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Introduction: Intracranial hemorrhages present across a spectrum of clinical phenotypes, with many patients transferred across hospitals to access higher levels of neurocritical care. We sought to characterize patient dispositions following intracranial hemorrhage and examine disparities associated with interhospital transfers. Methods: Using the Healthcare Cost and Utilization Project database, we mapped and identified factors influencing the likelihood of patient transfers and receipt of specialist interventional procedures following intracranial hemorrhage. Results: Of 11,660 patients with intracranial hemorrhage, 59.4% had non-traumatic and 87.5% single compartment bleeds. After presentation, about a quarter of patients were transferred to another facility either directly from the ED (23.0%) or after inpatient admission (1.8%). On unadjusted analysis, patients who were white, in the upper income quartiles, with private insurance, or resided in suburban areas were more frequently transferred. After adjusting for patient and hospital-level variables, younger and non-white patients had higher odds of transfer. Hospital capabilities, residence location, insurance status, and prior therapeutic relationship remained as transfer predictors. Transferred patients had a similar hospital length of stay compared to admitted patients, with 43.1% having no recorded surgical or specialist interventional procedure after transfer. Discussion: Our analysis reveals opportunities for improvement in risk stratification guiding transfers, as well as structural challenges likely impacting transfer decisions. [ABSTRACT FROM AUTHOR]

Rett syndrome is an X-linked neurodevelopmental disorder caused by mutation of Mecp2 gene and primarily affects females. Glial cell dysfunction has been implicated in in Rett syndrome (RTT) both in patients and in mouse models of this disorder and can affect synaptogenesis, glial metabolism and inflammation. Here we assessed whether treatment of adult (5–6 months old) symptomatic Mecp2- heterozygous female mice with N-acetyl cysteine conjugated to dendrimer (D-NAC), which is known to target glia and modulate inflammation and oxidative injury, results in improved behavioral phenotype, sleep and glial inflammatory profile. We show that unbiased global metabolomic analysis of the hippocampus and striatum in adult Mecp2-heterozygous mice demonstrates significant differences in lipid metabolism associated with neuroinflammation, providing the rationale for targeting glial inflammation in this model. Our results demonstrate that treatment with D-NAC (10 mg/kg NAC) once weekly is more efficacious than equivalently dosed free NAC in improving the gross neurobehavioral phenotype in symptomatic Mecp2-heterozygous female mice. We also show that D-NAC therapy is significantly better than saline in ameliorating several aspects of the abnormal phenotype including paw clench, mobility, fear memory, REM sleep and epileptiform activity burden. Systemic D-NAC significantly improves microglial proinflammatory cytokine production and is associated with improvements in several aspects of the phenotype including paw clench, mobility, fear memory, and REM sleep, and epileptiform activity burden in comparison to saline-treated Mecp2-hetereozygous mice. Systemic glial-targeted delivery of D-NAC after symptom onset in an older clinically relevant Rett syndrome model shows promise in improving neurobehavioral impairments along with sleep pattern and epileptiform activity burden. These findings argue for the translational value of this approach for treatment of patients with Rett Syndrome. [ABSTRACT FROM AUTHOR]

This article explores the impact of chromosome copy number thresholds on the molecular classification of meningioma, a type of brain tumor. The study analyzes data from 1,042 patients who underwent surgery for meningioma and examines the relationship between chromosome arm copy number variants (CNVs) and tumor behavior. The researchers find significant variability in the fraction of chromosomes lost or gained in meningiomas, with certain chromosomes showing higher rates of loss or gain. They also observe that altering CNV thresholds can affect the molecular classification of meningiomas and potentially impact predictions of tumor recurrence. The study highlights the importance of determining optimal CNV call thresholds to ensure accurate and reproducible molecular classification of meningiomas. [Extracted from the article]

Background: Surgical intervention is a critical tool to address adult spinal deformity (ASD). Given the evolution of spinal surgical techniques, we sought to characterize developments in ASD correction and barriers impacting clinical outcomes. Methods: We conducted a literature review utilizing PubMed, Embase, Web of Science, and Google Scholar to examine advances in ASD surgical correction and ongoing challenges from patient and clinician perspectives. ASD procedures were examined across pre-, intra-, and post-operative phases. Results: Several factors influence the effectiveness of ASD correction. Standardized radiographic parameters and three-dimensional modeling have been used to guide operative planning. Complex minimally invasive procedures, targeted corrections, and staged procedures can tailor surgical approaches while minimizing operative time. Further, improvements in osteotomy technique, intraoperative navigation, and enhanced hardware have increased patient safety. However, challenges remain. Variability in patient selection and deformity undercorrection have resulted in heterogenous clinical responses. Surgical complications, including blood loss, infection, hardware failure, proximal junction kyphosis/failure, and pseudarthroses, pose barriers. Although minimally invasive approaches are being utilized more often, clinical validation is needed. Conclusions: The growing prevalence of ASD requires surgical solutions that can lead to sustained symptom resolution. Leveraging computational and imaging advances will be necessary as we seek to provide comprehensive treatment plans for patients. [ABSTRACT FROM AUTHOR]

Trimethylation of lysine 27 on histone 3 (H3K27me3) loss has been implicated in worse prognoses for patients with meningiomas. However, there have been challenges in measuring H3K27me3 loss, quantifying its impact, and interpreting its clinical utility. We conducted a systematic review across Pubmed, Embase, and Web of Science to identify studies examining H3K27me3 loss in meningioma. Clinical, histopathological, and immunohistochemistry (IHC) characteristics were aggregated. A meta-analysis was performed using a random-effects model to assess prevalence of H3K27me3 loss and meningioma recurrence risk. Study bias was characterized using the NIH Quality Assessment Tool and funnel plots. Nine publications met inclusion criteria with a total of 2376 meningioma cases. The prevalence of H3K27me3 loss was 16% (95% CI 0.09–0.27), with higher grade tumors associated with a significantly greater proportion of loss. H3K27me3 loss was more common in patients who were male, had recurrent meningiomas, or required adjuvant radiation therapy. Patients were 1.70 times more likely to have tumor recurrence with H3K27me3 loss (95% CI 1.35–2.15). The prevalence of H3K27me3 loss in WHO grade 2 and 3 meningiomas was found to be significantly greater in tissue samples less than five years old versus tissue of all ages and when a broader definition of IHC staining loss was applied. This analysis demonstrates that H3K27me3 loss significantly associates with more aggressive meningiomas. While differences in IHC and tumor tissue age have led to heterogeneity in studying H3K27me3 loss, a robust prognostic signal is present. Our findings suggest an opportunity to improve study design and standardize tissue processing to optimize clinical viability of this epigenetic marker. [ABSTRACT FROM AUTHOR]

Simple Summary: Diagnosis of posterior fossa tumors is challenging yet proper classification is imperative given that treatment decisions diverge based on tumor type. The aim of this systematic review is to summarize the current state of machine learning methods developed as diagnostic tools for these pediatric brain tumors. We found that, while individual algorithms were quite efficacious, the field is limited by its heterogeneity in methods, outcome reporting, and study populations. We identify common limitations in the study and development of these algorithms and make recommendations as to how they can be overcome. If incorporated into algorithm design, the practical guidelines outlined in this review could help to bridge the gap between theoretical algorithm diagnostic testing and practical clinical application for a wide variety of pathologies. Background: Posterior fossa tumors (PFTs) are a morbid group of central nervous system tumors that most often present in childhood. While early diagnosis is critical to drive appropriate treatment, definitive diagnosis is currently only achievable through invasive tissue collection and histopathological analyses. Machine learning has been investigated as an alternative means of diagnosis. In this systematic review and meta-analysis, we evaluated the primary literature to identify all machine learning algorithms developed to classify and diagnose pediatric PFTs using imaging or molecular data. Methods: Of the 433 primary papers identified in PubMed, EMBASE, and Web of Science, 25 ultimately met the inclusion criteria. The included papers were extracted for algorithm architecture, study parameters, performance, strengths, and limitations. Results: The algorithms exhibited variable performance based on sample size, classifier(s) used, and individual tumor types being investigated. Ependymoma, medulloblastoma, and pilocytic astrocytoma were the most studied tumors with algorithm accuracies ranging from 37.5% to 94.5%. A minority of studies compared the developed algorithm to a trained neuroradiologist, with three imaging-based algorithms yielding superior performance. Common algorithm and study limitations included small sample sizes, uneven representation of individual tumor types, inconsistent performance reporting, and a lack of application in the clinical environment. Conclusions: Artificial intelligence has the potential to improve the speed and accuracy of diagnosis in this field if the right algorithm is applied to the right scenario. Work is needed to standardize outcome reporting and facilitate additional trials to allow for clinical uptake. [ABSTRACT FROM AUTHOR]