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8. Differential Effects of Lipid-lowering Drugs in Modulating Morphology of Cholesterol Particles.

Author

Madasamy, Shanmugavel, Liu, David, Lundry, Jason, Alderete, Benjamin, Kong, Raymond, Robinson, J Paul, Wu, Alan HB, and Amento, Edward P

Subjects
Humans, Simvastatin, Cholesterol, Anticholesteremic Agents, Aged, Middle Aged, Female, Male, Cholesterol particles morphology, lipid-lowering drugs, plaque array, atherosclerosis, imaging flow cytometry, cardiovascular diagnosis, Atherosclerosis, Cardiovascular, 2.1 Biological and endogenous factors, Biochemistry and Cell Biology, Psychology, Cognitive Sciences
Abstract

Treatment of dyslipidemia patients with lipid-lowering drugs leads to a significant reduction in low-density lipoproteins (LDL) level and a low to moderate level of increase in high-density lipoprotein (HDL) cholesterol in plasma. However, a possible role of these drugs in altering morphology and distribution of cholesterol particles is poorly understood. Here, we describe the in vitro evaluation of lipid-lowering drug effects in modulating morphological features of cholesterol particles using the plaque array method in combination with imaging flow cytometry. Image analyses of the cholesterol particles indicated that lovastatin, simvastatin, ezetimibe, and atorvastatin induce the formation of both globular and linear strand-shaped particles, whereas niacin, fibrates, fluvastatin, and rosuvastatin induce the formation of only globular-shaped particles. Next, purified very low-density lipoprotein (VLDL) and LDL particles incubated with these drugs showed changes in the morphology and image texture of cholesterol particles subpopulations. Furthermore, screening of 50 serum samples revealed the presence of a higher level of linear shaped HDL cholesterol particles in subjects with dyslipidemia (mean of 18.3%) compared to the age-matched normal (mean of 11.1%) samples. We also observed considerable variations in lipid-lowering drug effects on reducing linear shaped LDL and HDL cholesterol particles formation in serum samples. These findings indicate that lipid-lowering drugs, in addition to their cell-mediated hypolipidemic effects, may directly modulate morphology of cholesterol particles by a non-enzymatic mechanism of action. The outcomes of these results have potential to inform diagnosis of atherosclerosis and predict optimal lipid-lowering therapy.

Published
2017

11. A Statistical Modeling Approach to Computer-Aided Quantification of Dental Biofilm

Author

Mansoor, Awais, Patsekin, Valery, Scherl, Dale, Robinson, J. Paul, and Rajwa, Bartlomiej

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Biofilm is a formation of microbial material on tooth substrata. Several methods to quantify dental biofilm coverage have recently been reported in the literature, but at best they provide a semi-automated approach to quantification with significant input from a human grader that comes with the graders bias of what are foreground, background, biofilm, and tooth. Additionally, human assessment indices limit the resolution of the quantification scale; most commercial scales use five levels of quantification for biofilm coverage (0%, 25%, 50%, 75%, and 100%). On the other hand, current state-of-the-art techniques in automatic plaque quantification fail to make their way into practical applications owing to their inability to incorporate human input to handle misclassifications. This paper proposes a new interactive method for biofilm quantification in Quantitative light-induced fluorescence (QLF) images of canine teeth that is independent of the perceptual bias of the grader. The method partitions a QLF image into segments of uniform texture and intensity called superpixels; every superpixel is statistically modeled as a realization of a single 2D Gaussian Markov random field (GMRF) whose parameters are estimated; the superpixel is then assigned to one of three classes (background, biofilm, tooth substratum) based on the training set of data. The quantification results show a high degree of consistency and precision. At the same time, the proposed method gives pathologists full control to post-process the automatic quantification by flipping misclassified superpixels to a different state (background, tooth, biofilm) with a single click, providing greater usability than simply marking the boundaries of biofilm and tooth as done by current state-of-the-art methods., Comment: 10 pages, 7 figures, Journal of Biomedical and Health Informatics 2014. keywords: {Biomedical imaging;Calibration;Dentistry;Estimation;Image segmentation;Manuals;Teeth}, http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6758338&isnumber=6363502

Published
2014
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12. BiofilmQuant: A Computer-Assisted Tool for Dental Biofilm Quantification

Author

Mansoor, Awais, Patsekin, Valery, Scherl, Dale, Robinson, J. Paul, and Rajwa, Bartlomiej

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Dental biofilm is the deposition of microbial material over a tooth substratum. Several methods have recently been reported in the literature for biofilm quantification; however, at best they provide a barely automated solution requiring significant input needed from the human expert. On the contrary, state-of-the-art automatic biofilm methods fail to make their way into clinical practice because of the lack of effective mechanism to incorporate human input to handle praxis or misclassified regions. Manual delineation, the current gold standard, is time consuming and subject to expert bias. In this paper, we introduce a new semi-automated software tool, BiofilmQuant, for dental biofilm quantification in quantitative light-induced fluorescence (QLF) images. The software uses a robust statistical modeling approach to automatically segment the QLF image into three classes (background, biofilm, and tooth substratum) based on the training data. This initial segmentation has shown a high degree of consistency and precision on more than 200 test QLF dental scans. Further, the proposed software provides the clinicians full control to fix any misclassified areas using a single click. In addition, BiofilmQuant also provides a complete solution for the longitudinal quantitative analysis of biofilm of the full set of teeth, providing greater ease of usability., Comment: 4 pages, 4 figures, 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2014)

Published
2014
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19. Data from Development of an Anti-canine PD-L1 Antibody and Caninized PD-L1 Mouse Model as Translational Research Tools for the Study of Immunotherapy in Humans

Author

Oh, Wonkyung, primary, Kim, Alyssa Min Jung, primary, Dhawan, Deepika, primary, Kirkham, Perry M., primary, Ostafe, Raluca, primary, Franco, Jackeline, primary, Aryal, Uma K., primary, Carnahan, Robert H., primary, Patsekin, Valery, primary, Robinson, J. Paul, primary, Knapp, Deborah W., primary, and Lim, Seung-Oe, primary

Published
2023
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20. FIGURE 6 from Development of an Anti-canine PD-L1 Antibody and Caninized PD-L1 Mouse Model as Translational Research Tools for the Study of Immunotherapy in Humans

Author

Oh, Wonkyung, primary, Kim, Alyssa Min Jung, primary, Dhawan, Deepika, primary, Kirkham, Perry M., primary, Ostafe, Raluca, primary, Franco, Jackeline, primary, Aryal, Uma K., primary, Carnahan, Robert H., primary, Patsekin, Valery, primary, Robinson, J. Paul, primary, Knapp, Deborah W., primary, and Lim, Seung-Oe, primary

Published
2023
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21. TABLE 1 from Development of an Anti-canine PD-L1 Antibody and Caninized PD-L1 Mouse Model as Translational Research Tools for the Study of Immunotherapy in Humans

Author

Oh, Wonkyung, primary, Kim, Alyssa Min Jung, primary, Dhawan, Deepika, primary, Kirkham, Perry M., primary, Ostafe, Raluca, primary, Franco, Jackeline, primary, Aryal, Uma K., primary, Carnahan, Robert H., primary, Patsekin, Valery, primary, Robinson, J. Paul, primary, Knapp, Deborah W., primary, and Lim, Seung-Oe, primary

Published
2023
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22. FIGURE 5 from Development of an Anti-canine PD-L1 Antibody and Caninized PD-L1 Mouse Model as Translational Research Tools for the Study of Immunotherapy in Humans

Author

Oh, Wonkyung, primary, Kim, Alyssa Min Jung, primary, Dhawan, Deepika, primary, Kirkham, Perry M., primary, Ostafe, Raluca, primary, Franco, Jackeline, primary, Aryal, Uma K., primary, Carnahan, Robert H., primary, Patsekin, Valery, primary, Robinson, J. Paul, primary, Knapp, Deborah W., primary, and Lim, Seung-Oe, primary

Published
2023
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23. FIGURE 1 from Development of an Anti-canine PD-L1 Antibody and Caninized PD-L1 Mouse Model as Translational Research Tools for the Study of Immunotherapy in Humans

Author

Oh, Wonkyung, primary, Kim, Alyssa Min Jung, primary, Dhawan, Deepika, primary, Kirkham, Perry M., primary, Ostafe, Raluca, primary, Franco, Jackeline, primary, Aryal, Uma K., primary, Carnahan, Robert H., primary, Patsekin, Valery, primary, Robinson, J. Paul, primary, Knapp, Deborah W., primary, and Lim, Seung-Oe, primary

Published
2023
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24. FIGURE 3 from Development of an Anti-canine PD-L1 Antibody and Caninized PD-L1 Mouse Model as Translational Research Tools for the Study of Immunotherapy in Humans

Author

Oh, Wonkyung, primary, Kim, Alyssa Min Jung, primary, Dhawan, Deepika, primary, Kirkham, Perry M., primary, Ostafe, Raluca, primary, Franco, Jackeline, primary, Aryal, Uma K., primary, Carnahan, Robert H., primary, Patsekin, Valery, primary, Robinson, J. Paul, primary, Knapp, Deborah W., primary, and Lim, Seung-Oe, primary

Published
2023
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25. FIGURE 4 from Development of an Anti-canine PD-L1 Antibody and Caninized PD-L1 Mouse Model as Translational Research Tools for the Study of Immunotherapy in Humans

Author

Oh, Wonkyung, primary, Kim, Alyssa Min Jung, primary, Dhawan, Deepika, primary, Kirkham, Perry M., primary, Ostafe, Raluca, primary, Franco, Jackeline, primary, Aryal, Uma K., primary, Carnahan, Robert H., primary, Patsekin, Valery, primary, Robinson, J. Paul, primary, Knapp, Deborah W., primary, and Lim, Seung-Oe, primary

Published
2023
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26. FIGURE 2 from Development of an Anti-canine PD-L1 Antibody and Caninized PD-L1 Mouse Model as Translational Research Tools for the Study of Immunotherapy in Humans

Author

Oh, Wonkyung, primary, Kim, Alyssa Min Jung, primary, Dhawan, Deepika, primary, Kirkham, Perry M., primary, Ostafe, Raluca, primary, Franco, Jackeline, primary, Aryal, Uma K., primary, Carnahan, Robert H., primary, Patsekin, Valery, primary, Robinson, J. Paul, primary, Knapp, Deborah W., primary, and Lim, Seung-Oe, primary

Published
2023
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27. Table S1 from Development of an Anti-canine PD-L1 Antibody and Caninized PD-L1 Mouse Model as Translational Research Tools for the Study of Immunotherapy in Humans

Author

Oh, Wonkyung, primary, Kim, Alyssa Min Jung, primary, Dhawan, Deepika, primary, Kirkham, Perry M., primary, Ostafe, Raluca, primary, Franco, Jackeline, primary, Aryal, Uma K., primary, Carnahan, Robert H., primary, Patsekin, Valery, primary, Robinson, J. Paul, primary, Knapp, Deborah W., primary, and Lim, Seung-Oe, primary

Published
2023
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28. Development of an anti-canine PD-L1 Antibody and Caninized PD-L1 Mouse Model as Translational Research Tools for the Study of Immunotherapy in Humans

Author

Oh, Wonkyung, primary, Kim, Alyssa Min Jung, additional, Dhawan, Deepika, additional, Kirkham, Perry M, additional, Ostafe, Raluca, additional, Franco, Jackeline, additional, Aryal, Uma K, additional, Carnahan, Robert H., additional, Patsekin, Valery, additional, Robinson, J Paul, additional, Knapp, Deborah W, additional, and Lim, Seung-Oe, additional

Published
2023
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