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Background and Purpose The goal of this study was to systematically review the utility 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 1986-2018. All studies assessing long-term functional outcome (defined as 30 days or greater) following IS with respect to serum biomarkers were included. Data were extracted and pooled using a meta-analysis of odds ratios. Results Of the total 2928 articles in the original literature search, 183 studies were ultimately selected. A total of 127 serum biomarkers were included. Biomarkers were grouped into several categories: inflammatory (32), peptide/enzymatic (30), oxidative/metabolic (28), hormone/steroid based (23), and hematologic/vascular (14). The most commonly studied biomarkers in each category were found to be CRP, S100β, albumin, copeptin, and D-dimer. With the exception of S100β, 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 IS patients. This meta-analysis has identified CRP, 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. Ultimately these tools will assist physicians in providing guidance to families with regards to long-term independence of patients.

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.

From their origins as cardiovascular research tools, endovascular techniques have evolved to provide a minimally invasive means of diagnosis and therapy for individuals suffering from occlusive artery disease. The techniques were pioneered by William Harvey, whose work set the stage for all subsequent endovascular experiments. These included the bold self-catheterization procedure performed by Werner Forssmann in 1929, which would lead to his dismissal by his superiors, only to regain respect within the medical community in 1956 on receiving the Nobel Prize. Charles Dotter was the first to understand the true potential of endovascular approaches after a chance recanalization that would catapult arterial catheterization first into the cardiovascular surgical arena, then into neurosurgery for intracranial stenoses. Having been meticulously evaluated and compared with open vascular procedures, endovascular neurosurgery has continued to be refined and optimized. Understanding the history and development of these techniques and their applications in neurosurgery is necessary to appreciate the current clinical utility of these procedures, serving to provide the vascular neurosurgeon a greater array of treatment options for patients. Here we explore the major scientific and technological advancements that facilitated the development of the endovascular approach to cerebral revascularization, as well as current indications and ongoing clinical trials.

Cortical spreading depression (CSD) is a pathophysiologic phenomenon that describes an expanding wave of depolarization within the cortical gray matter. Originally described over 70 years ago, this spreading depression disrupts neuronal and glial ionic equilibrium, leading to increased energy demands that can cause a metabolic crisis. This results in secondary insult, further perpetuating brain injury and neuronal death. Initially not thought to be of clinical significance, the view of CSD was modified with the advent of intracranial electroencephalography, or electrocorticography. With these improved monitoring techniques, CSD has been identified as a major mechanism by which traumatic brain injury (TBI) imparts its negative sequalae. TBI is a heterogenous disease process that runs the gamut of clinical presentations. This includes concussion, epidural and subdural hematoma, diffuse axonal injury, and subarachnoid hemorrhage. Nonetheless, CSD appears to be frequently occurring among the various types of TBI, thus allowing for the potential development of targeted therapies in an otherwise ill-fated patient cohort. Although a complete understanding of the interplay between CSD and TBI has not yet been achieved, the authors recount the efforts that have been employed over the last several decades in an effort to bridge this gap. In addition, our current understanding of the role neuroimmune cells play in CSD is discussed in the context of TBI. Finally, current therapeutic strategies using CSD as a pharmacologic target are explored with respect to their clinical use in patients with TBI.