Low-grade gliomas are the most common brain tumors in children. This tumor type presents a wide range of clinical, histological, and biological behaviors. In recent years, an association between estrogens and progesterone and the development of tumors has been suggested. A case of a 2-year-old girl is described with a low-grade brain tumor treated with chemotherapy and disease stabilization. The treatment with Decapeptyl® was initiated due to precocious puberty, and the tumor showed a decrease in its solid component—more than 50% of the initial size—three years after starting treatment. Several studies have described the influence of estrogen and progesterone on the development of gliomas, decreasing or increasing their expression in those tumors with greater aggressiveness, respectively. Despite the fact that the tumor-hormonal expression relationship in other tumor types has been evaluated, its role in the treatment of brain tumors remains unknown.
The aim of this paper was to evaluate the usefulness of C-Reactive Protein (CRP), Procalcitonin (PCT) and Interleukine 6 (IL6) biomarkers in predicting the existence of Gram negative bloodstream infections (Gr-BSI) or the development of Systemic Inflammatory Response Syndrome (SIRS) during the first 24 hours of fever in pediatric cancer patients. The present study included a total of 103 consecutive fever episodes in 44 hemato-oncological pediatric patients, from whom samples for biomarkers CRP, PCT and IL6 were taken upon initial evaluation and then between 12 and 24 hours after.An IL6 value at the first evaluation (IL6-1) higher than 164 pg/ml and an increase in CRP higher than 291% between the first and subsequent samples (CRP-2vs1) showed a statistically significant OR of 26.03 and 19.62, respectively, in multivariate analysis.Conclusion: IL6-1 and CRP-2vs1 showed a strong, independent correlation with Gr-BSI and SIRS episodes and, therefore, could be used as reliable predictors of these kinds of severe episodes. The approach taken in our study, using biomarker variations over time as a variable, has shown itself to be an improvement in the predictive model.
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