RESULTS: As pod developed, cacao exhibited a rise with the peak of flavonol occurring at months 4 and 5 after pod maturity was initiated while nitrogen balance showed a decreasing trend during maturity. Cacao pods contained high chlorophyll as they developed but chlorophyll content declined significantly on pods that ripened at month 5.
CONCLUSION: Cacao pods harvested at months 4 and 5 can be considered as commercially-ready as the beans have developed good quality and comply with the Malaysian standard on cacao bean specification. Thus, cacao pods can be harvested earlier when they reach maturity at month 4 after pod emergence to avoid germinated beans and over fermentation in ripe pods harvested at month 5. © 2018 Society of Chemical Industry.
RESULTS: As pods developed, flavonol accumulated while nitrogen content degraded. Mature pods produced beans with a higher flavonol, catechin, and total phenolic content (TPC). As fermentation progressed, the beans' fat, TPC, antioxidant activity, and catechin content increased, regardless of pod maturity at harvest. Free fatty acid (FFA) levels were highest in 5 day fermented beans. The 3 day fermented beans contained significantly higher epicatechin, with lower FFA content. Chocolate made from mature beans with 3 day fermentation was more pleasant as it scored the highest in flavor intensity and complexity and the lowest in acidity and astringency.
CONCLUSION: This study suggests that cacao pods harvested at the mature stage with further fermentation for 3 days under controlled temperatures produce specialty beans with potential health benefits. © 2021 Society of Chemical Industry.
OBJECTIVES: The objective of this work was to compare quantification techniques for CEST imaging that specifically separate APT and NOE effects for application in the clinical setting. Towards this end a methodological comparison of different CEST quantification techniques was undertaken in healthy subjects, and around clinical endpoints in a cohort of acute stroke patients.
METHODS: MRI data from 12 patients presenting with ischaemic stroke were retrospectively analysed. Six APT quantification techniques, comprising model-based and model-free techniques, were compared for repeatability and ability for APT to distinguish pathological tissue in acute stroke.
RESULTS: Robustness analysis of six quantification techniques indicated that the multi-pool model-based technique had the smallest contrast between grey and white matter (2%), whereas model-free techniques exhibited the highest contrast (>30%). Model-based techniques also exhibited the lowest spatial variability, of which 4-pool APTR∗ was by far the most uniform (10% coefficient of variation, CoV), followed by 3-pool analysis (20%). Four-pool analysis yielded the highest ischaemic core contrast-to-noise ratio (0.74). Four-pool modelling of APT effects was more repeatable (3.2% CoV) than 3-pool modelling (4.6% CoV), but this appears to come at the cost of reduced contrast between infarct growth tissue and normal tissue.
CONCLUSION: The multi-pool measures performed best across the analyses of repeatability, spatial variability, contrast-to-noise ratio, and grey matter-white matter contrast, and might therefore be more suitable for use in clinical imaging of acute stroke. Addition of a fourth pool that separates NOEs and semisolid effects appeared to be more biophysically accurate and provided better separation of the APT signal compared to the 3-pool equivalent, but this improvement appeared be accompanied by reduced contrast between infarct growth tissue and normal tissue.
METHODS: In this work, we developed deep convolutional neural network (CNN) based heterogeneous ensemble models for automated analysis of renal histopathological images without detailed annotations. The proposed method would first segment the histopathological tissue into patches with different magnification factors, then classify the generated patches into normal and tumor tissues using the pre-trained CNNs and lastly perform the deep ensemble learning to determine the final classification. The heterogeneous ensemble models consisted of CNN models from five deep learning architectures, namely VGG, ResNet, DenseNet, MobileNet, and EfficientNet. These CNN models were fine-tuned and used as base learners, they exhibited different performances and had great diversity in histopathological image analysis. The CNN models with superior classification accuracy (Acc) were then selected to undergo ensemble learning for the final classification. The performance of the investigated ensemble approaches was evaluated against the state-of-the-art literature.
RESULTS: The performance evaluation demonstrated the superiority of the proposed best performing ensembled model: five-CNN based weighted averaging model, with an Acc (99%), specificity (Sp) (98%), F1-score (F1) (99%) and area under the receiver operating characteristic (ROC) curve (98%) but slightly inferior recall (Re) (99%) compared to the literature.
CONCLUSIONS: The outstanding robustness of the developed ensemble model with a superiorly high-performance scores in the evaluated metrics suggested its reliability as a diagnosis system for assisting the pathologists in analyzing the renal histopathological tissues. It is expected that the proposed ensemble deep CNN models can greatly improve the early detection of renal cancer by making the diagnosis process more efficient, and less misdetection and misdiagnosis; subsequently, leading to higher patients' survival rate.
METHODS: Five APT quantification methods, including asymmetry analysis and its variants as well as two Lorentzian model-based methods, were applied to data acquired from six rats that underwent middle cerebral artery occlusion scanned at 9.4T. Diffusion and perfusion-weighted images, and water relaxation time maps were also acquired to study the relationship of these conventional imaging modalities with the different APT quantification methods.
RESULTS: The APT ischemic area estimates had varying sizes (Jaccard index: 0.544 ≤ J ≤ 0.971) and had varying correlations in their distributions (Pearson correlation coefficient: 0.104 ≤ r ≤ 0.995), revealing discrepancies in the quantified ischemic areas. The Lorentzian methods produced the highest contrast-to-noise ratios (CNRs; 1.427 ≤ CNR ≤ 2.002), but generated APT ischemic areas that were comparable in size to the cerebral blood flow (CBF) deficit areas; asymmetry analysis and its variants produced APT ischemic areas that were smaller than the CBF deficit areas but larger than the apparent diffusion coefficient deficit areas, though having lower CNRs (0.561 ≤ CNR ≤ 1.083).
CONCLUSION: There is a need to further investigate the accuracy and correlation of each quantification method with the pathophysiology using a larger scale multi-imaging modality and multi-time-point clinical study. Future studies should include the magnetization transfer ratio asymmetry results alongside the findings of the study to facilitate the comparison of results between different centers and also the published literature.
METHODS: Diffusion-weighted imaging, perfusion-weighted imaging, and chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) data were acquired from five rats that underwent scans at 9.4 T after middle cerebral artery occlusion.
RESULTS: The apparent diffusion coefficient (ADC), cerebral blood flow (CBF), and apparent exchange-dependent relaxations (AREX) at 3.5 ppm and NOE(-1.6 ppm) were quantified. AREX(3.5 ppm) and NOE(-1.6 ppm) were found to be hypointense and exhibited different signal patterns within the ischemic tissue. The NOE(-1.6 ppm) deficit areas were equal to or larger than the ADC deficit areas, but smaller than the AREX(3.5 ppm) deficit areas. This suggested that NOE(-1.6 ppm) might further delineate the acidotic tissue estimated using AREX(3.5 ppm). Since NOE(-1.6 ppm) is closely related to membrane phospholipids, NOE(-1.6 ppm) potentially highlighted at-risk tissue affected by lipid peroxidation and membrane damage. Altogether, the ADC/NOE(-1.6 ppm)/AREX(3.5 ppm)/CBF mismatches revealed four zones of increasing sizes within the ischemic tissue, potentially reflecting different pathophysiological information.
CONCLUSIONS: Using CEST coupled with ADC and CBF, the ischemic tissue may thus potentially be separated into four zones to better understand the pathophysiology after stroke and improve ischemic tissue fate definition. Further verification of the potential utility of NOE(-1.6 ppm) may therefore lead to a more precise diagnosis.