‎Shannon Entropy Analysis of Serum C-Terminal Agrin Fragment as a Biomarker for Kidney Function‎: ‎Reference‎ ‎Ranges‎, ‎Healing Sequences and Insights

Transactions on Fuzzy Sets and Systems

مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 13 دی 1402   

نوع مقاله: Original Article

شناسه دیجیتال (DOI): 10.30495/tfss.2024.1984846.1069

نویسنده

Mehmet Sengonul^*

Department of Mathematics, Adyaman University, Adyaman, Trkiye.

چکیده

This article focuses on evaluating the success or failure of kidney transplantation using Shannon entropy‎, ‎fuzzy sets‎, ‎and Scaf‎. ‎The data for Scaf references used in this study for both healthy individuals and kidney transplant recipients have been collected from the relevant literature‎. ‎For both groups‎, ‎Scaf's Shannon entropy values have been calculated using an appropriate probability density function and formulation‎, ‎and sequences have been generated for CAF and Scr biomarkers from entropy values‎, ‎with findings interpreted‎. ‎These sequences are called healing sequences‎. ‎A case study demonstrating whether the transplant procedure was successful or unsuccessful was presented using sequences that we refer to as healing sequences‎. ‎In this context‎, ‎the utilization of mathematical tools such as fuzzy sets‎, ‎Shannon entropy‎, ‎and reference intervals becomes evident‎. ‎These tools provide a systematic and quantitative approach to assessing the outcomes of kidney transplantation‎. ‎By leveraging the principles of Shannon entropy‎, ‎we gain insights into the degree of unpredictability and fuzziness associated with biomarker values‎, ‎which can be indicative of the transplant's success‎.
‎Furthermore‎, ‎the concept of healing sequences provides a valuable framework for tracking the progression of patients post-transplantation‎. ‎By monitoring changes in CAF and Scr biomarkers over time‎, ‎healthcare professionals can make informed decisions and interventions to ensure the well-being of kidney transplant recipients‎.

کلیدواژه‌ها

Healing sequence‎؛ ‎Shannon entropy‎؛ ‎Fuzzy set‎؛ ‎Renal transplant‎؛ ‎Biomarker‎

مراجع

[1] Bardossy A. Blinowska A. Fuzzy reasoning in pacemaker control. In: Szczepaniak PS, Lisboa PJG, Kacprzyk J. (eds.) Fuzzy systems in medicine. Heidelberg. Germany: Physica-Verlag HD; 2000. p.451466.
[2] Bloch I. Fusion of Numerical and Structural Image Information in Medical Imaging in the Framework of Fuzzy Sets . In: Szczepaniak PS, Lisboa PJG, Kacprzyk J. (eds.) Fuzzy systems in medicine. Heidelberg. Germany: Physica-Verlag HD; 2000. p.429447.
[3] Clinical Pharmacology & Therapeutics. Commentary: Biomarkers and surrogate endpoints: Preferred definitions and conceptual framework. https://doi.org/10.1067/mcp.2001.113989 [Accessed 20 April 2023].
[4] Czogala E, Leski J. Entropy and energy measures of fuzziness in ecg signal processing, In: Szczepaniak PS, Lisboa PJG, Kacprzyk J. (eds.) Fuzzy systems in medicine. Heidelberg. Germany: Physica-Verlag HD; 2000. p.227245.
[5] Cekim Tunc S, Klnc G, Sengönül M, Kayaduman K. Logistic entropy of bone mineral density at diabetes and nondiabetes and new type visualizations. Annals of Fuzzy Mathematics and Informatics. 2022; (24)3: 271-280. DOI: https://doi.org/10.30948/afmi.2022.24.3.271
[6] De Luca A, Termini S. A definition of a nonprobabilistic entropy in the setting of fuzzy sets theory. Information and Control. 1972; 20: 301-312. DOI: https://doi.org/10.1016/S0019-9958(72)90199-4
[7] Good IJ. The Estimation of Probabilities: An Essay on Modern Bayesian Methods. Cambridge: The MIT Press Publishing; 1965.
[8] Hamedan F, Orooji A, Sanadgol H, Sheikhtaheri A. Clinical decision support system to predict chronic kidney disease: A fuzzy expert system approach. International journal of medical informatics. 2020; 138: 104-134. DOI: https://doi.org/10.1016/j.ijmedinf.2020.104134
[9] Huang QM, Tong XJ, Zeng S, Wang WK. Digital image resolution and entropy. 2007 International Conference on Machine Learning and Cybernetics, 01.10. 2007. Hong Kong. China: IEEE, 2007. p.15741577. DOI: https://doi.org/10.1109/ICMLC.2007.4370396
[10] Johanyk ZC, Kovcs S. Distance based similarity measures of fuzzy sets. In: 3rd Slovakian-Hungarian Joint Symposium on Applied Machine Intelligence, 2122 January 2005, Herl’any. Slovakia: Slovak Artificial Intelligence Society; 2005. p.265276.
[11] Kln G. Logistic Entropy of Trabecular Bone Score in SLE Patients and New Type Visualizations, Computational and Mathematical Methods in Medicine, vol. 2022, Article ID 3292249, 8 pages, 2022. DOI:
https://doi.org/10.1155/2022/3292249
[12] Li X, Zhao Z, Cheng HD. Fuzzy entropy threshold to breast cancer detection. Information Sciences Applications. 1995; 4(1): 49-56. DOI: https://doi.org/10.1016/1069-0115(94)00019-X.
[13] Linkens DA, Abbod MF, Backory JK, Shieh JS. Closed-loop control of anaesthesia using fuzzy logic. In: Szczepaniak PS, Lisboa PJG, Kacprzyk J. (eds.) Fuzzy systems in medicine. Heidelberg. Germany: Physica-Verlag HD; 2000. p.467483.
[14] Monti E, Sarto F, Sartori R, et al. C-terminal agrin fragment as a biomarker of muscle wasting and weakness: A narrative review. Journal of Cachexia, Sarcopenia and Muscle. 2023; 14(2): 730-744. DOI: https://doi.org/10.1002/jcsm.13189
[15] Murugesan G, Ahmed TI, Bhola J, et al. Fuzzy logic-based systems for the diagnosis of chronic kidney disease. BioMed Research International. 2022; Article ID 2653665, 15 pages. DOI: https://doi.org/10.1155/2022/2653665
[16] Norouzi J, Yadollahpour A, Mirbagheri SA, et al. Predicting renal failure progression in chronic kidney disease using integrated intelligent fuzzy expert system. Computational and Mathematical Methods in Medicine. 2016(3): 1-9. DOI: https://doi.org/10.1155/2016/6080814
[17] Rakus-Andersson E, Zettervall H. Different Approaches to Operations on Continuous Fuzzy Numbers. Warszawa. EXIT Publishing House; 2008.
[18] Rutkowska D, Starczewski A. Fuzzy inference neural networks and their applications to medical diagnosis. In: Szczepaniak PS, Lisboa PJG, Kacprzyk J. (eds.) Fuzzy systems in medicine. Heidelberg. Germany: Physica-Verlag HD; 2000. p.503518.
[19] Sadegh K, Zadeh AL. The Construction of Fuzziness. In: Seising R, Tabacchi ME (eds) Fuzziness and Medicine: Philosophical Reflections and Application Systems in Health Care. Verlag Berlin Heidelberg. Germany: Springer; 2013.
[20] Sengönül M. An application of fuzzy sets to veterinary medicine. Theory & Applications of Mathematics & Computer Science. 2016; 6(1): 1-12.
[21] Sengönül M, Kayaduman K, Zararsz Z, Atpnar S. The entropies of the sequences of fuzzy sets and applications of entropy to cardiography. International Journal of Mathematical Modelling & Computations. 2016; 6(2): 159-173.
[22] Taherkhani N, Sepehri MM, Khasha R, Shafaghi Sh. Ranking patients on the kidney transplant waiting list based on fuzzy inference system. BMC Nephrology. 2022; 23(1): 1-14. DOI: https://doi.org/10.1186/s12882-022-02662-5
[23] Wang WJ, Chui CH. The entropy change of fuzzy numbers with arithmetic operations. Fuzzy Sets and Systems. 1999; 103(3): 357-366. DOI: https://doi.org/10.1016/S0165-0114(99)80001-3
[24] Woroniecki RP, Schnaper HW. Progression of glomerular and tubular disease in pediatrics. Seminars in Nephrology. 2009; 29(4): 412-424. DOI: https://doi.org/10.1016/j.semnephrol.2009.03.016
[25] Yu D, Li HX, Liu Y, et al. The reference intervals for serum c-terminal agrin fragment in healthy individuals and as a biomarker for renal function in kidney transplant recipients. Journal of Clinical Laboratory Analysis. 2017; 31(3): e22059. DOI: 10.1002/jcla.22059

[26] Zadeh LA. Fuzzy sets. Information and Control. 1965; 8(3): 338-353. DOI: http://dx.doi.org/10.1016/S0019-9958(65)90241-X
[27] Zimmermann HJ. Fuzzy Set Theory-and Its Applications. Boston: Kluwer Academic Publishers; 1996.