OBJECTIVE: This study investigated the metabolite changes along the developmental stages of a local stevia cultivar.
METHODOLOGY: Stevia leaves were harvested at 4 different developmental stages (early vegetative, late vegetative, budding, and flowering). Samples were then subjected to LC-MS metabolomics analysis to determine the metabolite variations.
RESULTS: A total of 55 metabolites, comprising phenolic acids, flavonoids, and terpenoids were identified by MS/MS analysis of the stevia leaf extracts, revealing a metabolite profile which was comparatively similar with those of cultivars grown in other countries. PLS-DA differentiated the early vegetative stage stevia leaf samples from those of the later stages by higher content of phenolic acids. The leaf metabolomes of the later 3 stages (late vegetative, budding, and flowering) were collectively richer in flavonoids. Meanwhile, the content of steviol glycosides is highest during the late vegetative and budding stages.
CONCLUSION: The present study provided, for the first time, a general overview of the metabolite variations with regard to the different developmental stages of stevia. The information may facilitate decision making of suitable harvesting times for higher yields of steviol glycosides or a more balanced metabolite profile in terms of pharmacologically useful metabolites.
METHODS: Samples of 54 Wistar rats were divided into six groups: C- control group without treatment; C + wounded group without treatment; TB wound group with Povidone-iodine treatment; TD wounded group with doxycycline treatment; TLB wounded group with 403 nm diode laser treatment; and TLR wounded group with 649 nm diode laser treatment. Mandibular samples were observed for the number of lymphocytes and fibroblasts cells, new blood vessels formation, Interleukin 1β, and Collagen 1α expression level.
RESULTS: Based on the histopathological test results, red laser diode treatment significantly increased the number of lymphocyte, fibroblast cells and the formation of new blood vessels. Meanwhile, immunohistochemical tests showed an increase in the expression of the Colagen-1α protein which plays a role in the formation of collagen for new tissues formation after damage, as well as a decrease in Interleukin-1β expression level. Blue laser is also able to show a positive effect on wound healing even though its penetration level into the tissue is lower compared to red laser.
CONCLUSION: The red diode laser 649 nm has been shown to accelerate the process of proliferation in wound healing post molar extraction based on histopathological and immunohistochemical test results.
METHOD: This study proposed a single-scale multi-input convolutional neural network (SSMICNN) method to classify ERP signals between aMCI patients with T2DM and the control group. Firstly, the 18-electrode ERP signal on alpha, beta, and theta frequency bands was extracted by using the fast Fourier transform, and then the mean, sum of squares, and absolute value feature of each frequency band were calculated. Finally, these three features are converted into multispectral images respectively and used as the input of the SSMICNN network to realize the classification task.
RESULTS: The results show that the SSMICNN can fuse MSI formed by different features, SSMICNN enriches the feature quantity of the neural network input layer and has excellent robustness, and the errors of SSMICNN can be simultaneously transmitted to the three convolution channels in the back-propagation phase. Comparison with Existing Method(s): SSMICNN could more effectively identify ERP signals from aMCI with T2DM from the control group compared to existing classification methods, including convolution neural network, support vector machine, and logistic regression.
CONCLUSIONS: The combination of SSMICNN and MSI can be used as an effective biological marker to distinguish aMCI patients with T2DM from the control group.