METHODS: Observing anti-urolithiathic activity via in vitro nucleation and aggregation assay using a spectrophotometer followed by microscopic observation. A total of 12 methanolic extracts were tested to determine the potential extracts in anti-urolithiasis activities. Cystone was used as a positive control.
RESULTS: The results manifested an inhibition of nucleation activity (0.11 ± 2.32% to 55.39 ± 1.01%) and an aggregation activity (4.34 ± 0.68% to 58.78 ± 1.81%) at 360 min of incubation time. The highest inhibition percentage in nucleation assay was obtained by the Musa acuminate x balbiciana Colla cv "Awak Legor" methanolic pseudo-stem extract (2D) which was 55.39 ± 1.01%at 60 min of incubation time compared to the cystone at 30.87 ± 0.74%. On the other hand,the Musa acuminate x balbiciana Colla cv "Awak Legor" methanolic bagasse extract (3D) had the highest inhibition percentage in the aggregation assay incubated at 360 min which was obtained at 58.78 ± 1.8%; 5.53% higher than the cystone (53.25%).The microscopic image showed a great reduction in the calcium oxalate (CaOx) crystals formation and the size of crystals in 2D and 3D extracts, respectively, as compared to negative control.
CONCLUSIONS: The results obtained from this study suggest that the extracts are potential sources of alternative medicine for kidney stones disease.
RESULTS: In this research, chili pest and disease features extracted using the traditional approach were compared with features extracted using a deep-learning-based approach. A total of 974 chili leaf images were collected, which consisted of five types of diseases, two types of pest infestations, and a healthy type. Six traditional feature-based approaches and six deep-learning feature-based approaches were used to extract significant pests and disease features from the chili leaf images. The extracted features were fed into three machine learning classifiers, namely a support vector machine (SVM), a random forest (RF), and an artificial neural network (ANN) for the identification task. The results showed that deep learning feature-based approaches performed better than the traditional feature-based approaches. The best accuracy of 92.10% was obtained with the SVM classifier.
CONCLUSION: A deep-learning feature-based approach could capture the details and characteristics between different types of chili pests and diseases even though they possessed similar visual patterns and symptoms. © 2020 Society of Chemical Industry.
MATERIAL AND METHODS: A PRISMA-compliant systematic search of literature was done from the MEDLINE, CENTRAL, Science Direct, PubMed and Google Scholar. Literature that fulfilled eligibility criteria was identified. Data measuring plaque score and bleeding score were extracted. Qualitative and random-effects meta-analyses were conducted.
RESULTS: From 1736 titles and abstracts screened, eight articles were utilized for qualitative analysis, while five were selected for meta-analysis. The pooled effect estimates of SMD and 95% CI were -0.07 [-0.60 to 0.45] with an χ2 statistic of 0.32 (p = 0.0001), I2 = 80% as anti-plaque function and 95% CI were -2.07 [-4.05 to -0.10] with an χ2 statistic of 1.67 (p = 0.02), I2 = 82%.
CONCLUSION: S. persica chewing stick is a tool that could control plaque, comparable to a standard toothbrush. Further, it has a better anti-gingivitis effect and can be used as an alternative.