OBJECTIVE: Cranioplasty is a reconstructive procedure used to restore skull anatomy and repair skull defects. Optimal skull reconstruction is a challenge for neurosurgeons, and the strategy used to achieve the best result remains a topic of debate, especially in pediatric patients for whom the continuing skull growth makes the choice of material more difficult. When the native bone flap, which is universally accepted as the preferred option in pediatric patients, is unavailable, the authors' choice of prosthetic material is a polymethylmethacrylate (PMMA) implant designed using a custom-made technique. In this paper the authors present the results of their clinical series of 12 custom-made PMMA implants in pediatric patients. METHODS: A retrospective study of the patients who had undergone cranioplasty at Gaslini Children's Hospital between 2006 and 2013 was conducted. A total of 12 consecutive cranioplasties in 12 patients was reviewed, in which a patient-specific PMMA implant was manufactured using a virtual 3D model and then transformed into a physical model using selective laser sintering or 3D printing. All patients or parents were administered a questionnaire to assess how the patient/parent judged the aesthetic result. RESULTS: Patient age at craniectomy ranged from 5 months to 12.5 years, with a mean age of 84.33 months at cranioplasty. The mean extension of the custom-made plastic was 56.83 cm². The mean time between craniectomy and cranioplasty was 9.25 months. The mean follow-up duration was 55.7 months. No major complications were recorded; 3 patients experienced minor/moderate complications (prosthesis dislocation, granuloma formation, and fluid collection). CONCLUSIONS: In this patient series, PMMA resulted in an extremely low complication rate and the custom-made technique was associated with an excellent grade of patient or parent satisfaction on long-term follow up.

Background: Chloride intracellular channel-1 (CLIC1) activity controls glioblastoma proliferation. Metformin exerts antitumor effects in glioblastoma stem cells (GSCs) inhibiting CLIC1 activity, but its low potency hampers its translation in clinical settings. Methods: We synthesized a small library of novel biguanide-based compounds that were tested as antiproliferative agents for GSCs derived from human glioblastomas, in vitro using 2D and 3D cultures and in vivo in the zebrafish model. Compounds were compared to metformin for both potency and efficacy in the inhibition of GSC proliferation in vitro (MTT, Trypan blue exclusion assays, and EdU labeling) and in vivo (zebrafish model), migration (Boyden chamber assay), invasiveness (Matrigel invasion assay), self-renewal (spherogenesis assay), and CLIC1 activity (electrophysiology recordings), as well as for the absence of off-target toxicity (effects on normal stem cells and toxicity for zebrafish and chick embryos). Results: We identified Q48 and Q54 as two novel CLIC1 blockers, characterized by higher antiproliferative potency than metformin in vitro, in both GSC 2D cultures and 3D spheroids. Q48 and Q54 also impaired GSC self-renewal, migration and invasion, and displayed low systemic in vivo toxicity. Q54 reduced in vivo proliferation of GSCs xenotransplanted in zebrafish hindbrain. Target specificity was confirmed by recombinant CLIC1 binding experiments using microscale thermophoresis approach. Finally, we characterized GSCs from GBMs spontaneously expressing low CLIC1 protein, demonstrating their ability to grow in vivo and to retain stem-like phenotype and functional features in vitro. In these GSCs, Q48 and Q54 displayed reduced potency and efficacy as antiproliferative agents as compared to high CLIC1-expressing tumors. However, in 3D cultures, metformin and Q48 (but not Q54) inhibited proliferation, which was dependent on the inhibition dihydrofolate reductase activity. Conclusions: These data highlight that, while CLIC1 is dispensable for the development of a subset of glioblastomas, it acts as a booster of proliferation in the majority of these tumors and its functional expression is required for biguanide antitumor class-effects. In particular, the biguanide-based derivatives Q48 and Q54, represent the leads to develop novel compounds endowed with better pharmacological profiles than metformin, to act as CLIC1-blockers for the treatment of CLIC1-expressing glioblastomas, in a precision medicine approach.

Moyamoya vasculopathy is a rare chronic cerebrovascular disorder characterized by the stenosis of the terminal branches of the internal carotid arteries and the proximal tracts of anterior and middle cerebral arteries. Although surgical revascularization does not significantly change the underlying pathogenic mechanisms, it plays a pivotal role in the management of affected individuals, allowing to decrease the risk of ischemic and hemorrhagic complications. Surgical approaches may be direct (extracranial-intracranial bypass), indirect, or a combination of the two. Several indirect techniques classifiable according to the tissue (muscle, periosteum, galea, dura mater, and extracranial tissues) or vessel (artery) used as a source of blood supply are currently available. In this study, we reviewed the pertinent literature and analyzed the advantages, disadvantages, and pitfalls of the most relevant indirect revascularization techniques. We discussed the technical aspects and the therapeutical implications of each procedure, providing a current state-of-the-art overview on the limits and pitfalls of indirect revascularization in the treatment of moyamoya vasculopathy.

Objective: Clinical and surgical decisions for glioblastoma patients depend on a tumor imaging-based evaluation. Artificial Intelligence (AI) can be applied to magnetic resonance imaging (MRI) assessment to support clinical practice, surgery planning and prognostic predictions. In a real-world context, the current obstacles for AI are low-quality imaging and postoperative reliability. The aim of this study is to train an automatic algorithm for glioblastoma segmentation on a clinical MRI dataset and to obtain reliable results both pre- and post-operatively. Methods: The dataset used for this study comprises 237 (71 preoperative and 166 postoperative) MRIs from 71 patients affected by a histologically confirmed Grade IV Glioma. The implemented U-Net architecture was trained by transfer learning to perform the segmentation task on postoperative MRIs. The training was carried out first on BraTS2021 dataset for preoperative segmentation. Performance is evaluated using DICE score (DS) and Hausdorff 95% (H95). Results: In preoperative scenario, overall DS is 91.09 (± 0.60) and H95 is 8.35 (± 1.12), considering tumor core, enhancing tumor and whole tumor (ET and edema). In postoperative context, overall DS is 72.31 (± 2.88) and H95 is 23.43 (± 7.24), considering resection cavity (RC), gross tumor volume (GTV) and whole tumor (WT). Remarkably, the RC segmentation obtained a mean DS of 63.52 (± 8.90) in postoperative MRIs. Conclusions: The performances achieved by the algorithm are consistent with previous literature for both pre-operative and post-operative glioblastoma’s MRI evaluation. Through the proposed algorithm, it is possible to reduce the impact of low-quality images and missing sequences.