NEW SGEM Scientific Online eLibrary
After a long time work, our pride is the absolutely new SGEM Online eLibrary, available here. The Library is created according the rules of the International databases, with a whole needed information and ability to export the article in BIB format. Try and spread your experience with us.
Till we complete the new library with papers from all the years, you can use this link /Full Old Library/, if papers that you need are not visible yet here.

APPLICATION OF NEURAL NETWORK MODEL FOR PREDICTING THE ANTIBACTERIAL ACTIVITY OF ALGINATE-CHITOSAN SPONGES

Gordienko, M.; Palchikova, V.; Galusina, A.; Kalenov, S.; Menshutina, N.
Abstract:
Medical materials based on natural polysaccharides can be used as hemostatic sorbents and dressings. In this work, some samples of sponges had been obtained based on sodium alginate and chitosan. The concentration of sodium alginate in the solution, the ratio of alginate and chitosan were varied during an experiment. Polysaccharide cross-linking was carried out by inotropic gelation or by combining ionotropic gelation with ionic substitution. According to the literature, a chitosan have antibacterial properties against many microorganisms. To enhance the antibacterial effect of sponges, a silver nanoparticles were incorporated into some samples. The silver nanoparticles had been obtained by microbiological synthesis by using three different cultures of fungi: F.nivale, F.oxysporum and P.glabrum. The antibacterial activity of sponges was tested on three types of microorganisms: B.cereus, S.aureus and P.aeruginosa. The results did not allow us to reveal a direct correlation between the composition of the sponge and the width of the zone of inhibition. To predict antibacterial activity, the use of a neural network model has been proposed. To build a model, an array of data containing information on 48 samples was divided into test and training datasets. Were built 3 neural networks (separately for each type of microorganisms) with a different number of hidden layers and the number of neurons in them. Network training was performed by back propagating the error. Validation of the models was performed on a test sample. The standard deviation was chosen as the criterion. In general, satisfactory results were obtained only for the culture of S.saureus. For this culture, with the best result, the network 4-4-1 gave an error on the training and test sample of 10%; and the network 4-2-3-1 ? 15%. To increase the accuracy of the model is required to increase the amount of data.
SGEM Research areas:
Year:
2018
Type of Publication:
In Proceedings
Keywords:
alginate-chitosan sponges; microbiology silver; artificial neural networks; Inhibition zones; bacterial strain
Volume:
18
SGEM Book title:
18th International Multidisciplinary Scientific GeoConference SGEM2018
Book number:
6.4
SGEM Series:
International Multidisciplinary Scientific GeoConference-SGEM
Pages:
71-78
Publisher address:
51 Alexander Malinov blvd, Sofia, 1712, Bulgaria
SGEM supporters:
Bulgarian Acad Sci; Acad Sci Czech Republ; Latvian Acad Sci; Polish Acad Sci; Russian Acad Sci; Serbian Acad Sci & Arts; Slovak Acad Sci; Natl Acad Sci Ukraine; Natl Acad Sci Armenia; Sci Council Japan; World Acad Sci; European Acad Sci, Arts & Letters; Ac
Period:
3 – 6 December, 2018
ISBN:
978-619-7408-71-3
ISSN:
1314-2704
Conference:
18th International Multidisciplinary Scientific GeoConference SGEM2018, 3 – 6 December, 2018
DOI:
10.5593/sgem2018V/6.4/S08.010
Hits: 51
/** LightBox **/ /** END LightBox **/ /** SweetAlert **/ /** **/ /** SweetAlert2 **/ /** END SweetAlert2 **/