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Juvenile xanthogranuloma (JXG) is a rare histiocytic disorder primarily affecting children, typically presenting as self-limiting skin lesions. However, extracutaneous JXG (extra-JXG) or systemic JXG (sJXG) can involve various organs, including the central nervous system (CNS), leading to high morbidity and mortality, especially in cases of CNS involvement. Treatment approaches for CNS–JXG, including surgical resection, chemotherapy, and radiation therapy, lack consensus, with reports of relapsed or refractory cases being rare. Here, we present the case of a 2-year-old girl with refractory extra-JXG involving the CNS, characterized by multiple intracranial lesions. Initial chemotherapy with prednisolone and vinblastine showed no response, and second-line therapy, including cytarabine and vincristine combined with surgical resection, was also ineffective. However, a 6-cycle third-line chemotherapy regimen incorporating vincristine and carboplatin led to a positive response, with stable disease status, and normal growth and neurodevelopment observed. Our findings suggest that combined chemotherapy with carboplatin and vincristine is a feasible and well-tolerated treatment strategy for patients with CNS–JXG.

Abstract Aiming toward a sustainable energy era, the design of efficient photocatalysts for water splitting by engineering their band properties has been actively studied. One promising avenue for the band engineering of active photocatalysts is the use of solid-solution alloying. However, the enormous possible configurations of multicomponent alloys hinders the experimental screening of this multidimensional material space, providing an opportunity for machine learning (ML) approaches to help accelerate the discovery of new multicomponent alloy materials. A conventional prerequisite for ML approaches is a large database of accurate material properties, which may require exhaustive computational and/or experimental resources. This study demonstrates that the screening of solid-solution alloys (up to hexanary systems) can be performed using a small database to minimize (and optimize) the number of high-level computational calculations. Specifically, we use ZnTe-based alloys as a prototypical example and employ a secure independent screening and sparsifing operator with the recently developed agreement method (α-method). Furthermore, we discuss and propose design routes to determine the optimal solid-solution ZnTe-based alloys for photoassisted water-splitting reactions.