prikaz prve stranice dokumenta Innovative statistical approaches: the use of neural networks reduces the sample size in the splenectomy-MCAO mouse model
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Scientific paper - Original scientific paper
Innovative statistical approaches: the use of neural networks reduces the sample size in the splenectomy-MCAO mouse model
Croatian Medical Journal, 65 (2024), 2; 122-137. https://doi.org/10.3325/cmj.2024.65.122
Romić, Dominik; Berecki, Monika; Srakočić, Sanja; Josić Dominović, Paula; Justić, Helena; Hamer, Dominik; Petrinec, Daniela; Radmilović, Marina; Hackenberger, Branimir; Gajović, Srećko; Glasnović, Anton

Institutional repository: Dr Med - University of Zagreb School of Medicine Digital Repository

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Romić, D., Berecki, M., Srakočić, S., Josić Dominović, P., Justić, H., Hamer, D. ... Glasnović, A. (2024). Innovative statistical approaches: the use of neural networks reduces the sample size in the splenectomy-MCAO mouse model. Croatian Medical Journal, 65. (2), 122-137. doi: 10.3325/cmj.2024.65.122

Romić, Dominik, et al. "Innovative statistical approaches: the use of neural networks reduces the sample size in the splenectomy-MCAO mouse model." Croatian Medical Journal, vol. 65, no. 2, 2024, pp. 122-137. https://doi.org/10.3325/cmj.2024.65.122

Romić, Dominik, Monika Berecki, Sanja Srakočić, Paula Josić Dominović, Helena Justić, Dominik Hamer, Daniela Petrinec, et al. "Innovative statistical approaches: the use of neural networks reduces the sample size in the splenectomy-MCAO mouse model." Croatian Medical Journal 65, no. 2 (2024): 122-137. https://doi.org/10.3325/cmj.2024.65.122

Romić, D., et al. (2024) 'Innovative statistical approaches: the use of neural networks reduces the sample size in the splenectomy-MCAO mouse model', Croatian Medical Journal, 65(2), pp. 122-137. doi: 10.3325/cmj.2024.65.122

Romić D, Berecki M, Srakočić S, Josić Dominović P, Justić H, Hamer D, and sur.. Innovative statistical approaches: the use of neural networks reduces the sample size in the splenectomy-MCAO mouse model. Croatian Medical Journal [Internet]. 2024 April 30 [cited 2024 December 29];65(2):122-137. doi: 10.3325/cmj.2024.65.122

D. Romić, et al., "Innovative statistical approaches: the use of neural networks reduces the sample size in the splenectomy-MCAO mouse model", Croatian Medical Journal, vol. 65, no. 2, pp. 122-137, April 2024. [Online]. Available at: https://urn.nsk.hr/urn:nbn:hr:105:242122. [Accessed: 29 December 2024]

Metadata
Title (english)Innovative statistical approaches: the use of neural networks reduces the sample size in the splenectomy-MCAO mouse model
AuthorDominik Romić
AuthorMonika Berecki
AuthorSanja Srakočić
AuthorPaula Josić Dominović
AuthorHelena Justić
AuthorDominik Hamer
AuthorDaniela Petrinec
AuthorMarina Radmilović
AuthorBranimir Hackenberger
AuthorSrećko Gajović
AuthorAnton Glasnović
Author's institutionUniversity of Zagreb
School of Medicine
(Croatian Institiute for Brain Research)
Author's institutionUniversity of Zagreb
School of Medicine
(Department of Histology and Embryology)
Scientific / art field,
discipline and subdiscipline		BIOMEDICINE AND HEALTHCARE
Basic Medical Sciences
Cytology, Histology and Embryology
Scientific / art field,
discipline and subdiscipline		BIOMEDICINE AND HEALTHCARE
Basic Medical Sciences
Neuroscience
Abstract (english)
Aim: To compare the effectiveness of artificial neural network (ANN) and traditional statistical analysis on identical data sets within the splenectomy-middle carotid artery occlusion (MCAO) mouse model. Methods: Mice were divided into the splenectomized (SPLX) and sham-operated (SPLX-sham) group. A splenectomy was conducted 14 days before middle carotid artery occlusion (MCAO). Magnetic resonance imaging (MRI), bioluminescent imaging, neurological scoring (NS), and histological analysis, were conducted at two, four, seven, and 28 days after MCAO. Frequentist statistical analyses and ANN analysis employing a multi-layer perceptron architecture were performed to assess the probability of discriminating between SPLX and SPLX-sham mice. Results: Repeated measures ANOVA showed no significant differences in body weight (F (5, 45)=0.696, P=0.629), NS (F (2.024, 18.218)=1.032, P=0.377) and brain infarct size on MRI between the SPLX and SPLX-sham groups post-MCAO (F (2, 24)=0.267, P=0.768). ANN analysis was employed to predict SPLX and SPL-sham classes. The highest accuracy in predicting SPLX class was observed when the model was trained on a data set containing all variables (0.7736±0.0234). For SPL-sham class, the highest accuracy was achieved when it was trained on a data set excluding the variable combination MR contralateral/animal mass/NS (0.9284±0.0366). Conclusion: This study validated the neuroprotective impact of splenectomy in an MCAO model using ANN for data analysis with a reduced animal sample size, demonstrating the potential for leveraging advanced statistical methods to minimize sample sizes in experimental biomedical research.
Keywords (english)
Languageenglish
Publication typeScientific paper - Original scientific paper
Publication statusPublished
Peer reviewPeer review
Publication versionPublished version
Journal titleCroatian Medical Journal
Numberingvol. 65, no. 2, pp. 122-137
p-ISSN0353-9504
e-ISSN1332-8166
DOIhttps://doi.org/10.3325/cmj.2024.65.122
URN:NBNurn:nbn:hr:105:242122
Publication2024-04-30
Document URLhttp://www.cmj.hr/2024/65/2/38706238.htm
Type of resourceText
Access conditionsOpen access
Terms of useLicence Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) icon
Created on2024-06-20 11:04:53