scholarly journals The Optimization of Pressure Cycling Technology (PCT) for Differential Extraction of Sexual Assault Casework

2016 ◽  
Author(s):  
Vanessa Martinez
2020 ◽  
Vol 19 (5) ◽  
pp. 1982-1990 ◽  
Author(s):  
Huanhuan Gao ◽  
Fangfei Zhang ◽  
Shuang Liang ◽  
Qiushi Zhang ◽  
Mengge Lyu ◽  
...  

2012 ◽  
Vol 127 (2) ◽  
pp. 321-333 ◽  
Author(s):  
Pamela L. Marshall ◽  
Jonathan L. King ◽  
Nathan P. Lawrence ◽  
Alexander Lazarev ◽  
Vera S. Gross ◽  
...  

2018 ◽  
Vol 1 (2) ◽  
pp. e201800042 ◽  
Author(s):  
Tiannan Guo ◽  
Li Li ◽  
Qing Zhong ◽  
Niels J Rupp ◽  
Konstantina Charmpi ◽  
...  

It remains unclear to what extent tumor heterogeneity impacts on protein biomarker discovery. Here, we quantified proteome intra-tissue heterogeneity (ITH) based on a multi-region analysis of prostate tissues using pressure cycling technology and Sequential Windowed Acquisition of all THeoretical fragment ion mass spectrometry. We quantified 6,873 proteins and analyzed the ITH of 3,700 proteins. The level of ITH varied depending on proteins and tissue types. Benign tissues exhibited more complex ITH patterns than malignant tissues. Spatial variability of 10 prostate biomarkers was validated by immunohistochemistry in an independent cohort (n = 83) using tissue microarrays. Prostate-specific antigen was preferentially variable in benign prostatic hyperplasia, whereas growth/differentiation factor 15 substantially varied in prostate adenocarcinomas. Furthermore, we found that DNA repair pathways exhibited a high degree of variability in tumorous tissues, which may contribute to the genetic heterogeneity of tumors. This study conceptually adds a new perspective to protein biomarker discovery: it suggests that recent technological progress should be exploited to quantify and account for spatial proteome variation to complement biomarker identification and utilization.


2007 ◽  
Vol 28 (6) ◽  
pp. 1022-1024 ◽  
Author(s):  
Heather Ringham ◽  
Richard L. Bell ◽  
Gary B. Smejkal ◽  
James Behnke ◽  
Frank A. Witzmann

2016 ◽  
Vol 15 (6) ◽  
pp. 1821-1829 ◽  
Author(s):  
Shiying Shao ◽  
Tiannan Guo ◽  
Vera Gross ◽  
Alexander Lazarev ◽  
Ching Chiek Koh ◽  
...  

2020 ◽  
Author(s):  
Yaoting Sun ◽  
Sathiyamoorthy Selvarajan ◽  
Zelin Zang ◽  
Wei Liu ◽  
Yi Zhu ◽  
...  

SUMMARYUp to 30% of thyroid nodules cannot be accurately classified as benign or malignant by cytopathology. Diagnostic accuracy can be improved by nucleic acid-based testing, yet a sizeable number of diagnostic thyroidectomies remains unavoidable. In order to develop a protein classifier for thyroid nodules, we analyzed the quantitative proteomes of 1,725 retrospective thyroid tissue samples from 578 patients using pressure-cycling technology and data-independent acquisition mass spectrometry. With artificial neural networks, a classifier of 14 proteins achieved over 93% accuracy in classifying malignant thyroid nodules. This classifier was validated in retrospective samples of 271 patients (91% accuracy), and prospective samples of 62 patients (88% accuracy) from four independent centers. These rapidly acquired proteotypes and artificial neural networks supported the establishment of an effective protein classifier for classifying thyroid nodules.


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