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Cesar, C. L.

Publications and source records attributed to Cesar, C. L..

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Quantitative Assessment of Colorectal Cancer Progression: a Comparative Study of Linear and Nonlinear Microscopy Techniques

BACKGROUND AND AIMSColorectal cancer (CRC) is a disease that can be prevented if is diagnosed and treated at pre-invasive stages. Thus, the monitoring of colonic cancer progression can improve the early diagnosis and detection of malignant lesions in the colon. This monitoring should be performed with appropriate image techniques and be accompanied by proper quantification to minimize subjectivity. We have monitored the mice CRC progression by image deconvolution, two-photon emission fluorescence (TPEF) and second harmonic generation (SHG) microscopies and present different quantization indices for diagnosis.\n\nMETHODSThe Azoxymethane (AOM) / dextran sodium sulfate (DSS) protocol was used. 35 eight-week old male BALB/cCmedc mice were used and distal colon segments were dissected at day zero and fourth, eighth, sixteen, and twenty weeks after injection. These segments were observed with linear and nonlinear optical microscopies and several parameters were used for quantification.\n\nRESULTSCrypt diameter higher than 0.08 mm and increased fluorescence signal intensity in linear images; as well as aspect relation above 0.7 and altered organization reflexed by high-energy values obtained from SHG images, away from those obtained in normal tissues.\n\nCONCLUSIONThe combination of linear and nonlinear signals improve the detection and classification of pathological changes in crypt morphology/distribution and collagen fiber structure/arrangement. In combination with standard screening approaches for CRC, the proposed methods improve the detection of the disease in its early stages, thereby increasing the chances of successful treatment.

cancer biology

Impact of gray matter signal regression in resting state and language task functional networks

A network analysis of the resting state (RS) and language task (LT) of fRMI data sets is presented. Specifically, the analysis compares the impact of the global signal regression of gray matter signal on the graph parameters and community structure derived of functional data. It was found that, without gray matter signal regression (GSR), the group comparison showed no significant changes of the global metrics between the two conditions studied. With gray matter signal regression, significant differences between the global (local) metrics for the conditions were obtained. The mean degree, the clustering coefficient of the network and the mean value of the local efficiency were metrics with significant changes. The community structure of group connectivity matrices was explored for both conditions (RS and LT) and for different preprocessing steps. When gray matter signal regression was performed, small changes of the community structure were observed. Approximately, the same regions were classified in the same communities before and after GSR. This means, that the community structure of the data is weakly affected by this preprocessing step. The modularity index presented significant changes between conditions (RS and LT) and between different preprocessing pipeline.

neuroscience