Quantitative analysis evaluation of globus pallidus:thalamus and dentate nucleus:pons of the pediatric brain demonstrated an increase after serial administrations of macrocyclic GBCA. Further research is necessary to fully understand GBCA pharmacokinetic in children.

BackgroundGanglioglioma (GG) and pilocytic astrocytoma (PA) represent the most frequent low-grade gliomas (LGG) occurring in paediatric age. LGGs not amenable of complete resection (CR) represent a challenging subgroup where traditional treatments often fail. Activation of the MAP Kinase (MAPK) pathway caused by the BRAFV600E mutation or the KIAA1549-BRAF fusion has been reported in pediatric GG and PA, respectively.Case presentationWe report on a case of BRAFV600E mutated cervicomedullary GG treated with standard chemotherapy and surgery. After multiple relapse, BRAF status was analyzed by immunohistochemistry and sequencing showing a BRAFV600E mutation. Treatment with Vemurafenib as single agent was started. For the first time, a radiological and clinical response was obtained after 3 months of treatment and sustained after 6 months.ConclusionOur experience underline the importance of understanding the driver molecular alterations of LGG and suggests a role for Vemurafenib in the treatment of pediatric GG not amenable of complete surgical resection.

More than a year has passed since the report of the first case of coronavirus disease 2019 (COVID), and increasing deaths continue to occur. Minimizing the time required for resource allocation and clinical decision making, such as triage, choice of ventilation modes and admission to the intensive care unit is important. Machine learning techniques are acquiring an increasingly sought-after role in predicting the outcome of COVID patients. Particularly, the use of baseline machine learning techniques is rapidly developing in COVID mortality prediction, since a mortality prediction model could rapidly and effectively help clinical decision-making for COVID patients at imminent risk of death. Recent studies reviewed predictive models for SARS-CoV-2 diagnosis, severity, length of hospital stay, intensive care unit admission or mechanical ventilation modes outcomes; however, systematic reviews focused on prediction of COVID mortality outcome with machine learning methods are lacking in the literature. The present review looked into the studies that implemented machine learning, including deep learning, methods in COVID mortality prediction thus trying to present the existing published literature and to provide possible explanations of the best results that the studies obtained. The study also discussed challenging aspects of current studies, providing suggestions for future developments.