Over the years, advancement in ceramic-based dental restorative materials has led to the development of monolithic zirconia with increased translucency. The monolithic zirconia fabricated from nano-sized zirconia powders is shown to be superior in physical properties and more translucent for anterior dental restorations. Most in vitro studies on monolithic zirconia have focused mainly on the effect of surface treatment or the wear of the material, while the nanotoxicity of this material is yet to be explored. Hence, this research aimed to assess the biocompatibility of yttria-stabilized nanozirconia (3-YZP) on the three-dimensional oral mucosal models (3D-OMM). The 3D-OMMs were constructed using human gingival fibroblast (HGF) and immortalized human oral keratinocyte cell line (OKF6/TERT-2), co-cultured on an acellular dermal matrix. On day 12, the tissue models were exposed to 3-YZP (test) and inCoris TZI (IC) (reference material). The growth media were collected at 24 and 48 h of exposure to materials and assessed for IL-1β released. The 3D-OMMs were fixed with 10% formalin for the histopathological assessments. The concentration of the IL-1β was not statistically different between the two materials for 24 and 48 h of exposure (p = 0.892). Histologically, stratification of epithelial cells was formed without evidence of cytotoxic damage and the epithelial thickness measured was the same for all model tissues. The excellent biocompatibility of nanozirconia, as evidenced by the multiple endpoint analyses of the 3D-OMM, may indicate the potential of its clinical application as a restorative material.
Analyzing metabolic pathways in systems biology requires accurate kinetic parameters that represent the simulated in vivo processes. Simulation of the fermentation pathway in the Saccharomyces cerevisiae kinetic model help saves much time in the optimization process. Fitting the simulated model into the experimental data is categorized under the parameter estimation problem. Parameter estimation is conducted to obtain the optimal values for parameters related to the fermentation process. This step is essential because insufficient identification of model parameters can cause erroneous conclusions. The kinetic parameters cannot be measured directly. Therefore, they must be estimated from the experimental data either in vitro or in vivo. Parameter estimation is a challenging task in the biological process due to the complexity and nonlinearity of the model. Therefore, we propose the Artificial Bee Colony algorithm (ABC) to estimate the parameters in the fermentation pathway of S. cerevisiae to obtain more accurate values. A metabolite with a total of six parameters is involved in this article. The experimental results show that ABC outperforms other estimation algorithms and gives more accurate kinetic parameter values for the simulated model. Most of the estimated kinetic parameter values obtained from the proposed algorithm are the closest to the experimental data.
Rare diseases (RDs) are rare complex genetic diseases affecting a conservative estimate of 300 million people worldwide. Recent Next-Generation Sequencing (NGS) studies are unraveling the underlying genetic heterogeneity of this group of diseases. NGS-based methods used in RDs studies have improved the diagnosis and management of RDs. Concomitantly, a suite of bioinformatics tools has been developed to sort through big data generated by NGS to understand RDs better. However, there are concerns regarding the lack of consistency among different methods, primarily linked to factors such as the lack of uniformity in input and output formats, the absence of a standardized measure for predictive accuracy, and the regularity of updates to the annotation database. Today, artificial intelligence (AI), particularly deep learning, is widely used in a variety of biological contexts, changing the healthcare system. AI has demonstrated promising capabilities in boosting variant calling precision, refining variant prediction, and enhancing the user-friendliness of electronic health record (EHR) systems in NGS-based diagnostics. This paper reviews the state of the art of AI in NGS-based genetics, and its future directions and challenges. It also compare several rare disease databases.
The metabolic network is the reconstruction of the metabolic pathway of an organism that is used to represent the interaction between enzymes and metabolites in genome level. Meanwhile, metabolic engineering is a process that modifies the metabolic network of a cell to increase the production of metabolites. However, the metabolic networks are too complex that cause problem in identifying near-optimal knockout genes/reactions for maximizing the metabolite's production. Therefore, through constraint-based modelling, various metaheuristic algorithms have been improvised to optimize the desired phenotypes. In this paper, PSOMOMA was compared with CSMOMA and ABCMOMA for maximizing the production of succinic acid in E. coli. Furthermore, the results obtained from PSOMOMA were validated with results from the wet lab experiment.