EXPLORING SELJE TOPOLOGICAL SPACE: APPLICATION IN OPTIMIZING RENAL HEALTH
DOI:
https://doi.org/10.29121/shodhkosh.v5.i6.2024.2737Keywords:
Renal Dysfunction, Topological Analysis, Etiological Factors, Risk Assessment, 2000 AMS Classification: 54A05, 54B05Abstract [English]
Renal health is vital for maintaining overall body homeostasis, and dysfunction in the kid- neys can lead to severe complications, including chronic kidney disease and cardiovascular problems. Identifying the factors that contribute most significantly to renal impairment is criti- cal for early diagnosis and effective treatment. This study utilizes the Selje Topological Space framework to analyze renal health factors. Data was collected over several months from pa- tients with renal dysfunction, focusing on key contributors such as hemoglobin, blood urea nitrogen, azotocreatinine, estimated glomerular filtration rate (eGFR), and potassium levels. The Selje Topological Space approach was used to identify the primary determinants of kidney dysfunction. The analysis revealed that urea and potassium levels were the most significant factors affecting kidney function under normal sugar conditions. In cases where blood sugar levels were abnormal, urea was identified as the dominant contributor to renal impairment. The study’s findings emphasize the importance of monitoring urea and potassium levels in patients at risk for renal disease. These insights can guide clinical interventions and improve strategies for preventing kidney dysfunction in patients with both normal and abnormal blood sugar lev- els.
References
Borg, I., & Groenen, P. J. (2021). Multidimensional scaling and topology for analyzing patient data in chronic diseases. Statistics in Medicine, 40(16), 3422-3436.
Caldas, M. (2003). A note on some applications of α open sets. International Journal of Mathematics and Mathematical Sciences, 2, 125-130. DOI: https://doi.org/10.1155/S0161171203203161
Chandrasekar, S. (2019). On Micro Topological Spaces. Journal of New Theory, 26, 23- 31.
Elamin, M. B., & Nair, V. (2019). Renal health and management of chronic kidney dis- ease: Emerging trends and challenges. Kidney International Supplements, 9(1), 33-40.
Gao, Y., & Wu, J. (2019). Topological insights into chronic disease networks: A graph- theoretical approach. Network Biology, 9(2), 85-99.
Hamlett, T. R., & Rose, D. (1990). Topological properties. International Journal of Math- ematics and Mathematical Sciences, 13(3), 507-512. DOI: https://doi.org/10.1155/S0161171290000734
Jeyanthi, V., & Selva Nandhini, N. (2023). On Selje Topological Spaces. Indian Journal of Natural Sciences, 14(80), 64187-64193.
Jiang, W., & Liu, L. (2021). The impact of diabetes on renal health: Insights from recent studies. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 15(6), 1045- 1052.
Khan, S. H., & Barak, A. (2020). Topological modeling of chronic disease progression: A case study on diabetes mellitus. Journal of Mathematical Biology, 81(4), 847-865.
Kovesdy, C. P. (2020). Chronic kidney disease: The case for early diagnosis and the role of primary care. American Journal of Kidney Diseases, 75(5), 730-738.
Levine, N. (1970). Generalized closed sets in topology. Rendiconti del Circolo Matem- atico di Palermo, 19(2), 89-96. DOI: https://doi.org/10.1007/BF02843888
Li, X., & Liu, Y. (2020). A topological approach to chronic disease management using big data analytics. Journal of Healthcare Engineering, 2020, 1-10.
Mora, C., & Gupta, A. (2023). Nutritional interventions in chronic kidney disease: A systematic review. Nutrients, 15(2), 301.
Smith, R. T., & Jones, M. A. (2021). Applications of algebraic topology in predictive modeling for chronic disease. Mathematical Medicine and Biology: A Journal of the IMA, 38(3), 355-370. DOI: https://doi.org/10.1093/imammb/dqab010
Thivagar, M. L., & Richard, C. (2013). On Nano Forms of Weakly Open Sets. Interna- tional Journal of Mathematics and Statistics Invention, 1(1), 31-37.
Thivagar, M. L., Richard, C., & Paul, N. R. (2012). Mathematical Innovations of a Modern Topology in Medical Events. International Journal of Information Science, 2(4), 33-36. DOI: https://doi.org/10.5923/j.ijis.20120204.01
Tziomalos, K., & Spanou, L. (2021). Chronic kidney disease and cardiovascular risk: A comprehensive review. Journal of Clinical Medicine, 10(7), 1517.
Verduijn, M., & van Diepen, M. (2019). Biomarkers for early detection and prognosis of chronic kidney disease: Recent developments. Clinical Kidney Journal, 12(5), 654-661.
Wang, H., & Chen, S. (2022). Advancements in renal replacement therapy and patient outcomes in chronic kidney disease. Nephrology Dialysis Transplantation, 37(8), 1364- 1371.
Yamamoto, K., & Tanaka, T. (2022). Topology-based data analysis for chronic kidney disease progression. Biometrics, 78(1), 142-153.
Zhao, Y., & Wang, L. (2022). Mathematical modeling of disease dynamics in chronic health conditions. Mathematical Medicine and Biology, 39(1), 95-112
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