METHODS AND RESULT: The pure culture of K. nataicola was obtained from yeast-glucose-calcium carbonate (YGC) agar, followed by genomic DNA extraction, and subjected to whole genome sequencing on a Nanopore flongle flow cell. The genome of K. nataicola consists of a 3,767,936 bp chromosome with six contigs and 4,557 protein coding sequences. The maximum likelihood phylogenetic tree and average nucleotide identity analysis confirmed that the bacterial isolate was K. nataicola. The gene annotation via RAST server discovered the presence of cellulose synthase, along with three genes associated with lactate utilization and eight genes involved in lactate fermentation that could potentially contribute to the increase in acid concentration during BC synthesis.
CONCLUSION: A more comprehensive genome study of K. nataicola may shed light into biological pathway in BC productivity as well as benefit the analysis of metabolites generated and understanding of biological and chemical interactions in BC production later.
METHODS: The Multi-Layered Perceptron (MLP) neural network was used to predict the dissolution profiles of theophylline pellets containing different ratios of microcrystalline cellulose (MCC) and glyceryl monostearate (GMS). The concepts of leave-one-out as well as a time-point by time-point estimation basis were used to predict the rate of drug release for each matrix ratio. All the data were used for training, except for one set which was selected to compare with the predicted output. The closeness between the predicted and the reference dissolution profiles was investigated using similarity factor (f2).
RESULTS: The f2 values were all above 60, indicating that the predicted dissolution profiles were closely similar to the dissolution profiles obtained from physical experiments.
CONCLUSION: The MLP network could be used as a model for predicting the dissolution profiles of matrix-controlled release theophylline pellet preparation in product development.