METHODS: The liquids were adsorbed on microcrystalline cellulose, and all developed formulations were compressed using 10.5 mm shallow concave round punches.
RESULTS: The resulting tablets were evaluated for different quality-control parameters at pre- and postcompression levels. Simvastatin showed better solubility in a mixture of oils and Tween 60 (10:1). All the developed formulations showed lower self-emulsification time (˂200 seconds) and higher cloud point (˃60°C). They were free of physical defects and had drug content within the acceptable range (98.5%-101%). The crushing strength of all formulations was in the range of 58-96 N, and the results of the friability test were within the range of USP (≤1). Disintegration time was within the official limits (NMT 15 min), and complete drug release was achieved within 30 min.
CONCLUSION: Using commonly available excipients and machinery, SEDDS-based tablets with better dissolution profile and bioavailability can be prepared by direct compression. These S-SEDDSs could be a better alternative to conventional tablets of simvastatin.
METHODS: This work proposes a convolutional neural network (CNN) model for classifying IDC and metastatic breast cancer. The study utilized a large-scale dataset of microscopic histopathological images to automatically perceive a hierarchical manner of learning and understanding.
RESULTS: It is evident that using machine learning techniques significantly (15%-25%) boost the effectiveness of determining cancer vulnerability, malignancy, and demise. The results demonstrate an excellent performance ensuring an average of 95% accuracy in classifying metastatic cells against benign ones and 89% accuracy was obtained in terms of detecting IDC.
CONCLUSIONS: The results suggest that the proposed model improves classification accuracy. Therefore, it could be applied effectively in classifying IDC and metastatic cancer in comparison to other state-of-the-art models.