Marike L. D. Broekman

Possibilities and limitations of current technologies for quantification of biological extracellular vesicles and synthetic mimics

Nano-sized extracelullar vesicles (EVs) released by various cell types play important roles in a plethora of (patho)physiological processes and are increasingly recognized as biomarkers for disease. In addition, engineered EV and EV-inspired liposomes hold great potential as drug delivery systems. Major technologies developed for high-throughput analysis of individual EV include nanoparticle tracking analysis (NTA), tunable resistive pulse sensing (tRPS) and high-resolution flow cytometry (hFC). Currently, there is a need for comparative studies on the available technologies to improve standardization of vesicle analysis in diagnostic or therapeutic settings. We investigated the possibilities, limitations and comparability of NTA, tRPS and hFC for analysis of tumor cell-derived EVs and synthetic mimics (i.e. differently sized liposomes). NTA and tRPS instrument settings were identified that significantly affected the quantification of these particles. Furthermore, we detailed the differences in absolute quantification of EVs and liposomes using the three technologies. This study increases our understanding of possibilities and pitfalls of NTA, tRPS and hFC, which will benefit standardized and large-scale clinical application of (engineered) EVs and EV-mimics in the future.

Glioblastoma-derived extracellular vesicles modify the phenotype of monocytic cells.

Glioblastoma multiforme (GBM) is the most common primary brain tumor and is without exception lethal. GBMs modify the immune system, which contributes to the aggressive nature of the disease. Particularly, cells of the monocytic lineage, including monocytes, macrophages and microglia, are affected. We investigated the influence of GBM‐derived extracellular vesicles (EVs) on the phenotype of monocytic cells. Proteomic profiling showed GBM EVs to be enriched with proteins functioning in extracellular matrix interaction and leukocyte migration. GBM EVs appeared to skew the differentiation of peripheral blood‐derived monocytes to alternatively activated/M2‐type macrophages. This was observed for EVs from an established cell line, as well as for EVs from primary cultures of GBM stem‐like cells (GSCs). Unlike EVs of non‐GBM origin, GBM EVs induced modified expression of cell surface proteins, modified cytokine secretion (e.g., an increase in vascular endothelial growth factor and IL‐6) and increased phagocytic capacity of the macrophages. Most pronounced effects were observed upon incubation with EVs from mesenchymal GSCs. GSC EVs also affected primary human microglia, resulting in increased expression of Membrane type 1‐matrix metalloproteinase, a marker for GBM microglia and functioning as tumor‐supportive factor. In conclusion, GBM‐derived EVs can modify cells of the monocytic lineage, which acquire characteristics that resemble the tumor‐supportive phenotypes observed in patients.

A standardized method to determine the concentration of extracellular vesicles using tunable resistive pulse sensing

Background Understanding the pathogenic role of extracellular vesicles (EVs) in disease and their potential diagnostic and therapeutic utility is extremely reliant on in-depth quantification, measurement and identification of EV sub-populations. Quantification of EVs has presented several challenges, predominantly due to the small size of vesicles such as exosomes and the availability of various technologies to measure nanosized particles, each technology having its own limitations. Materials and Methods A standardized methodology to measure the concentration of extracellular vesicles (EVs) has been developed and tested. The method is based on measuring the EV concentration as a function of a defined size range. Blood plasma EVs are isolated and purified using size exclusion columns (qEV) and consecutively measured with tunable resistive pulse sensing (TRPS). Six independent research groups measured liposome and EV samples with the aim to evaluate the developed methodology. Each group measured identical samples using up to 5 nanopores with 3 repeat measurements per pore. Descriptive statistics and unsupervised multivariate data analysis with principal component analysis (PCA) were used to evaluate reproducibility across the groups and to explore and visualise possible patterns and outliers in EV and liposome data sets. Results PCA revealed good reproducibility within and between laboratories, with few minor outlying samples. Measured mean liposome (not filtered with qEV) and EV (filtered with qEV) concentrations had coefficients of variance of 23.9% and 52.5%, respectively. The increased variance of the EV concentration measurements could be attributed to the use of qEVs and the polydisperse nature of EVs. Conclusion The results of this study demonstrate the feasibility of this standardized methodology to facilitate comparable and reproducible EV concentration measurements.

Agents for fluorescence-guided glioma surgery: a systematic review of preclinical and clinical results

BackgroundFluorescence-guided surgery (FGS) is a technique used to enhance visualization of tumor margins in order to increase the extent of tumor resection in glioma surgery. In this paper, we systematically review all clinically tested fluorescent agents for application in FGS for glioma and all preclinically tested agents with the potential for FGS for glioma.MethodsWe searched the PubMed and Embase databases for all potentially relevant studies through March 2016. We assessed fluorescent agents by the following outcomes: rate of gross total resection (GTR), overall and progression-free survival, sensitivity and specificity in discriminating tumor and healthy brain tissue, tumor-to-normal ratio of fluorescent signal, and incidence of adverse events.ResultsThe search strategy resulted in 2155 articles that were screened by titles and abstracts. After full-text screening, 105 articles fulfilled the inclusion criteria evaluating the following fluorescent agents: 5-aminolevulinic acid (5-ALA) (44 studies, including three randomized control trials), fluorescein (11), indocyanine green (five), hypericin (two), 5-aminofluorescein-human serum albumin (one), endogenous fluorophores (nine) and fluorescent agents in a pre-clinical testing phase (30). Three meta-analyses were also identified.Conclusions5-ALA is the only fluorescent agent that has been tested in a randomized controlled trial and results in an improvement of GTR and progression-free survival in high-grade gliomas. Observational cohort studies and case series suggest similar outcomes for FGS using fluorescein. Molecular targeting agents (e.g., fluorophore/nanoparticle labeled with anti-EGFR antibodies) are still in the pre-clinical phase, but offer promising results and may be valuable future alternatives.

Readmission and Other Adverse Events after Transsphenoidal Surgery: Prevalence, Timing, and Predictive Factors

BACKGROUNDnTranssphenoidal surgery is a common neurosurgical procedure for accessing the pituitary and anterior skull base, yet few multicenter analyses have evaluated outcomes after this procedure.nnnSTUDY DESIGNnPatients undergoing transsphenoidal surgery from 2006 to 2015 were extracted from the American College of Surgeons NSQIP database. Logistic regression was used to identify predictors of 30-day complications.nnnRESULTSnOf 1,240 patients included in this analysis, 6.9% experienced a major complication, and 9.4% experienced any complication within 30 days. Other adverse events included death in 0.7% and nonroutine hospital discharge in 5.3%. Most adverse events occurred within the first 2 weeks postoperatively; 82.9% of patients experienced their first complication during the initial hospital stay. Multivariable analysis demonstrated that predictors of hospital stay longer than 4 days included American Society of Anesthesiologists classification III to V (pxa0= 0.015), insulin-dependent diabetes mellitus (p < 0.001), and operative time in the third and fourth quartiles (both p < 0.001). American Society of Anesthesiologists classification III to V and operative time in the fourth quartile were also predictors of any adverse event (pxa0= 0.01 and pxa0= 0.005, respectively). Among these patients, 3.7% underwent reoperation, the most common reason for which was postoperative cerebrospinal fluid leak (63.6%). Readmission occurred after 8.5% of cases at a median of 11.0 days post-discharge. The most common cause of readmission was hyponatremia (29.5%), followed by delayed postoperative cerebrospinal fluid leak (16.0%).nnnCONCLUSIONSnOverall rates of adverse events in patients undergoing transsphenoidal surgery are relatively low, and most occur before discharge from the hospital. Post-discharge complications associated with transsphenoidal surgery include deep vein thrombosis, pulmonary embolism, and urinary tract infection. Delayed postoperative cerebrospinal fluid leak is the major cause of reoperation, and hyponatremia is the major cause of readmission.

Tunable Resistive Pulse Sensing for the Characterization of Extracellular Vesicles.

Accurate characterization of extracellular vesicles (EVs), including exosomes and microvesicles, is essential to obtain further knowledge on the biological relevance of EVs. Tunable resistive pulse sensing (tRPS) has shown promise as a method for single particle-based quantification and size profiling of EVs. Here, we describe the technical background of tRPS and its applications for EV characterization. Besides the standard protocol, we describe an alternative protocol, in which samples are spiked with polystyrene beads of known size and concentration. This alternative protocol can be used to overcome some of the challenges of direct EV characterization in biological fluids.

Natural and Artificial Intelligence in Neurosurgery: A Systematic Review

BACKGROUND Machine learning (ML) is a domain of artificial intelligence that allows computer algorithms to learn from experience without being explicitly programmed. OBJECTIVE To summarize neurosurgical applications of ML where it has been compared to clinical expertise, here referred to as “natural intelligence.” METHODS A systematic search was performed in the PubMed and Embase databases as of August 2016 to review all studies comparing the performance of various ML approaches with that of clinical experts in neurosurgical literature. RESULTS Twenty‐three studies were identified that used ML algorithms for diagnosis, presurgical planning, or outcome prediction in neurosurgical patients. Compared to clinical experts, ML models demonstrated a median absolute improvement in accuracy and area under the receiver operating curve of 13% (interquartile range 4‐21%) and 0.14 (interquartile range 0.07‐0.21), respectively. In 29 (58%) of the 50 outcome measures for which a P‐value was provided or calculated, ML models outperformed clinical experts (P < .05). In 18 of 50 (36%), no difference was seen between ML and expert performance (P > .05), while in 3 of 50 (6%) clinical experts outperformed ML models (P < .05). All 4 studies that compared clinicians assisted by ML models vs clinicians alone demonstrated a better performance in the first group. CONCLUSION We conclude that ML models have the potential to augment the decision‐making capacity of clinicians in neurosurgical applications; however, significant hurdles remain associated with creating, validating, and deploying ML models in the clinical setting. Shifting from the preconceptions of a human‐vs‐machine to a human‐and‐machine paradigm could be essential to overcome these hurdles.

Glioblastoma Multiforme in the Posterior Cranial Fossa in a Patient with Neurofibromatosis Type I

Patients with Neurofibromatosis type 1 (NF1) have an increased risk of developing neoplasms. The most common brain tumors, found in 15%–20% of NF1 patients, are hypothalamic-optic gliomas, followed by brainstem and cerebellar pilocytic astrocytomas. These tumors generally have a benign nature. NF1 patients are predisposed to a 5-fold increased incidence of high-grade astrocytomas, which are usually located in supratentorial regions of the brain. We present an NF1 patient who developed a high-grade astrocytoma in the posterior fossa and discuss possible pathophysiological mechanisms.

Machine Learning and Neurosurgical Outcome Prediction: A Systematic Review

OBJECTIVEnAccurate measurement of surgical outcomes is highly desirable to optimize surgical decision-making. An important element of surgical decision making is identification of the patient cohort that will benefit from surgery before the intervention. Machine learning (ML) enables computers to learn from previous data to make accurate predictions on new data. In this systematic review, we evaluate the potential of ML for neurosurgical outcome prediction.nnnMETHODSnA systematic search in the PubMed and Embase databases was performed to identify all potential relevant studies up to January 1,xa02017.nnnRESULTSnThirty studies were identified that evaluated ML algorithms used as prediction models for survival, recurrence, symptom improvement, and adverse events in patients undergoing surgery for epilepsy, brain tumor, spinal lesions, neurovascular disease, movement disorders, traumatic brain injury, and hydrocephalus. Depending on the specific prediction task evaluated and the type of input features included, ML models predicted outcomes after neurosurgery with a median accuracy and area under the receiver operating curve of 94.5% and 0.83, respectively. Compared with logistic regression, ML models performed significantly better and showed a median absolute improvement in accuracy and area under the receiver operating curve of 15% and 0.06, respectively. Some studies also demonstrated a better performance in ML models compared with established prognostic indices and clinical experts.nnnCONCLUSIONSnIn the research setting, ML has been studied extensively, demonstrating an excellent performance in outcome prediction for a wide range of neurosurgical conditions. However, future studies should investigate how ML can be implemented as a practical tool supporting neurosurgical care.

Venous thromboembolism and intracranial hemorrhage after craniotomy for primary malignant brain tumors: a National Surgical Quality Improvement Program analysis

Venous thromboembolism (VTE), including deep venous thrombosis (DVT) and pulmonary embolism (PE), frequently complicates the postoperative course of primary malignant brain tumor patients. Thromboprophylactic anticoagulation is commonly used to prevent VTE at the risk of intracranial hemorrhage (ICH). We extracted all patients who underwent craniotomy for a primary malignant brain tumor from the National Surgical Quality Improvement Program (NSQIP) registry (2005–2015) to perform a time-to-event analysis and identify relevant predictors of DVT, PE, and ICH within 30 days after surgery. Among the 7376 identified patients, the complication rates were 2.6, 1.5, and 1.3% for DVT, PE, and ICH, respectively. VTE was the second-most common major complication and third-most common reason for readmission. ICH was the most common reason for reoperation. The increased risk of VTE extends beyond the period of hospitalization, especially for PE, whereas ICH occurred predominantly within the first days after surgery. Older age and higher BMI were overall predictors of VTE. Dependent functional status and longer operative times were predictive for VTE during hospitalization, but not for post-discharge events. Admission two or more days before surgery was predictive for DVT, but not for PE. Preoperative steroid usage and male gender were predictive for post-discharge DVT and PE, respectively. ICH was associated with various comorbidities and longer operative times. This multicenter study demonstrates distinct critical time periods for the development of thrombotic and hemorrhagic events after craniotomy. Furthermore, the VTE risk profile depends on the type of VTE (DVT vs. PE) and clinical setting (hospitalized vs. post-discharge patients).