A Study of Immunoenzymatic Parameters in Pediatric Ischemic Stroke as a Contribution to More Efficient Pediatric Monitoring and Diagnosis
2025
Mariana Sprincean | Ludmila Sidorenko | Serghei Sprincean | Svetlana Hadjiu | Niels Wessel
<b>Introduction:</b> Pediatric ischemic stroke (IS) is a rare but severe neurological emergency, with an incidence of 2–13 per 100,000. Most cases occur in the prenatal period or early infancy. Integrating artificial intelligence (AI) into clinical practice may enhance the early recognition of stroke. This pilot <b>study aimed</b> to identify immunoenzymatic markers as early predictors of pediatric IS, supporting machine learning applications. <b>Materials and Methods:</b> A prospective study (2017–2019) in Moldova included 53 children with IS and 53 healthy controls. The serum levels of vascular endothelial growth factor (VEGF), ciliary neurotrophic factor (CNTF), the S100B protein, CD105 (endoglin), antiphospholipid antibodies (APAs), and interleukin-6 (IL-6) were measured using ELISA during the acute phase. <b>Results:</b> Endoglin levels were significantly lower in IS patients (2.06 ± 0.012 ng/mL) vs. controls (2.51 ± 0.071 ng/mL) (<i>p</i> < 0.001). S100B levels were elevated (0.524 ± 0.0850 ng/mL vs. 0.120 ± 0.0038 ng/mL, <i>p</i> < 0.01). VEGF levels were significantly increased (613.41 ± 39.299 pg/mL vs. 185.50 ± 12.039 pg/mL, <i>p</i> < 0.001), correlating with the infarct size and disease severity. CNTF levels were also higher (7.84 ± 0.322 pg/mL vs. 5.29 ± 0.067 pg/mL, <i>p</i> < 0.001). APA levels were elevated (1.37 ± 0.046 U/mL vs. 0.92 ± 0.021 U/mL, <i>p</i> < 0.001). IL-6 levels were 10 times higher in IS patients (22.02 ± 2.143 pg/mL vs. 2.38 ± 0.302 pg/mL, <i>p</i> < 0.001), correlating with the infarct size (<i>p</i> < 0.004) and neurological prognosis at six months (<i>p</i> < 0.01). <b>Conclusions:</b> IL-6, VEGF, CNTF, S100B, CD105, and APAs are key markers in pediatric IS, reflecting neuroinflammation, vascular disruption, and the long-term prognosis. Their integration into AI-driven diagnostic models may improve early stroke detection and pediatric monitoring.
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