Bazı Salisiliden Anilin Schiff Bazlarının DMSO ve DMF Çözücülerinde Antibakteriyel Aktiviteleri
Yıl 2022,
Cilt: 3 Sayı: 2, 116 - 122, 25.11.2022
Tuğçe Deniz Karaca
,
Aytac Kocabas
,
Mehmet Abdulkadir Akay
Öz
Günümüzde en önemli problemlerden birisi bakterilerde gözlemlenen çoklu antibiyotik dirençliliğidir. Bu sorun ülkelerin hem sağlık sektörünü hem de ekonomisini etkilemektedir. Mevcut antibiyotiklerin etkisiz kalması sağlık harcamalarına ayrılan bütçenin artmasına sebep olmaktadır. Bu soruna çözüm olarak yeni antimikrobiyal etkiye sahip kimyasallar yeni araştırmaların odak noktası olmuştur. Bu araştırmaların odak noktasını doğal bitkisel kaynaklar oluştursa da antibakteriyel etkiye sahip yeni kimyasalların sentezlenmesi de ön plana çıkmaktadır. Bu kimyasallar arasında Schiff bazları önemli bir yer tutmaktadır. Bu çalışmada da salisiliden anilin Schiff bazı ve onun 2-sübstitüe iyot, klor ve brom halojen türevi Schiff bazlarının hem DMSO hem de DMF’de antibakteriyel etkileri gram negatif Escherichia coli ve gram pozitif Staphylococcus aureus’a karşı ölçülmüştür. DMF’nin aşırı toksik etkisi kimyasalların biyoaktivitesini perdelemiştir. Buna karşılık, DMSO’da çözdürülen kimyasallar antibakteriyel etki göstermişlerdir. Kimyasalların gram negatif E. coli’ ye karşı daha etkili oldukları gözlemlenmiştir. En düşük MİK değeri 135 µg/mL olarak belirlenmiş ve E. coli’ye karşı tüm Schiff bazları bu etkiyi göstermiştir. Dolayısıyla yeni sentezlenen Schiff bazların geleceğin antibiyotik çağının öncülleri olması muhtemeldir.
Kaynakça
- [1] Mirkin, B. (1998). Mathematical classification and clustering: From how to what and why. Classification, Data
analysis, and Data Highways, 172-181.
- [2] Wang, B., Miao, Y., Zhao, H., Jin, J., & Chen, Y. (2016). A biclustering-based method for market segmentation
using customer pain points. Engineering Applications of Artificial Intelligence, 47, 101-109.
- [3] Dhillon, I. S. (2001). Co-clustering documents and words using bipartite spectral graph partitioning. In
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, 269-
274.
- [4] Busygin, S., Prokopyev, O., & Pardalos, P. M. (2008). Biclustering in data mining. Computers & Operations
Research, 35(9), 2964-2987.
- [5] Cheng, Y., & Church, G. M. (2000). Biclustering of expression data. “International Conference on Intelligent
Systems for Molecular Biology” kongresinde sunulan bildiri, UC San Diego, California, USA.
- [6] Lazzeroni, L., & Owen, A. (2002). Plaid models for gene expression data. Statistica Sinica, 61-86.
- [7] Bergmann, S., Ihmels, J., & Barkai, N. (2003). Iterative signature algorithm for the analysis of large-scale gene
expression data. Physical Review E, 67(3), 031902.
- [8] Ben-Dor, A., Chor, B., Karp, R., & Yakhini, Z. (2003). Discovering local structure in gene expression data: the
order-preserving submatrix problem. Journal of Computational Biology, 10(3-4), 373-384.
- [9] Prelić, A., Bleuler, S., Zimmermann, P., Wille, A., Bühlmann, P., Gruissem, W., Zitzler, E. (2006). A systematic
comparison and evaluation of biclustering methods for gene expression data. Bioinformatics, 22(9), 1122-1129.
- [10] Li, G., Ma, Q., Tang, H., Paterson, A. H., & Xu, Y. (2009). QUBIC: a qualitative biclustering algorithm for
analyses of gene expression data. Nucleic Acids Research, 37(15), 101.
- [11] Hochreiter, S., Bodenhofer, U., Heusel, M., Mayr, A., Mitterecker, A., Kasim, A., Talloen, W. (2010). FABIA:
factor analysis for bicluster acquisition. Bioinformatics, 26(12), 1520-1527.
- [12] Chekouo, T., & Murua, A. (2015). The penalized biclustering model and related algorithms. Journal of Applied
Statistics, 42(6), 1255-1277.
- [13] Biswal, B. S., Mohapatra, A., & Vipsita, S. (2019). Ensemble Neighborhood Search (ENS) for biclustering of
gene expression microarray data and single cell RNA sequencing data. Journal of King Saud University-Computer
and Information Sciences, 5(2), 105-112.
- [14] Chowdhury, H. A., Ahmed, H. A., Bhattacharyya, D. K., & Kalita, J. K. (2020). NCBI: A Novel Correlation
Based Imputing Technique Using Biclustering. Computational Intelligence in Pattern Recognition, 1, 509-519.
- [15] Seridi, K., Jourdan, L., & Talbi, E.-G. (2012). Hybrid metaheuristic for multi-objective biclustering in microarray
data. “2012 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)”
kongresinde sunulan bildiri, San Diego, California, USA.
- [16] Al-Akwaa, F. M., Ali, M. H., & Kadah, Y. M. (2009). Bicat_plus: An automatic comparative tool for bi/clustering
of gene expression data obtained using microarrays. “2009 National Radio Science Conference” kongresinde sunulan
bildiri.
- [17] Chia, B. K. H., & Karuturi, R. K. M. (2010). Differential co-expression framework to quantify goodness of
biclusters and compare biclustering algorithms. Algorithms for Molecular Biology, 5(1), 23.
- [18] Karim, M. B., Kanaya, S., & Altaf-Ul-Amin, M. (2019). Implementation of BiClusO and its comparison with
other biclustering algorithms. Applied Network Science, 4(1), 1-15.
- [19] Liu, X., & Wang, L. (2006). Computing the maximum similarity bi-clusters of gene expression data.
Bioinformatics, 23(1), 50-56.
- [20] Padilha, V. A., & Campello, R. J. (2017). A systematic comparative evaluation of biclustering techniques.
BioMed Central Bioinformatics, 18(1), 55.
- [21] Seridi, K., Jourdan, L., & Talbi, E.-G. (2015). Using multiobjective optimization for biclustering microarray data.
Applied Soft Computing, 33, 239-249.
- [22] Goldberg, D. E., & Holland, J. H. (1988). Genetic algorithms and machine learning. Machine Learning, 3(2), 95-
99.
- [23] Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598),
671-680.
- [24] Alikar, N., Mousavi, S. M., Ghazilla, R. A. R., Tavana, M., & Olugu, E. U. (2017). Application of the NSGA-II
algorithm to a multi-period inventory-redundancy allocation problem in a series-parallel system. Reliability
Engineering & System Safety, 160, 1-10.
- [25] Vo-Duy, T., Duong-Gia, D., Ho-Huu, V., Vu-Do, H., & Nguyen-Thoi, T. (2017). Multi-objective optimization
of laminated composite beam structures using NSGA-II algorithm. Composite Structures, 168, 498-509.
- [26] Wang, B., Liang, Y., Zheng, T., Yuan, M., & Zhang, H. (2018). Multi-objective site selection optimization of the
gas-gathering station using NSGA-II. Process Safety and Environmental Protection, 119, 350-359.
- [27] Wang, S., Ma, S., & Duan, W. (2018). Seakeeping optimization of trimaran outrigger layout based on NSGA-II.
Applied Ocean Research, 78, 110-122.
- [28] Yang, Y., Cao, L., Wang, C., Zhou, Q., & Jiang, P. (2018). Multi-objective process parameters optimization of
hot-wire laser welding using ensemble of metamodels and NSGA-II. Robotics and Computer-Integrated
Manufacturing, 53, 141-152.
- [29] Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm:
NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182-197.
- [30] Türkşen, Ö. (2011). Çok Yanıtlı Yüzey Problemlerinin Çözümüne Bulanık ve Sezgisel Yaklaşım. Doktora
Tezi. Ankara Üniversitesi Fen bilimleri Enstitüsü. Ankara. 126.
- [31] Hwang, C., & Yoon, K. (1981). Multiple decision attribute making: Methods and applications. New York:
Springer-Verlag, 58-191.
- [32] Pamucar D. & Cirovic G. (2015). The selection of transport and handling resources in logistics centers using
multi-attributive border approximation area comparison (mabac). Expert Systems with Applications, 42(6), 3016-
3028.
- [33] Liu P., Li H., Wang P., & Liu J. (2016). Electre method and its application in multiple attribute decision making
based on ins. Journal of Shandong University of Finance and Economics, 28(2), 80-87.
- [34] Pontes, B., Girldez, R., & Aguilar-Ruiz, J. S. (2015). Quality measures for gene expression biclusters. Plos One,
10(3), 0115497.
- [35] Yip, K. Y., Cheung, D. W., & Ng, M. K. (2004). Harp: A practical projected clustering algorithm. IEEE
Transactions on Knowledge and Data Engineering, 16(11), 1387-1397.
- [36] Lashkargir, M., Monadjemi, S. A., & Dastjerdi, A. B. (2009). A new biclustering method for gene expersion data
based on adaptive multi-objective particle swarm optimization. In 2009 Second International Conference on
Computer and Electrical Engineering, 1, 559-563.
- [37] Liu, J., Li, Z., Hu, X., & Chen, Y. (2009, April). Biclustering of microarray data with MOSPO based on crowding
distance. In BMC bioinformatics, 10(4), 1-10.
- [38] Mitra, S., & Banka, H. (2006). Multi-objective evolutionary biclustering of gene expression data. Pattern
Recognition, 39(12), 2464-2477.
- [39] Talbi, E.-G. (2009). Metaheuristics: from design to implementation (74). New Jersey: John Wiley & Sons, 34-
48.
- [40] Türkşen, Ö., & Akgün, F. (2018). Genetik-Simpleks hibrit algoritması ile doğrusal olmayan regresyon model
parametrelerinin nokta tahmini. İstatistikçiler Dergisi: İstatistik ve Aktüerya, 11(2), 81-92.
- [41] Dale, J., Zhao, J., & Obafemi-Ajayi, T. (2019). Multi-objective optimization approach to find biclusters in gene
expression data. In 2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational
Biology (CIBCB), 1-8