Flow cytometry has been proven to be a very versatile tool for cell biology research and clinical applications. A drawback of conventional technique lies in the fundamental limit of extracting morphological characteristics of the interrogated cells. In this paper, we present recent results of instrumentation design of flow cytometer systems with a new diffraction imaging method for the of 3D cell features measurement consisting of a jet-in-fluid flow chamber, a dual-polarization imaging system and a friendly human-computer interaction interface. A precise fluid control unit has been developed for producing stable laminar flow that is critical for acquisition of dual-polarization diffraction images and cell classification. Virtual simulation and experiment test results of the flow control unit and the new system with polystyrene microspheres and cells indicated the strong correlations between the diffraction image patterns and the 3D morphology of the particles.
Current flow cytometry (FCM) requires fluorescent dyes labeling cells which make the procedure costly and time consuming. This manuscript reports a feasibility study of detecting the cell apoptosis with a label-free method in glioblastoma cells. A human glioma cell line M059K was exposed to 8 Gy dose of radiation, which enables the cells to undergo radiation-induced apoptosis. The rates of apoptosis were studied at different time points post-irradiation with two different methods: FCM in combination with Annexin V-FITC/PI staining and a newly developed technique named polarization diffraction imaging flow cytometry. Totally 1000 diffraction images were acquired for each sample and the gray level co-occurrence matrix (GLCM) algorithm was used in morphological characterization of the apoptotic cells. Among the feature parameters extracted from each image pair, we found that the two GLCM parameters of angular second moment (ASM) and sum entropy (SumEnt) exhibit high sensitivities and consistencies as the apoptotic rates (Pa) measured with FCM method. In addition, no significant difference exists between Pa and ASM_S, Pa and SumEnt_S, respectively (P > 0.05). These results demonstrated that the new label-free method can detect cell apoptosis effectively. Cells can be directly used in the subsequent biochemical experiments as the structure and function of cells and biomolecules are well-preserved with this new method.
Purpose: Stereological method used to obtain three dimensional quantitative information from two dimensional images is a widely used tool in the study of cells and pathology. But the feasibility of the method for quantitative evaluation of volumes with 3D image data sets for radiotherapy clinical application has not been explored. On the other hand, a quick, easy‐to‐use and reliable method is highly desired in image‐guided‐radiotherapy(IGRT) for tumor volume measurement for the assessment of response to treatment. To meet this need, a stereological method for evaluating tumor volumes for esophageal cancer is presented in this abstract. Methods: The stereology method was optimized by selecting the appropriate grid point distances and sample types. 7 patients with esophageal cancer were selected retrospectively for this study, each having pre and post treatment computed tomography (CT) scans. Stereological measurements were performed for evaluating the gross tumor volume (GTV) changes after radiotherapy and the results was compared with the ones by planimetric measurements. Two independent observers evaluated the reproducibility for volume measurement using the new stereological technique. Results: The intraobserver variation in the GTV volume estimation was 3.42±1.68cm3 (the Wilcoxon matched‐pairs test Resultwas Z=−1.726,P=0.084>0.05); the interobserver variation in the GTV volume estimation was 22.40±7.23 cm3 (Z=−3.296,P=0.083>0.05), which showed the consistency in GTV volume calculation with the new method for the same and different users. The agreement level between the results from the two techniques was also evaluated. Difference between the measured GTVs was 20.10±5.35 cm3 (Z=−3.101,P=0.089>0.05). Variation of the measurement results using the two techniques was low and clinically acceptable. Conclusion: The good agreement between stereological and planimetric techniques proves the reliability of the stereological tumor volume estimations. The optimized stereological technique described in this abstract may provide a quick, unbiased and reproducible tool for tumor volume estimation for treatment response assessment. Supported by NSFC (#81041107, #81171342 and #31000784)
vii Acknowledgements viii Chapter One: Introduction 1 1.1 TRIPS in an Innovation and Development Context 1 1.2 A Two-Pronged Approach to Innovation and Development 10 1.2.1 Innovation Capability Approach to Development 11 1.2.2 Ensuring Equal Innovation Opportunity and the Freedom to Innovate 13 1.2.3 Integrating TRIPS into a Fair and Balanced Global Innovation System 14 1.3 Research Methodology 16 1.4 Thesis Structure 17 Chapter Two: The Values and Dynamics of Innovation 21 2.1 The Definition of Innovation 22 2.1.1 The Technological Dimension of Innovation 23 2.1.2 The Commercial Dimension of Innovation 25 2.1.3 The Social Dimension of Innovation 27 2.1.4 A Purpose-Centred Definition of Innovation 28 2.2 The Public Good Character of Innovation 29 2.2.1 Characteristics of Public Goods 29 2.2.2 Innovation as an Intrinsic Public Good 37 2.2.3 The Distributive and Equalizing Effects of Innovation as a Global Public Good 41 2.3 The Dynamics of Innovation Models and Decentralization of Innovation Capability 45 2.3.1 The Changing Innovation Environment: Innovating Wikily 46 2.3.2 The Open and Collaborative Innovation Model 50 2.3.3 The Cumulative Innovation Model 53 iii 2.3.4 The User Innovation Model 56 2.3.5 Indigenous Innovation 58 2.4 A Multi-faceted System of Innovation 60 2.5 Innovation Systems in Developing and Developed Countries 66 2.6 Conclusion 68 Chapter Three: An Innovation Capability Approach to Development 69 3.
The combination of positron emission tomography (PET) and CT images provides complementary functional and anatomical information of human tissues and it has been used for better tumor volume definition of lung cancer. This paper proposed a robust method for automatic lung tumor segmentation on PET/CT images. The new method is based on fuzzy Markov random field (MRF) model. The combination of PET and CT image information is achieved by using a proper joint posterior probability distribution of observed features in the fuzzy MRF model which performs better than the commonly used Gaussian joint distribution. In this study, the PET and CT simulation images of 7 non-small cell lung cancer (NSCLC) patients were used to evaluate the proposed method. Tumor segmentations with the proposed method and manual method by an experienced radiation oncologist on the fused images were performed, respectively. Segmentation results obtained with the two methods were similar and Dice's similarity coefficient (DSC) was 0.85 ± 0.013. It has been shown that effective and automatic segmentations can be achieved with this method for lung tumors which locate near other organs with similar intensities in PET and CT images, such as when the tumors extend into chest wall or mediastinum.
Diffraction imaging of scattered light allows extraction of information on scatterer's morphology. We present a method for accurate simulation of diffraction imaging of single particles by combining rigorous light scattering model with ray-tracing software. The new method has been validated by comparison to measured images of single microspheres. Dependence of fringe patterns on translation of an objective based imager to off-focus positions has been analyzed to clearly understand diffraction imaging with multiple optical elements. The calculated and measured results establish unambiguously that diffraction imaging should be pursued in non-conjugate configurations to ensure accurate sampling of coherent light distribution from the scatterer.
In the field of gliomas research, the broad availability of genetic and image information originated by computer technologies and the booming of biomedical publications has led to the advent of the big-data era. Machine learning methods were applied as possible approaches to speed up the data mining processes. In this article, we reviewed the present situation and future orientations of machine learning application in gliomas within the context of workflows to integrate analysis for precision cancer care. Publicly available tools or algorithms for key machine learning technologies in the literature mining for glioma clinical research were reviewed and compared. Further, the existing solutions of machine learning methods and their limitations in glioma prediction and diagnostics, such as overfitting and class imbalanced, were critically analyzed.
Objective To determinted whether GM1 had a protective effect on injury induced by serum-deprivation and the possible mechanism in PC12 cells. Methods The viability of PC12 cells was quantified by MTT after serum-deprivation.The number of apoptotic cells and necrotic cells were determined by Hoechst 33258/PI staining.And the change of PKC protein expression on PC12 cells' membrane and cytosols was detected by Western blotting. Results 1.The viability of PC12 cells decreased after serum-deprivation and the serum-deprivation for 24 hours was chosen as an injury model in this research.Most of the PC12 cells presented apoptosis 24 hours after serum-deprivation.In addition,the PC12 cells' cytosols PKC protein decreased,while the PC12 cells' membrane PKC protein increased significantly,and this result suggested PKC's translocation to membrane and its activation.2.The viability of PC12 cells preincubated with GM1 in high concentrations(10,1,0.1μmol/L) increased significantly and GM1 protected PC12 cells from apoptosis after serum-deprived injury.GM1 reduced the damage of serum-deprivation on PC12 cells and inhibited PKC protein translocation after injury.3.The repair function of GM1 was effective to neuronal resume after serum-deprived injury.Conclusion Neuroprotective effects of GM1 on serum-deprived injury may be partly mediated through the regulation of PKC pathways and it is helpful for the recovery after injury.
Background: The flow cytometry (FCM) has been widely used in both basic and clinical research applications. However, the conventional noncoherent fluorescence and the bright or dark field images acquired spatially integrated and can only yield limited information. Few 3D morphological features of cells can be unveiled. Objective: Diffraction imaging techniques can be used to improve the flow cytometry system and to reflect some 3D morphological features of cells. Method: The newly developed diffraction imaging flow cytometry system (DIFC) in our previous studies could be used to compensate conventional flow cytometries to reflect a cell's 3D morphological features. In this study, we developed a method based on a Support Vector Machine to classify the diffraction images acquired from human acute leukaemia T (Jurkat) cells and Burkitt lymphoma B (Ramos) cells with the diffraction imaging flow cytometry system technique. Results: As a result, an accuracy of 99.38% with MCC value of 0.9875 was achieved in an independent testing dataset, which indicated that the DIFC system could differentiate the cells. Conclusion: It is indicated by the results that strong correlation exists between the characteristic parameters of the images and the 3D morphological features of cells. Since diffraction images correlate strongly to the 3D morphology of cells, this system could be used for studies concerning cellular morphology. Keywords: Flow cytometry, diffraction imaging, Jurkat, Ramos, support vector machine
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