADC 5% , ADC 10% , ADC min ) differed significantly between N0 vs N2, N2 vs N3 (all P < 0.05). Most parameters except ADC max performed well in differentiating different T and N stages of gastric cancers. Especially for identifying patients with and without lymph node metastasis, the ADC 10% yielded the largest area under the ROC curve of 0.794 (95% confidence interval, 0.677-0.911). All the parameters except ADC max showed excellent inter-observer agreement with intra-class correlation coefficients higher than 0.800., Conclusion: Whole-volume ADC histogram parameters held great potential in differentiating different T and N stages of gastric cancers preoperatively.

Published
2017
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11. Development and validation of a chromatographic method for determining Clematichinenoside AR and related impurities.

Author

Zhou Y, Guan Y, Shi J, Zhang X, Yao L, and Liu L

Abstract

Background: Clematichinenoside AR is a promising lead compound for the treatment of rheumatoid arthritis. A systematic research for the related impurities in AR bulk samples is still lacking. For the safe use of this natural product in future clinical practice, the structure and content of each constituent, including the main ingredient as well as the impurities in AR bulk sample must be characterized in detail., Results: A simple and stability indicating RP-HPLC method was developed and validated for determining the purity of clematichinenoside AR (AR), a natural product from the roots of Clematis manshurica Rupr. (Ranunculaceae) with the potential of treating rheumatoid arthritis. Five impurities were characterized, and impurity 2 (Clematomandshurica saponin F) is a new triterpenoid saponin isolated from this product. Optimum separation for clematichinenoside AR and five related impurities was carried out on an Agilent octadecylsilane bonded silica gel column (TC-C18, 4.6 mm ×150 mm, 5 μm) using a gradient HPLC method. The validation results showed good sensitivity, specificity, linearity(r2>0.9992) precision(RSD<1.63%), accuracy(recoveries in the range of 95.60%-104.76%) and robustness. Three AR bulk samples containing all the impurities were examined by two methods, and the stability of correction factors for the determination of related impurities was discussed. The proposed stability-indicating method was suitable for the quality control of this natural product., Conclusion: Five related impurities of clematichinenoside AR were characterized, including a new triterpenoid saponins firstly found in clematichinenoside AR bulk samples. In the simple chromatographic method for determining clematichinenoside AR and its related impurities in bulk samples, the correction factor was better for the quality control in the relative stable concentrations.

Published
2012
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