Izvorni znanstveni članak
https://doi.org/10.3325/cmj.2024.65.122
Innovative statistical approaches: the use of neural networks reduces the sample size in the splenectomy-MCAO mouse model
Dominik Romić
; Department of Neurosurgery, Dubrava University Hospital, Zagreb, Croatia
Monika Berecki
; Croatian Institute for Brain Research, University of Zagreb School of Medicine, Zagreb, Croatia
Sanja Srakočić
; Croatian Institute for Brain Research, University of Zagreb School of Medicine, Zagreb, Croatia
Paula Josić Dominović
; Croatian Institute for Brain Research, University of Zagreb School of Medicine, Zagreb, Croatia
Helena Justić
; Croatian Institute for Brain Research, University of Zagreb School of Medicine, Zagreb, Croatia
Dominik Hamer
; Croatian Institute for Brain Research, University of Zagreb School of Medicine, Zagreb, Croatia
Daniela Petrinec
; Croatian Institute for Brain Research, University of Zagreb School of Medicine, Zagreb, Croatia
Marina Radmilović
; Croatian Institute for Brain Research, University of Zagreb School of Medicine, Zagreb, Croatia
Branimir Hackenberger
; Department of Biology, “Josip Juraj Strossmayer” University, Osijek, Croatia
Srećko Gajović
; Croatian Institute for Brain Research, University of Zagreb School of Medicine, Zagreb, Croatia
Anton Glasnović
; Croatian Institute for Brain Research, University of Zagreb School of Medicine, Zagreb, Croatia
*
* Dopisni autor.
Sažetak
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 postMCAO (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.
Ključne riječi
Hrčak ID:
331943
URI
Datum izdavanja:
30.4.2024.
Posjeta: 173 *