Turkish coffee suppresses the progression of C6 glioma cells via the activation of apoptosis

Abstract

Glioma is the most invasive form of brain tumor and usually results in death within months of diagnosis. C6 glioma cells are frequently used in glioblastoma multiform studies because they are cells with different malignant glioblastoma features. Coffee is one of the most pop-ular beverages consumed in large quantities. Recent research has shown the functional and protective potential properties of coffee as well as its stimulatory effect. Coffee blending and grinding processes change the antioxidant composition of coffee. The main characteristic sep-arating Turkish Coffee (TC), which is formed from freshly roasted pulverized coffee beans, from the other coffee types is the brewing method. Our aim in this study is to investigate the antioxidant and apoptotic effects of TC prepared with the traditional method on C6 glioma cells, which are glial cells derived from rat brain with glioma. Cell viability in C6 glioma cells treated with TC at different concentrations (0-8000 µg/ml) was analyzed by the MTT method. According to MTT results, three doses (10000, 15000, and 20000 µg/ml) of TC were applied to the cells and the untreated cells were considered the control. Total oxidant and antioxidant statuses (TOS, TAS) and oxidative stress markers were determined. Caspase-3, caspase-8, and caspase-9 mRNA expressions were detected by using quantitative real-time PCR. It was de-termined that the application of TC at concentrations of 4000 µg/ml and above to C6 glioma cells inhibited cell proliferation depending on the concentration. Caspase 3, caspase-8, and caspase-9 mRNA expression levels increased in C6 glioma cells treated with TC at concen-trations of 10000 and 15000 µg/ml as compared to control cells. TAS and TOS levels were unchanged, while protein carbonyl levels increased in TC treated C6 glioma cells compared to the control group. These findings suggested that TC may induce apoptosis by changing caspases expressions and inducing protein oxidation. Thus, it can be thought that TC may prevent the proliferation of C6 glioma cells.

Keywords

• Turkish Coffee
• C6 Glioma
• Antioxidant
• Apoptosis
• Oxidative Stress

References

1. REFERENCES
2. [1] Chandwani V, Agrawal V, Nagar R. Applications of soft computing in civil engineering: a review. Int J Comput Appl 2013;81:1320.
3. [2] Armaghani DJ, Hatzigeorgiou GD, Karamani C, Skentou A, Zoumpoulaki I, Asteris PG. Soft computing-based techniques for concrete beams shear strength. Procedia Struct Integr 2019;17:924933.
4. [3] Naderpour H, Nagai K, Haji M, Mirrashid M. Adaptive neuro-fuzzy inference modelling and sensitivity analysis for capacity estimation of fiber reinforced polymer-strengthened circular reinforced concrete columns. Expert Syst 2019;36.
5. [4] Uzunoğlu M, Kap T. Prediction of concrete compressive strength in buildings that would be reinforced by fuzzy logic. Int J Phys Sci 2012;7:51935201.
6. [5] Tekeli H, Korkmaz KA, Demir F, Carhoglu AI. Comparison of critical column buckling load in regression, fuzzy logic and ANN based estimations. J Intell Fuzzy Syst 2014;26:10771087.
7. [6] Mirrashid M, Naderpour H. Recent trends in prediction of concrete elements behavior using soft computing (2010–2020). Arch Comput Methods Eng 2020;121.
8. [7] Garzón-Roca J, Marco CO, Adam JM. Compressive strength of masonry made of clay bricks and cement mortar: Estimation based on Neural Networks and Fuzzy Logic. Eng Struct 2013;48:2127.

9. [8] Ozkul S, Ayoub A, Altunkaynak A. Fuzzy-logic based inelastic displacement ratios of degrading RC structures. Eng Struct 2014;75:590603.
10. [9] Doran B, Yetilmezsoy K, Murtazaoglu S. Application of fuzzy logic approach in predicting the lateral confinement coefficient for RC columns wrapped with CFRP. Eng Struct 2015;88:7491.
11. [10] Naderpour H, Alavi SA. A proposed model to estimate shear contribution of FRP in strengthened RC beams in terms of Adaptive Neuro-Fuzzy Inference System. Compos Struct 2017;170:215227.
12. [11] Golafshani EM, Rahai A, Sebt MH, Akbarpour H. Prediction of bond strength of spliced steel bars in concrete using artificial neural network and fuzzy logic. Constr Build Mater 2012;36:411418.
13. [12] ud Darain KM, Jumaat MZ, Hossain MA, Hosen MA, Obaydullah M, Huda MN, Hossain I. Automated serviceability prediction of NSM strengthened structure using a fuzzy logic expert system. Expert Syst Appl 2015;42:376389.
14. [13] Amani J, Moeini R. Prediction of shear strength of reinforced concrete beams using adaptive neuro-fuzzy inference system and artificial neural network. Scientia Iranica 2012;19:242248.
15. [14] De Iuliis M, Kammouh O, Cimellaro GP, Tesfamariam S. Downtime estimation of building structures using fuzzy logic. Int J Disaster Risk Reduct 2019;34:196208.
16. [15] Cao Y, Fan Q, Azar SM, Alyousef R, Yousif ST, Wakil K, Alaskar A. Computational parameter identification of strongest influence on the shear resistance of reinforced concrete beams by fiber reinforcement polymer. Struct 2020;27:118127.
17. [16] Allali SA, Abed M, Mebarki A. Post-earthquake assessment of buildings damage using fuzzy logic. Eng Struct 2018;166:117127.
18. [17] Cao Y, Zandi Y, Rahimi A, Petković D, Denić N, Stojanović J, Assilzadeh H. Evaluation and monitoring of impact resistance of fiber reinforced concrete by adaptive neuro fuzzy algorithm. Struct 2021;34:37503756.
19. [18] Şen Z. Rapid visual earthquake hazard evaluation of existing buildings by fuzzy logic modeling. Expert Syst Appl 2010;37:56535660.
20. [19] Şen Z. Supervised fuzzy logic modeling for building earthquake hazard assessment. Expert Syst Appl 2011;38:1456414573.
21. [20] Harirchian E, Lahmer T. Developing a hierarchical type-2 fuzzy logic model to improve rapid evaluation of earthquake hazard safety of existing buildings. Struct 2020;28:13841399.
22. [21] Choi SK, Tareen N, Kim J, Park S, Park I. Real-time strength monitoring for concrete structures using EMI technique incorporating with fuzzy logic. Appl Sci 2018;8:75.
23. [22] Chao CJ, Cheng FP. Fuzzy pattern recognition model for diagnosing cracks in RC structures. J Comput Civ Eng 1998;12:111119.
24. [23] Elenas A, Vrochidou E, Alvanitopoulos P, Andreadis I. Classification of seismic damages in buildings using fuzzy logic procedures. In: Computational Methods in Stochastic Dynamics. Springer; 2013. p. 335-344.
25. [24] Cukaric A, Camagic I, Dutina V, Milkic Z, Jovic S. Parameters ranking based on influence on dynamical strength of ultra-high performance concrete by neuro fuzzy logic. Struct Concr 2019;433:17.
26. [25] Govardhan P, Kalapatapu P, Pasupuleti VDK. Identification of Multiple Cracks on Beam using Fuzzy Logic. In: 2021 International Conference on Emerging Techniques in Computational Intelligence (ICETCI); 2021 Aug. p. 165169.
27. [26] Khoshnoudian F, Molavi-Tabrizi A. Responses of isolated building with MR Dampers and Fuzzy Logic. Int J Civ Eng 2012;10.
28. [27] Zabihi-Samani M, Ghanooni-Bagha M. Optimal semi-active structural control with a wavelet-based cuckoo-search fuzzy logic controller. Iran J Sci Technol Trans Civ Eng 2019;43:619634.
29. [28] Elbeltagi E, Hosny OA, Elhakeem A, Abd-Elrazek ME, Abdullah A. Selection of slab formwork system using fuzzy logic. Constr Manag Econ 2011;29:659670.
30. [29] Sung YC, Su CK. Fuzzy genetic optimization on performance-based seismic design of reinforced concrete bridge piers with single-column type. Optim Eng 2010;11:471496.
31. [30] Akintunde OP. Fuzzy Logic Design Approach for A Singly Reinforced Concrete Beam. J Civ Eng Res Technol 2021;3:14.
32. [31] Öztekin E. Fuzzy inverse logic: part-1. introduction and bases. Gümüşhane Univ Fen Bilim Derg 2021;11:675691.
33. [32] Öztekin E. Fuzzy inverse logic: part-2. validation and evaluation of the method. Gümüşhane Univ Fen Bilim Derg 2021;11:768791.
34. [33] Mamdani EH, Assilian S. An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man-Mach Stud 1975;7:113.
35. [34] Mamdani EH. Advances in the linguistic synthesis of fuzzy controllers. Int J Man-Mach Stud 1976;8:669678.
36. [35] Zadeh LA. Information and control. Fuzzy Sets 1965;8:338353.
37. [36] Zadeh LA. Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans Syst Man Cybern 1973;:2844.
38. [37] Zadeh LA. The concept of a linguistic variable and its application to approximate reasoning-III. Inf Sci 1975;9:4380.

39. [38] Dong WM, Wong FS. Fuzzy weighted averages and implementation of the extension principle. Fuzzy Sets Syst 1987;21:183199.

40. [39] EN 1992-1-2, 2004. Eurocode 2: Design of Concrete Structures - Part 1-2. 1st ed. Brussels: BSi
41. [40] TS 500-2000. Requirements for design and construction of reinforced concrete structures. Ankara, Turkey: Turkish Standards Institute; 2000.

42. [41] TBEC. Turkish Building Earthquake Code. Ankara, Turkey: T.C. Resmi Gazete; 2018.
43. [42] Visual Studio: Yazılım Geliştiricileri ve Ekipleri için IDE ve Kod Düzenleyicisi. Microsoft; Available at: https://visualstudio.microsoft.com/tr/
Accessed on Mar 13, 2024.