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Objective:To investigate the application value of magnetic resonance (MR) diffusion tensor imaging (DTI) automatic fiber quantification (AFQ) in the diagnosis and prediction of Alzheimer's disease (AD).Methods:Clinical and MR data of 21 patients with AD (AD group) and 33 normal controls (NC group) were collected.AFQ software was used to analyze DTI data, track 20 white matter fiber bundles in the brain, and compare the differences of fractional anisotropy (FA) and mean diffusivity (MD) value of each bundle between groups.Each fiber bundle was divided into 100 equal parts along the direction of travel, and the FA or MD value of each part was taken as a characteristic.Screening the characteristics with statistic differences between groups for classification of AD and NC by support vector machine (SVM) with leave one method for cross validation.Classification effectiveness was evaluated using the receiver operating characteristic (ROC) curve.Results:Eleven (left/right anterior thalamic radiation (ATR), left/right corticospinal tract (CST), genu of corpus callosum (CC Genu), right inferior longitudinal fasciculus (ILF), right superior longitudinal fasciculus (SLF), left/right uncinated fasciculus (UF), and left/right arcuate fasciculus (AF)) of the 20 fiber bundles were successfully tracked in all subjects.Compared with NC group, the FA values of 2 fiber bundles (left/right UF) in AD group were significantly decreased( t=-2.532, -2.391, both P<0.05), and the MD values of 7 fiber bundles (left ATR, left/right CST, right ILF, left/right UF, and left AF) were significantly increased ( t=2.569, 2.411, 2.108, 2.357, 3.773, 3.796, 3.492, all P<0.05). Among the 2 200 characteristics of 11 fiber bundles, 412 classification characteristics with inter-group differences were selected.Among which, 78 FA characteristics were distributed in 7 fiber bundles (left ATR, left/right CST, CC Genu, right ILF, left/right UF), and 334 MD characteristics were distributed in 9 fiber tracts (left/right ATR, left/right CST, CC Genu, right ILF, left/right UF, and left AF). The accuracy of SVM classification was 85.19%, sensitivity was 80.95%, specificity was 87.88%, and area under ROC curve was 0.894 7. Conclusion:AFQ analysis based on DTI has a high application value in the diagnosis and prediction of AD.
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Objective:To investigate the expression of activator of basal transcription 1 (ABT1) protein in gastric cancer tissue and its relationships with the clinical parameters and prognosis of gastric cancer patients, and to clarify the role of ABT1in the occurrence and development of gastric cancer.Methods:A total of 100cases of cancer tissue of the gastric cancer patients and 80pairs of adjacent tissue were selected.The expressions of ABT1in cancer tissue and adjacent tissue were detected by immunohistochemistry and the proportion of stained cells and the degree of staining in the immunohistochemistry results were analyzed using semi-quantitative analysis.The relationships between the semi-quantitative analysis results and the clinical parameters of gastric cancer patients were statistically analyzed.Kaplan-Meier method was used to analyze the correlation between the ABT1 protein expression level and the survival of gastric cancer patients.Results:ABT1-positive staining was observed in the nucleus and cytoplasm of gastric cancer tissue and adjacent gastric tissue.The expression level of ABT1in gastric cancer tissue was lower than that in adjacent tissue (P=0.021) .The ABT1protein expression level in gastric cancer tissue was significantly negatively correlated with the pathological grade (r=-0.224, P=0.026) .The Kaplan-Meier analysis results of the survival curve showed that the high expression of ABT1was associated with good prognosis in the gastric cancer patients (HR=1.483, P＜0.01) .The survival rate of gastric cancer patients with high ABT1expression was significantly higher than that of the patients with low ABT1expression (HR=2.411, P=0.0272) .Conclusion:The expression of ABT1in gastric cancer tissue is lower, indicating that ABT1can be used one of the markers of good prognosis of gastric cancer.
RESUMO
Objective: To investigate the expression of activator of basal transcription 1 (ABT1) protein in gastric cancer tissue and its relationships with the clinical parameters and prognosis of gastric cancer patients, and to clarify the role of ABT1 in the occurrence and development of gastric cancer. Methods: A total of 100 cases of cancer tissue of the gastric cancer patients and 80 pairs of adjacent tissue were selected. The expressions of ABT1 in cancer tissue and adjacent tissue were detected by immunohistochemistry and the proportion of stained cells and the degree of staining in the immunohistochemistry results were analyzed using semi-quantitative analysis. The relationships between the semi-quantitative analysis results and the clinical parameters of gastric cancer patients were statistically analyzed. Kaplan-Meier method was used to analyze the correlation between the ABT1 protein expression level and the survival of gastric cancer patients. Results: ABTl-positive staining was observed in the nucleus and cytoplasm of gastric cancer tissue and adjacent gastric tissue. The expression level of ABT1 in gastric cancer tissue was lower than that in adjacent tissue (P= 0.021). The ABT1 protein expression level in gastric cancer tissue was significantly negatively correlated with the pathological grade (r=-0.224, P = 0. 026). The Kaplan-Meier analysis results of the survival curve showed that the high expression of ABT1 was associated with good prognosis in the gastric cancer patients (HR=1. 483, P<.0. 01). The survival rate of gastric cancer patients with high ABT1 expression was significantly higher than that of the patients with low ABT1 expression (HR = 2.411, P=0. 0272). Conclusion: The expression of ABT1 in gastric cancer tissue is lower, indicating that ABT1 can be used one of the markers of good prognosis of gastric cancer.