Improving the 6-Aminopenicillanic acid release process using vermiculite-alginate biocomposite bead on drug delivery system.

The present study deals with developing vermiculite (VMT)-alginate (Alg) composites with different cross-linker concentrations (CaCl2) to deliver the controlled 6-aminopenicillin acid (6-APA). The Characterization of synthesized composites was conducted by Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD) analyses. Optimization attempts were explored via the response surface method (RSM) to best predict the actual amount of compound. The adsorption capacity of 6-APA onto this adsorbent was found to be 208.33 mg/g, which was higher than that for other clays. The equilibrium and Kinetic studies (chemical reaction and diffusion-based models) indicated that drug absorption on VMT-Alg is homogeneous with chemical interaction. An increase in cross-linker (CaCl2) concentration leads to improvement in the drug encapsulation efficiency while having no significant effect on loading efficiency. The in-vitro release of the pure drug shows a rapid burst release followed by 100% cumulative release within 6 h. Whereas, the synthesized drug with Alg substantially showed less release of 43% after 8 h. Release experiments revealed that the presence of the CaCl2 delayed the release of the 6-APA less than 35% after 12 h. The kinetic release of 6-APA is followed by the Korsmeyer-Peppas model based on Fick's law mechanism due to the kinetic exponent (n < 0.5). All studied composites antibacterial activity after 24 h exposure against E. Coli and S. aureus. The antibacterial activities of composites were evaluated by the halo of no growth. The results showed that the VMT-Alg-6APA composite had strong activity against Gram-positive and Gram-negative bacteria.

A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation.

The grade estimation is a quite important and money/time-consuming stage in a mine project, which is considered as a challenge for the geologists and mining engineers due to the structural complexities in mineral ore deposits. To overcome this problem, several artificial intelligence techniques such as Artificial Neural Networks (ANN) and Fuzzy Logic (FL) have recently been employed with various architectures and properties. However, due to the constraints of both methods, they yield the desired results only under the specific circumstances. As an example, one major problem in FL is the difficulty of constructing the membership functions (MFs).Other problems such as architecture and local minima could also be located in ANN designing. Therefore, a new methodology is presented in this paper for grade estimation. This method which is based on ANN and FL is called "Coactive Neuro-Fuzzy Inference System" (CANFIS) which combines two approaches, ANN and FL. The combination of these two artificial intelligence approaches is achieved via the verbal and numerical power of intelligent systems. To improve the performance of this system, a Genetic Algorithm (GA) - as a well-known technique to solve the complex optimization problems - is also employed to optimize the network parameters including learning rate, momentum of the network and the number of MFs for each input. A comparison of these techniques (ANN, Adaptive Neuro-Fuzzy Inference System or ANFIS) with this new method (CANFIS-GA) is also carried out through a case study in Sungun copper deposit, located in East-Azerbaijan, Iran. The results show that CANFIS-GA could be a faster and more accurate alternative to the existing time-consuming methodologies for ore grade estimation and that is, therefore, suggested to be applied for grade estimation in similar problems.