Most Cited Articles | Alzheimer’s Disease Classification Based on Multi-feature Fusion

Journal Name: Current Medical Imaging
Formerly: Current Medical Imaging Reviews

 

Author(s): Nuwan Madusanka, Heung-Kook Choi*, Jae-Hong So, Boo-Kyeong Choi.

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Abstract:

Background: In this study, we investigated the fusion of texture and morphometric features as a possible diagnostic biomarker for Alzheimer’s Disease (AD).

Methods: In particular, we classified subjects with Alzheimer’s disease, Mild Cognitive Impairment (MCI) and Normal Control (NC) based on texture and morphometric features. Currently, neuropsychiatric categorization provides the ground truth for AD and MCI diagnosis. This can then be supported by biological data such as the results of imaging studies. Cerebral atrophy has been shown to correlate strongly with cognitive symptoms. Hence, Magnetic Resonance (MR) images of the brain are important resources for AD diagnosis. In the proposed method, we used three different types of features identified from structural MR images: Gabor, hippocampus morphometric, and Two Dimensional (2D) and Three Dimensional (3D) Gray Level Co-occurrence Matrix (GLCM). The experimental results, obtained using a 5-fold cross-validated Support Vector Machine (SVM) with 2DGLCM and 3DGLCM multi-feature fusion approaches, indicate that we achieved 81.05% ±1.34, 86.61% ±1.25 correct classification rate with 95% Confidence Interval (CI) falls between (80.75-81.35) and (86.33-86.89) respectively, 83.33%±2.15, 84.21%±1.42 sensitivity and 80.95%±1.52, 85.00%±1.24 specificity in our classification of AD against NC subjects, thus outperforming recent works found in the literature. For the classification of MCI against AD, the SVM achieved a 76.31% ± 2.18, 78.95% ±2.26 correct classification rate, 75.00% ±1.34, 76.19%±1.84 sensitivity and 77.78% ±1.14, 82.35% ±1.34 specificity.

Results and Conclusion: The results of the third experiment, with MCI against NC, also showed that the multiclass SVM provided highly accurate classification results. These findings suggest that this approach is efficient and may be a promising strategy for obtaining better AD, MCI and NC classification performance. To read out more, please visit: http://www.eurekaselect.com/166178/article

Most Cited Articles | Novel Trifluoromethylpyrazole Acyl Thiourea Derivatives: Synthesis, Antifungal Activity and Docking Study

Journal Name: Letters in Drug Design & Discovery

Author(s): Han Wang, Zhi-Wen Zhai, Yan-Xia Shi, Cheng-Xia Tan, Jian-Quan Weng, Liang Han, Bao-Ju Li, Xing-Hai Liu*.

 

 

 

 

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Abstract:

Background: In recent years, pyrazole carboxamide derivatives possessed excellent fungicidal activity. In the process of designing new fungicides, the carboxamide group was modified in order to find novel structure pyrazole carboxamide derivatives.

Methods: Ten novel trifluoromethyl pyrazole acyl thiourea derivatives were designed and synthesized. In vivo fungicidal activities of these compounds were tested against Fusarium oxysporum, Corynespora mazei and Botrytis cinerea, respectively.

Results: Particularly compounds exhibited significant control effective at 100 mg/L. More importantly, some compounds showed the good control effective at 10 mg/L. Furthermore, docking was established to study the structure-activity relationship of the title compounds.

Conclusion: It is possible that trifluoromethylpyrazole acyl thiurea derivatives, which possess good control effective against Botrytis cinerea, may become novel lead compounds for the development of fungicides with further structure modification. To read out more, please visit: http://www.eurekaselect.com/163481/article

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