Exploring the dynamics of adult Axin2 cell lineage integration into dentate gyrus granule neurons.

The Wnt pathway plays critical roles in neurogenesis. The expression of Axin2 is induced by Wnt/ß-catenin signaling, making this gene a reliable indicator of canonical Wnt activity. We employed pulse-chase genetic lineage tracing with the Axin2-CreERT2 allele to follow the fate of Axin2+ lineage in the adult hippocampal formation. We found Axin2 expressed in astrocytes, neurons and endothelial cells, as well as in the choroid plexus epithelia. Simultaneously with the induction of Axin2 fate mapping by tamoxifen, we marked the dividing cells with 5-ethynyl-2'-deoxyuridine (EdU). Tamoxifen induction led to a significant increase in labeled dentate gyrus granule cells three months later. However, none of these neurons showed any EdU signal. Conversely, six months after the pulse-chase labeling with tamoxifen/EdU, we identified granule neurons that were positive for both EdU and tdTomato lineage tracer in each animal. Our data indicates that Axin2 is expressed at multiple stages of adult granule neuron differentiation. Furthermore, these findings suggest that the integration process of adult-born neurons from specific cell lineages may require more time than previously thought.

Dynamics of Adult Axin2 Cell Lineage Integration in Granule Neurons of the Dentate Gyrus.

The Wnt pathway plays critical roles in neurogenesis. The expression of Axin2 is induced by Wnt/ß-catenin signaling, making this gene a sensitive indicator of canonical Wnt activity. We employed pulse-chase genetic lineage tracing with the Axin2-CreERT2 allele to follow the fate of Axin2 -positive cells in the adult hippocampal formation. We found Axin2 expressed in astrocytes, neurons and endothelial cells, as well as in the choroid plexus epithelia. Simultaneously with tamoxifen induction of Axin2 fate mapping, the dividing cells were marked with 5-ethynyl-2'-deoxyuridine (EdU). Tamoxifen induction resulted in significant increase of dentate gyrus granule cells three months later; however, none of these neurons contained EdU signal. Conversely, six months after the tamoxifen/EdU pulse-chase labeling, EdU-positive granule neurons were identified in each animal. Our data imply that Axin2 is expressed at several different stages of adult granule neuron differentiation and suggest that the process of integration of the adult-born neurons from certain cell lineages may take longer than previously thought.

Stereotactic and Functional Neurosurgery Convection-Enhanced Delivery of Autologous Cerebrospinal Fluid Enhances Basal Ganglia Visualization during MRI-Guided Deep Brain Stimulation Surgery.

INTRODUCTION: The aim of this study was to determine the safety and feasibility of convection-enhanced delivery of autologous cerebrospinal fluid (CSF) for enhancing intraoperative magnetic resonance imaging (MRI) of the basal ganglia during stereotactic neurosurgery. METHODS: This pilot study was conducted in 4 patients with Parkinson's disease (PD) who underwent MRI-guided deep brain stimulation of the globus pallidus internus (GPi). CSF was obtained via lumbar puncture after general anesthesia and prior to incision. A frameless stereotaxy system was installed, and an infusion catheter was inserted to the GPi using intraoperative MRI. Infusion of autologous CSF was performed at a convective rate of 5 µL/min with a maximum volume of infusion (Vi) of 500 mL. T2-weighted MRI scans were obtained every 15 min up to a maximum of 105 min in order to calculate the volume of distribution (Vd). Safety was assessed with adverse event monitoring, and clinical outcomes were measured with changes in unmedicated UPDRS part III and PDQ-39 scores from baseline to 6 months postoperatively. RESULTS: All four infusions were safe and without adverse events. The mean unmedicated UPDRS part III and PDQ-39 scores improved by 24% and 26%, respectively. The Vd:Vi ratio ranged from 2.2 to 2.8 and peaked 45 min from the onset of infusion, which is when the borders of the GPi could generally be visualized based on T2-weighted MRI. Two patients underwent refinement of the stereotactic targeting based on infusion-enhanced images. CONCLUSIONS: The convective administration of autologous CSF to deep brain structures appears safe and feasible for enhancing intraoperative MRI during stereotactic procedures. Infusion-enhanced imaging with target-specific infusates could be developed to visualize neurochemical circuits or cellular regions that currently are not seen with anatomic/structural MRI.

Chiari I Malformation and Sleep-Disordered Breathing.

Sleep-disordered breathing (SDB) is a frequent symptomatic feature of pediatric Chiari I, reported in at least 24% of patients presenting to neurosurgeons. Here the epidemiology, natural history, pathophysiology, and diagnosis of SDB in Chiari I is reviewed. Diagnosis requires polysomnography, which should be pursued in young or symptomatic patients or those with prominent imaging findings. Review of case series of surgical decompression suggest that surgical decompression can improve SDB in selected patients.

Biomarkers Predictive of Long-Term Outcome After Ischemic Stroke: A Meta-Analysis.

BACKGROUND AND OBJECTIVE: The goal of this study was to systematically review the usefulness of serum biomarkers in the setting of ischemic stroke (IS) to predict long-term outcome. METHODS: A systematic literature review was performed using the PubMed and MEDLINE databases for studies published between 1986 and 2018. All studies assessing long-term functional outcome (defined as ≥30 days) after IS with respect to serum biomarkers were included. Data were extracted and pooled using a meta-analysis of odds ratios. RESULTS: Of the 2928 articles in the original literature search, 183 studies were selected. A total of 127 serum biomarkers were included. Biomarkers were grouped into several categories: inflammatory (n = 32), peptide/enzymatic (n = 30), oxidative/metabolic (n = 28), hormone/steroid based (n = 23), and hematologic/vascular (n = 14). The most commonly studied biomarkers in each category were found to be CRP, S100ß, albumin, copeptin, and D-dimer. With the exception of S100ß, all were found to be statistically associated with >30-day outcome after ischemic stroke. CONCLUSIONS: Serum-based biomarkers have the potential to predict functional outcome in patients with IS. This meta-analysis has identified C-reactive protein, albumin, copeptin, and D-dimer to be significantly associated with long-term outcome after IS. These biomarkers have the potential to serve as a platform for prognosticating stroke outcomes after 30 days. These serum biomarkers, some of which are routinely ordered, can be combined with imaging biomarkers and used in artificial intelligence algorithms to provide refined predictive outcomes after injury. These tools will assist physicians in providing guidance to families regarding long-term independence of patients.

Interventional outcomes for patients eligible for entry into the ARUBA clinical trial: a systematic review and meta-analysis.

OBJECTIVE: A Randomized Trial of Unruptured Brain Arteriovenous Malformations (ARUBA) suggested that medical management afforded outcomes superior to those following intervention for unruptured arteriovenous malformations (AVMs), but its findings have been controversial. Subsequent studies of AVMs that would have met the eligibility requirements of ARUBA have supported intervention for the management of some cases. The present meta-analysis was conducted with the object of summarizing interventional outcomes for ARUBA-eligible patients reported in the literature. METHODS: A systematic literature search (PubMed, Web of Science, Google Scholar) for AVM intervention studies that used inclusion criteria identical to those of ARUBA (age ≥ 18 years, no history of AVM hemorrhage, no prior intervention) was performed. The primary outcome was death or symptomatic stroke. Secondary outcomes included AVM obliteration, hemorrhage, death, and poor outcome (modified Rankin Scale score ≥ 2 at final follow-up). Bias assessment was performed with the Newcastle-Ottawa Scale, and the results were synthesized as pooled proportions. RESULTS: Of the 343 articles identified through database searches, 13 studies met the inclusion criteria, yielding an overall study cohort of 1909 patients. The primary outcome occurred in 11.2% of patients (pooled = 11%, 95% CI 8%-13%). The rates of AVM obliteration, hemorrhage, poor outcome, and death were 72.7% (pooled = 78%, 95% CI 70%-85%), 8.4% (pooled = 8%, 95% CI 6%-11%), 9.9% (pooled = 10%, 95% CI 7%-13%), and 3.5% (pooled = 2%, 95% CI 1%-4%), respectively. Annualized primary outcome and hemorrhage risks were 1.85 (pooled = 2.05, 95% CI 1.31-2.94) and 1.34 (pooled = 1.41, 95% CI 0.83-2.13) per 100 patient-years, respectively. CONCLUSIONS: Intervention for unruptured AVMs affords acceptable outcomes for appropriately selected patients. The risk of hemorrhage following intervention compared favorably to the natural history of unruptured AVMs. The included studies were retrospective and varied in treatment and AVM characteristics, thereby limiting the generalizability of their data. Future studies from prospective registries may clarify patient, nidus, and intervention selection criteria that will refine the challenging management of patients with unruptured AVMs.

Pituitary Tumors in the Computational Era, Exploring Novel Approaches to Diagnosis, and Outcome Prediction with Machine Learning.

BACKGROUND: Machine learning has emerged as a viable asset in the setting of pituitary surgery. In the past decade, the number of machine learning models developed to aid in the diagnosis of pituitary lesions and predict intraoperative and postoperative complications following transsphenoidal surgery has increased exponentially. As computational processing power continues to increase, big data sets continue to expand, and learning algorithms continue to surpass gold standard predictive tools, machine learning will serve to become an important component in improving patient care and outcomes. METHODS: Relevant studies were identified based on a literature search in PubMed and MEDLINE databases, as well as from other sources including reference lists of published articles. RESULTS: Radiomics and artificial neural networks comprise the majority of machine learning-based applications in pituitary surgery. Radiomics serves to quantify specific imaging features, which can then be used to noninvasively identify tumor characteristics and make definitive diagnoses, circumventing presurgical biopsy altogether. Neural networks can be adapted to predict intraoperative changes in visual evoked potentials or cerebral spinal fluid leak. In addition, these algorithms may be combined with others to predict tumor aggressiveness, gross total resection, recurrence and remission, and even total cost burden. CONCLUSIONS: The field of machine learning is broad, with radiomics and artificial neural networks comprising 2 commonly used supervised learning methods in pituitary surgery. Given the large heterogeneity of pituitary and sellar lesions, the promise of machine learning lies in its ability to identify relationships and patterns that are otherwise hidden from standard statistical methods. While machine learning has great potential as a clinical adjunct during the surgical preplanning process and in predicting complications and outcomes, challenges moving forward include standardization and validation of these paradigms.