RESULTS: Comparison of the PLS and RF showed that RF exhibited poorer generalization and hence poorer predictive performance. Both the regression coefficient of PLS and the variable importance of RF revealed that quercetin and kaempferol derivatives, caffeic acid and vitexin-2-O-rhamnoside were significant towards the tested bioactivities. Furthermore, principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) results showed that sonication and absolute ethanol are the preferable extraction method and ethanol ratio, respectively, to produce N. oleracea extracts with high phenolic levels and therefore high DPPH scavenging and α-glucosidase inhibitory activities.
CONCLUSION: Both PLS and RF are useful regression models in metabolomics studies. This work provides insight into the performance of different multivariate data analysis tools and the effects of different extraction conditions on the extraction of desired phenolics from plants. © 2017 Society of Chemical Industry.
PURPOSE: To develop and validate a fully automated neural network regression-based algorithm for segmentation of the LV in cardiac MRI, with full coverage from apex to base across all cardiac phases, utilizing both short axis (SA) and long axis (LA) scans.
STUDY TYPE: Cross-sectional survey; diagnostic accuracy.
SUBJECTS: In all, 200 subjects with coronary artery diseases and regional wall motion abnormalities from the public 2011 Left Ventricle Segmentation Challenge (LVSC) database; 1140 subjects with a mix of normal and abnormal cardiac functions from the public Kaggle Second Annual Data Science Bowl database.
FIELD STRENGTH/SEQUENCE: 1.5T, steady-state free precession.
ASSESSMENT: Reference standard data generated by experienced cardiac radiologists. Quantitative measurement and comparison via Jaccard and Dice index, modified Hausdorff distance (MHD), and blood volume.
STATISTICAL TESTS: Paired t-tests compared to previous work.
RESULTS: Tested against the LVSC database, we obtained 0.77 ± 0.11 (Jaccard index) and 1.33 ± 0.71 mm (MHD), both metrics demonstrating statistically significant improvement (P < 0.001) compared to previous work. Tested against the Kaggle database, the signed difference in evaluated blood volume was +7.2 ± 13.0 mL and -19.8 ± 18.8 mL for the end-systolic (ES) and end-diastolic (ED) phases, respectively, with a statistically significant improvement (P < 0.001) for the ED phase.
DATA CONCLUSION: A fully automated LV segmentation algorithm was developed and validated against a diverse set of cardiac cine MRI data sourced from multiple imaging centers and scanner types. The strong performance overall is suggestive of practical clinical utility.
LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.
Patients and methods: Participants received either usual care or CCM by a team of health care professionals including pharmacists, nurses, dietitians, and general practitioners. The participants in the intervention group received medication counseling, adherence, and dietary advice from the health care team. The QoL was measured using the EQ-5D (EuroQoL-five dimension, health-related quality of life questionnaire) and comparison was made between usual care and intervention groups at the beginning and end of the study at 6 months.
Results: Mean (standard deviation) EQ-5D index scores improved significantly in the intervention group (0.92±0.10 vs 0.95±0.08; P≤0.01), but not in the usual care group (0.94±0.09 vs 0.95±0.09; P=0.084). Similarly, more participants in the intervention group reported improvements in their QoL compared with the usual care group, especially in the pain/discomfort and anxiety/depression dimensions.
Conclusion: The implementation of the CCM resulted in significant improvement in QoL. An interdisciplinary team CCM approach should be encouraged, to ultimately result in behavior changes and improve the QoL of the patients.
PURPOSE: To develop 3D personalized left ventricular (LV) models and thickening assessment framework for assessing regional wall thickening dysfunction and dyssynchrony in AMI patients.
STUDY TYPE: Retrospective study, diagnostic accuracy.
SUBJECTS: Forty-four subjects consisting of 15 healthy subjects and 29 AMI patients.
FIELD STRENGTH/SEQUENCE: 1.5T/steady-state free precession cine MRI scans; LGE MRI scans.
ASSESSMENT: Quantitative thickening measurements across all cardiac phases were correlated and validated against clinical evaluation of infarct transmurality by an experienced cardiac radiologist based on the American Heart Association (AHA) 17-segment model.
STATISTICAL TEST: Nonparametric 2-k related sample-based Kruskal-Wallis test; Mann-Whitney U-test; Pearson's correlation coefficient.
RESULTS: Healthy LV wall segments undergo significant wall thickening (P 50% transmurality) underwent remarkable wall thinning during contraction (thickening index [TI] = 1.46 ± 0.26 mm) as opposed to healthy myocardium (TI = 4.01 ± 1.04 mm). For AMI patients, LV that showed signs of thinning were found to be associated with a significantly higher percentage of dyssynchrony as compared with healthy subjects (dyssynchrony index [DI] = 15.0 ± 5.0% vs. 7.5 ± 2.0%, P
METHODS: Respondents were sampled with quotas for urbanicity, gender, age, and ethnicity to ensure representativeness of the Malaysian population. The study was conducted using a standardized protocol involving the EuroQol Valuation Technology (EQ-VT) computer-assisted interview system. Respondents were administered ten composite time trade-off (C-TTO) tasks and seven discrete choice experiment (DCE) tasks. Both linear main effects and constrained non-linear regression models of C-TTO-only data and hybrid models combining C-TTO and DCE data were explored to determine an efficient and informative model for value set prediction.
RESULTS: Data from 1125 respondents representative of the Malaysian population were included in the analysis. Logical consistency was present in all models tested. Using cross-validation, eight-parameter models for C-TTO only and C-TTO + DCE hybrid data displayed greater out-of-sample predictive accuracy than their 20-parameter, main-effect counterparts. The hybrid eight-parameter model was chosen to represent the Malaysian value set, as it displayed greater out-of-sample predictive accuracy over C-TTO data than the C-TTO-only model, and produced more precise estimates. The estimated value set ranged from - 0.442 to 1.
CONCLUSIONS: The constrained eight-parameter hybrid model demonstrated the best potential in representing the Malaysian value set. The presence of the Malaysian EQ-5D-5L value set will facilitate its application in research and health technology assessment activities.