BACKGROUND: An understanding of global, regional, and national macroeconomic losses caused by stroke is important for allocation of clinical and research resources. The authors investigated the macroeconomic consequences of stroke disease burden in the year 2019 in 173 countries. METHODS: Disability-adjusted life year data for overall stroke and its subtypes (ischemic stroke, intracerebral hemorrhage, and subarachnoid hemorrhage) were collected from the GBD study (Global Burden of Disease) 2019 database. Gross domestic product (GDP, adjusted for purchasing power parity [PPP]) data were collected from the World Bank; GDP and disability-adjusted life year data were combined to estimate macroeconomic losses using a value of lost welfare (VLW) approach. All results are presented in 2017 international US dollars adjusted for PPP. RESULTS: Globally, in 2019, VLW due to stroke was $2059.67 billion or 1.66% of the global GDP. Global VLW/GDP for stroke subtypes was 0.78% (VLW=$964.51 billion) for ischemic stroke, 0.71% (VLW=$882.81 billion) for intracerebral hemorrhage, and 0.17% (VLW=$212.36 billion) for subarachnoid hemorrhage. The Central European, Eastern European, and Central Asian GBD super-region reported the highest VLW/GDP for stroke overall (3.01%), ischemic stroke (1.86%), and for subarachnoid hemorrhage (0.26%). The Southeast Asian, East Asian, and Oceanian GBD super-region reported the highest VLW/GDP for intracerebral hemorrhage (1.48%). CONCLUSIONS: The global macroeconomic consequences related to stroke are vast even when considering stroke subtypes. The present quantification may be leveraged to help justify increased spending of finite resources on stroke in an effort to improve outcomes for patients with stroke globally.

Glioblastoma is the most common and lethal central nervous system malignancy with a median survival after progression of only 6–9 months. Major biochemical mechanisms implicated in glioblastoma recurrence include aberrant molecular pathways, a recurrence-inducing tumor microenvironment, and epigenetic modifications. Contemporary standard-of-care (surgery, radiation, chemotherapy, and tumor treating fields) helps to control the primary tumor but rarely prevents relapse. Cytoreductive treatment such as surgery has shown benefits in recurrent glioblastoma; however, its use remains controversial. Several innovative treatments are emerging for recurrent glioblastoma, including checkpoint inhibitors, chimeric antigen receptor T cell therapy, oncolytic virotherapy, nanoparticle delivery, laser interstitial thermal therapy, and photodynamic therapy. This review seeks to provide readers with an overview of (1) recent discoveries in the molecular basis of recurrence; (2) the role of surgery in treating recurrence; and (3) novel treatment paradigms emerging for recurrent glioblastoma.

Oncolytic virotherapy is a rapidly progressing field that uses oncolytic viruses (OVs) to selectively infect malignant cells and cause an antitumor response through direct oncolysis and stimulation of the immune system. Despite demonstrated pre-clinical efficacy of OVs in many cancer types and some favorable clinical results in glioblastoma (GBM) trials, durable increases in overall survival have remained elusive. Recent evidence has emerged that tumor-associated macrophage/microglia (TAM) involvement is likely an important factor contributing to OV treatment failure. It is prudent to note that the relationship between TAMs and OV therapy failures is complex. Canonically activated TAMs (i.e., M1) drive an antitumor response while also inhibiting OV replication and spread. Meanwhile, M2 activated TAMs facilitate an immunosuppressive microenvironment thereby indirectly promoting tumor growth. In this focused review, we discuss the complicated interplay between TAMs and OV therapies in GBM. We review past studies that aimed to maximize effectiveness through immune system modulation—both immunostimulatory and immunosuppressant—and suggest future directions to maximize OV efficacy.

The lack of reliable predictive biomarkers to guide effective therapy is a major obstacle to the advancement of therapy for high-grade gliomas, particularly glioblastoma (GBM), one of the few cancers whose prognosis has not improved over the past several decades. With this pilot clinical trial (number NCT04135807), we provide first-in-human evidence that drug-releasing intratumoral microdevices (IMDs) can be safely and effectively used to obtain patient-specific, high-throughput molecular and histopathological drug response profiling. These data can complement other strategies to inform the selection of drugs based on their observed antitumor effect in situ. IMDs are integrated into surgical practice during tumor resection and remain in situ only for the duration of the otherwise standard operation (2 to 3 hours). None of the six enrolled patients experienced adverse events related to the IMD, and the exposed tissue was usable for downstream analysis for 11 out of 12 retrieved specimens. Analysis of the specimens provided preliminary evidence of the robustness of the readout, compatibility with a wide array of techniques for molecular tissue interrogation, and promising similarities with the available observed clinical-radiological responses to temozolomide. From an investigational aspect, the amount of information obtained with IMDs allows characterization of tissue effects of any drugs of interest, within the physiological context of the intact tumor, and without affecting the standard surgical workflow.

Magnetic resonance–guided focused ultrasound (MRgFUS) thalamotomy is an effective treatment for medically refractory essential tremor. We investigate ablation sites and potential tracts associated with optimal tremor control and side effects based on the analysis of 351 cases from three international hospitals. Lesions were segmented on day 1 thin-cut T2 axial images, mapped to standard Montreal Neurological Institute space, and used to derive probabilistic maps and tracts associated with tremor improvement and side effects. Lesioning of a specific subregion within the ventral intermediate nucleus and the cerebellothalamic tract was associated with optimal tremor improvements. Some lesion locations and tracts were associated with differential side effects. Overlaps with the optimal tremor improvement sites accounted for variance in clinical improvements in out-of-sample cases. Efficacy of this location was further confirmed by test-retest cases that underwent two MRgFUS procedures. We identify and validate a target area for optimal tremor control and sites of avoidance associated with side effects.

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.