UDC

[612.11+611.018.5]:576

DOI

10.37482/2687-1491-Z263

Authors

Tatjana A. Ishunina* ORCID: https://orcid.org/0000-0002-2743-7515
Yuliya D. Lutsenko* ORCID: https://orcid.org/0009-0006-2881-6839
Alina V. Oleynikova* ORCID: https://orcid.org/0009-0008-2861-1272
Sergey Yu. Mironov* ORCID: https://orcid.org/0000-0001-9889-9277
Andrey V. Prusachenko* ORCID: https://orcid.org/0000-0002-0426-691X
*Kursk State Medical University (Kursk, Russia)

Corresponding author: Tatjana Ishunina, address: ul. Karla Marksa 3, Kursk, 305041, Russia; e-mail: ishuninat@yandex.ru

Abstract

Predicting academic success is one of the fundamental tasks of learning analytics. Generally accepted predictive criteria include academic performance. However, there is limited information about the influence of morphofunctional factors indicating the state of health in students on their academic performance. Therefore, the purpose of this study was to investigate potential predictors of learning success based on blood cell data. Materials and methods. The experiment involved 79 apparently healthy second-year students of Kursk State Medical University (mean age 18.97 ± 0.14 years), 67 % female and 33 % male. An anonymous survey was conducted using Google Forms. The following information was obtained: rating points for the first semester of studying the discipline “Histology, Embryology, Cytology”, time spent preparing for classes, and some complete blood count data (concentrations of red blood cells, white blood cells, platelets and haemoglobin; haematocrit; neutrophil and lymphocyte fractions). Depending on the points scored, the students were divided into three groups: satisfactory performance (64–79 points, n = 25), good performance (80–90 points, n = 41) and excellent performance (91– 100 points, n = 13). Results. A significant positive correlation between platelet count and rating points scored was found (r = 0.411, p = 0.00017). At the same time, platelet concentration differed significantly between the three groups of students (p = 0.002) and was higher in the group with excellent performance compared to students who demonstrated good (p = 0.007) or satisfactory (p = 0.00045) performance. One of the reasons behind the increase in platelet concentration in students with currently high academic performance may be the neuropsychic stress they experience due to a longer and more thorough preparation for classes. Thus, platelet count can be used as a predictor of students’ academic success.

Keywords

blood cells, platelets, respiratory burst of neutrophils, predictors of academic success, academic performance, morphofunctional parameters of health, university students

References

1. Ozerova G.P., Pavlenko G.F. Prediction of Student Performance in Blended Learning Utilizing Learning Analytics Data. Sci. Educ. Today, 2019, vol. 9, no. 6, pp. 73–87 (in Russ.). https://doi.org/10.15293/2658-6762.1906.05
2. Stepanova I.P., Ganzina I.V., Atavina O.V., Postnova T.V., Mugak V.V. Prognozirovanie uspeshnosti obucheniya po khimicheskim distsiplinam po rezul’tatam EGE v meditsinskom vuze [Prognosis of Training Success in Chemical Disciplines According to Results of the Unified State Exam (USE) in Medical University]. Mezhdunarodnyy zhurnal eksperimental’nogo obrazovaniya, 2018, no. 11, pp. 17–22.
3. Noskov M.V., Vaynshteyn Yu.V., Somova M.V., Fedotova I.M. Prognostic Model for Assessing the Success of Subject Learning in Conditions of Digitalization of Education. RUDN J. Informatization Educ., 2023, vol. 20, no. 1, pp. 7–19 (in Russ.). https://doi.org/10.22363/2312-8631-2023-20-1-7-19
4. Kharitonov I.M., Krushel’ E.G., Privalov O.O., Stepanchenko I.V., Stepanchenko O.V. Prognozirovanie kachestva obucheniya v vuze s pomoshch’yu metodov regressionnogo analiza [Higher School Education Quality Forecasting by Regression Analysis Methods]. Izvestiya Sankt-Peterburgskogo gosudarstvennogo tekhnologicheskogo instituta (tekhnicheskogo universiteta), 2021, no. 56, pp. 72–80. https://doi.org/10.36807/1998-9849-2020-56-82-72-80
5. Gafarov F.M., Rudneva Ya.B., Sharifov U.Yu. Predictive Modeling in Higher Education: Determining Factors of Academic Performance. Vysshee obrazovanie v Rossii, 2023, vol. 32, no. 1, pp. 51–70 (in Russ.). https://doi.org/10.31992/0869-3617-2023-32-1-51-70
6. Yakunin Yu.Yu., Shestakov V.N., Liksonova D.I., Danichev A.A. Predicting Student Performance Using Machine Learning Tools. Inform. Educ., 2023, vol. 38, no. 4, pp. 28–43 (in Russ.). https://doi.org/10.32517/0234-0453-2023-38-4-28-43
7. Mukhina N.V. Effects of Physiological and Psychological Parameters of Female Students on Their Academic Performance. J. Med. Biol. Res., 2024, vol. 12, no. 3, pp. 338–346. https://doi.org/10.37482/2687-1491-Z206
8. Djordjević V.V., Kostić J., Krivokapić Ž., Krtinić D., Ranković M., Petković M., Ćosić V. Decreased Activity of Erythrocyte Catalase and Glutathione Peroxidase in Patients with Schizophrenia. Medicina (Kaunas), 2022, vol. 58, no. 10. Art. no. 1491. https://doi.org/10.3390/medicina58101491
9. Savushkina O.K., Boksha I.S., Prokhorova T.A., Tereshkina E.B., Burminskiy D.S., Morozova M.A., Vorob’eva E.A., Burbaeva G.Sh. Aktivnost’ eritrotsitarnykh i trombotsitarnykh glutationreduktazy i glutationS-transferazy pri paranoidnoy shizofrenii [The Activity of Erythrocyte and Platelet Glutathione Reductase and Glutathione-S-Transferase in Paranoid Schizophrenia]. Zhurnal nevrologii i psikhiatrii im. S.S. Korsakova, 2018, vol. 118, no. 11, pp. 77–81. https://doi.org/10.17116/jnevro201811811177
10. Cristalli D.O., Arnal N., Marra F.A., de Alaniz M.J.T., Marra C.A. Peripheral Markers in Neurodegenerative Patients and Their First-Degree Relatives. J. Neurol. Sci., 2012, vol. 314, no. 1–2, pp. 48–56. https://doi.org/10.1016/j.jns.2011.11.001
11. Fatkullina L.D., Molochkina E.M., Zorina O.M., Podchufarova D.E., Gavrilova S.I., Fedorova Ya.B., Klyushnik T.P., Burlakova E.B. Pokazateli struktury membrany i aktivnost’ atsetilkholinesterazy eritrotsitov u patsientov s sindromom myagkogo kognitivnogo snizheniya [Membrane Structure Markers and the Acetylcholinesterase Activity of Erythrocytes in Patients with Mild Cognitive Impairment]. Zhurnal nevrologii i psikhiatrii im. S.S. Korsakova, 2013, vol. 113, no. 6, pp. 62–67.
12. Bower J.E., Ganz P.A., Irwin M.R., Arevalo J.M.G., Cole S.W. Fatigue and Gene Expression in Human Leukocytes: Increased NF-κB and Decreased Glucocorticoid Signaling in Breast Cancer Survivors with Persistent Fatigue. Brain Behav. Immun., 2011, vol. 25, no. 1, pp. 147–150. https://doi.org/10.1016/j.bbi.2010.09.010
13. Burnouf T., Walker T.L. The Multifaceted Role of Platelets in Mediating Brain Function. Blood, 2022, vol. 140, no. 8, pp. 815–827. https://doi.org/10.1182/blood.2022015970
14. Sotnikov I., Veremeyko T., Starossom S.C., Barteneva N., Weiner H.L., Ponomarev E.D. Platelets Recognize Brain-Specific Glycolipid Structures, Respond to Neurovascular Damage and Promote Neuroinflammation. PLoS One, 2013, vol. 8, no. 3. Art. no. e58979. https://doi.org/10.1371/journal.pone.0058979
15. Dukhinova M., Kuznetsova I., Kopeikina E., Veniaminova E., Yung A.W.Y., Veremeyko T., Levchuk K., Barteneva N.S., Wing-Ho K.K., Yung W.-H., Liu J.Y.H., Rudd J., Yau S.S.Y., Anthony D.C., Strekalova T., Ponomarev E.D. Platelets Mediate Protective Neuroinflammation and Promote Neuronal Plasticity at the Site of Neuronal Injury. Brain Behav. Immun., 2018, vol. 74, pp. 7–27. https://doi.org/10.1016/j.bbi.2018.09.009
16. Heck N., Santos M.D. Dendritic Spines in Learning and Memory: From First Discoveries to Current Insights. Adv. Neurobiol., 2023, vol. 34, pp. 311–348. https://doi.org/10.1007/978-3-031-36159-3_7
17. Leiter O., Walker T.L. Platelets: The Missing Link Between the Blood and Brain? Prog. Neurobiol., 2019, vol. 183. Art. no. 101695. https://doi.org/10.1016/j.pneurobio.2019.101695
18. Park C., Hahn O., Gupta S., Moreno A.J., Marino F., Kedir B., Wang D., Villeda S.A., Wyss-Coray T., Dubal D.B. Platelet Factors Are Induced by Longevity Factor Klotho and Enhance Cognition in Young and Aging Mice. Nat. Aging, 2023, vol. 3, no. 9, pp. 1067–1078. https://doi.org/10.1038/s43587-023-00468-0
19. Schroer A.B., Ventura P.B., Sucharov J., Misra R., Chui M.K.K., Bieri G., Horowitz A.M., Smith L.K., Encabo K., Tenggara I., Couthouis J., Gross J.D., Chan J.M., Luke A., Villeda S.A. Platelet Factors Attenuate Inflammation and Rescue Cognition in Ageing. Nature, 2023, vol. 620, no. 7976, pp. 1071–1079. https://doi.org/10.1038/s41586-023-06436-3
20. Yu H., Liu Y., He B., He T., Chen C., He J., Yang X., Wang J.-Z. Platelet Biomarkers for a Descending Cognitive Function: A Proteomic Approach. Aging Cell, 2021, vol. 20, no. 5. Art. no. e13358. https://doi.org/10.1111/acel.13358
21. Koudouovoh-Tripp P., Sperner-Unterweger B. Influence of Mental Stress on Platelet Bioactivity. World J. Psychiatry, 2012, vol. 2, no. 6, pp. 134–147. https://doi.org/10.5498/wjp.v2.i6.134