Glioblastoma is the most aggressive primary brain malignancy in adults, and has a survival duration of approximately 15 months. First line treatment involves surgical resection, chemotherapy, and radiation, but despite the multi-pronged approach and advances in cancer research, glioblastoma remains devastating with a high mortality rate. Lipidomics is an emerging discipline that studies lipid pathways and characteristics, and is a promising field to understand biochemical mechanisms. In glioblastoma, disrupted lipid homeostasis has been reported in the literature. A thorough understanding of serum lipidomics may offer ways to better understand glioblastoma biomarkers, prognosis, and treatment options. Here, we review the literature, offering future directions for lipidomics research in glioblastomas.

Craniopharyngiomas are tumors that arise from the remnants of Rathke's pouch along the nasopharynx to the diencephalon. Current standard of care includes maximal surgical resection versus adjuvant ...

Children with mild traumatic brain injury (mTBI) and intracranial injury (ICI) often receive unnecessary imaging and hospital admission, leading to avoidable burdens on patients and health systems. While most of these patients do not develop critical neurological injuries, identifying those at risk would allow for a more optimal determination of the appropriate level of initial emergency care. The Brain Injury Guidelines (BIG) were developed as a triage tool to identify adult patients with mTBI and ICI who can benefit from repeat imaging, hospital admission, or neurosurgical consultation. Here, we sought to validate BIG in children at a Level I trauma center and determine if the BIG algorithm can accurately identify which patients with mTBI/ICI have critical neurosurgical injuries. We hypothesize that the BIG can identify critical neurological injuries more accurately than the Glasgow Coma Scale (GCS) alone and that more severe injury according to BIG is associated with worse patient outcome. We retrospectively reviewed TBI admissions at a single center (2017–2023) using an institutional registry. Patients included (0–17 years) had an initial head computerized tomography scan with ICI and a GCS of 14–15. Patients were retrospectively classified into the BIG categories (BIG 1, 2, or 3). Medical records were reviewed to identify clinically important TBI (ciTBI): death, neurological deterioration, neurosurgical intervention, intubation >24 h, or hospital admission >48 h due to TBI. Repeat imaging studies obtained were evaluated for progression of injury. The incidence of clinically important TBI (ciTBI) and imaging progression were recorded and compared across BIG categories. Outcomes were evaluated using the Glasgow Outcome Score Extended (GOS-E) 6 months after injury. Univariable and chi-square tests were used to analyze comparisons. Overall, 804 subjects were included in the analysis of which 551 (68.5%) were transfers. Overall, 175 (21.8%) patients had a BIG 1, 402 (50.0%) a BIG 2, and 227 (28.2%) a BIG 3 injury. CiTBI occurred among 64 (8.0%) patients overall, and in 1 (0.6%), 4 (1.0%), and 59 (26.0%) of the BIG 1, 2, and 3 injuries ( p < 0.0001). Progression on repeat imaging associated with neurological decline, neurosurgical intervention or resulting in additional evaluation was noted in 0 (0%), 2 (0.5%), and 41 (18.0%) of the BIG 1, 2, and 3 injuries ( p < 0.001). Amongst 471 patients (58.6%) with available 6-month patient outcomes, 98% had a GOS-E ≥5 and no outcome difference between BIG categories was observed. Risk stratification of mild TBI using BIG allowed for reasonable identification of children who subsequently develop ciTBI, suggesting that BIG classification can aid in triage and management of patients who might benefit from neurosurgical consultation, repeat imaging, and potentially transfer to a dedicated trauma center. More severe injury according to BIG was not associated with a worse patient outcome.

Cancers and neurological disorders are two major types of diseases in humans. We developed the concept called the “Aberrant Cell Cycle Disease (ACCD)” due to the accumulating evidence that shows that two different diseases share the common mechanism of aberrant cell cycle re-entry. The aberrant cell cycle re-entry is manifested as kinase/oncoprotein activation and tumor suppressor (TS) inactivation, which are associated with both tumor growth in cancers and neuronal death in neurological disorders. Therefore, some cancer therapies (e.g., kinase/oncogene inhibition and TS elevation) can be leveraged for neurological treatments. MicroRNA (miR/miRNA) provides a new style of drug-target binding. For example, a single tumor suppressor miRNA (TS-miR/miRNA) can bind to and decrease tens of target kinases/oncogenes, producing much more robust efficacy to block cell cycle re-entry than inhibiting a single kinase/oncogene. In this review, we summarize the miRNAs that are altered in both cancers and neurological disorders, with an emphasis on miRNA drugs that have entered into clinical trials for neurological treatment.

Background/Objectives: The recurrence of glioblastoma is an inevitable event in this disease’s course. In this study, we sought to identify the metabolomic signature in patients with recurrent glioblastomas undergoing surgery and radiation therapy. Methods: Blood samples collected prospectively from six patients with recurrent IDH-wildtype glioblastoma who underwent one surgery at diagnosis and a second surgery at relapse were analyzed using untargeted gas chromatography–time-of-flight mass spectrometry to measure metabolite abundance. The data analysis techniques included univariate analysis, correlation analysis, and a sample t-test. For predictive modeling, machine learning (ML) algorithms such as multinomial logistic regression, gradient boosting, and random forest were applied to predict the classification of samples in the correct treatment phase. Results: Comparing samples after the first surgery and after the relapse surgeries to the pre-operative samples showed a significant decrease in sorbitol and mannitol; there was a significant increase in urea, oxoproline, glucose, and alanine. After chemoradiation, two metabolites, erythritol and 6-deoxyglucitol, showed a decrease, with a cut-off of three and a significant reduction for 6-deoxyglucitol, while 2,4-difluorotoluene and 9-myristoleate showed an increase post radiation, with a fold-change cut-off of three. The gradient-boosting ML model achieved a high performance for the prediction of tumor conditions in patients with glioblastoma who had undergone relapse surgery. Conclusions: We developed an ML predictor for tumor phase based on the plasma metabolomic profile. Our study suggests the potential of combining metabolomics with ML as a new tool to stratify the risk of tumor progression in patients with glioblastoma.

Purpose: Pediatric cervical spine injury (PCSI) can result in devastating neurologic disability. While computed tomography (CT) imaging is both sensitive and specific in detecting clinically significant injuries, indiscriminate utilization can lead to excessive ionizing radiation exposure. A routine institutional audit revealed CTs were inappropriately obtained 54% of the time. This study evaluates the effects of an updated protocol to reduce radiation exposure in pediatric trauma patients. Methods: Data were retrospectively analyzed from a pediatric level 1 trauma center from 2021 to 2022. The data were divided into two cohorts, pre-implementation (2021) and post-implementation (2022). Inclusion criteria were patients 0–14 years old with a Glasgow Coma Scale (GCS) ranging 9–15. Outside-hospital transfers were excluded. The primary study endpoints were guideline compliance and CT utilization. Results: A total of 82 subjects were enrolled in this study. In 2021, there were 38 subjects (female/male 15/23, mean age 5.9 years old) with an average GCS of 13.6. In 2022, there were 44 subjects (female/male 19/25, mean age 5.2 years old) with an average GCS of 14.0. In 2021, the overall protocol adherence rate was 81.6%, and post-implementation in 2022, compliance was 93.2% ( p = 0.109). Following implementation, the rate of inappropriate (protocol non-adherent CT) use decreased from 58.6 to 6.8% ( p < 0.05). Conclusions: Implementation of a new evidence-based institutional protocol for PCSI was associated with improved adherence and reduction of unnecessary CT orders. Ongoing monitoring will help determine if these improvements are sustained.