Hypertriglyceridemia-induced acute necrotizing pancreatitis: Poor clinical outcomes requiring revisiting management modalities.

Hypertriglyceridemia-induced acute pancreatitis (HTG-AP) is the third most common cause of AP after gallstones and alcohol. Supportive measures, intravenous insulin, and plasmapheresis are possible treatment modalities for HTG-AP; however, definitive guidelines evaluating the best therapeutic approach are not clearly established. We present a rare case of a 42-year-old male without known comorbidities who was found to have HTG-AP. Despite early initiation of intravenous insulin and plasmapheresis and the initial decline in his triglycerides level, his condition was complicated by necrotizing pancreatitis and subsequent multi-organ failure. Future studies are warranted to evaluate the role of plasmapheresis in HTG-AP and its efficacy.

Glial Cell Adhesion Molecule (GlialCAM) Determines Proliferative versus Invasive Cell States in Glioblastoma.

The malignant brain cancer glioblastoma (GBM) contains groups of highly invasive cells that drive tumor progression as well as recurrence after surgery and chemotherapy. The molecular mechanisms that enable these GBM cells to exit the primary mass and disperse throughout the brain remain largely unknown. Here we report using human tumor specimens and primary spheroids from male and female patients that glial cell adhesion molecule (GlialCAM), which has normal roles in brain astrocytes and is mutated in the developmental brain disorder megalencephalic leukoencephalopathy with subcortical cysts (MLC), is differentially expressed in subpopulations of GBM cells. High levels of GlialCAM promote cell-cell adhesion and a proliferative GBM cell state in the tumor core. In contrast, GBM cells with low levels of GlialCAM display diminished proliferation and enhanced invasion into the surrounding brain parenchyma. RNAi-mediated inhibition of GlialCAM expression leads to activation of proinvasive extracellular matrix adhesion and signaling pathways. Profiling GlialCAM-regulated genes combined with cross-referencing to single-cell transcriptomic datasets validates functional links among GlialCAM, Mlc1, and aquaporin-4 in the invasive cell state. Collectively, these results reveal an important adhesion and signaling axis comprised of GlialCAM and associated proteins including Mlc1 and aquaporin-4 that is critical for control of GBM cell proliferation and invasion status in the brain cancer microenvironment.SIGNIFICANCE STATEMENT Glioblastoma (GBM) contains heterogeneous populations of cells that coordinately drive proliferation and invasion. We have discovered that glial cell adhesion molecule (GlialCAM)/hepatocyte cell adhesion molecule (HepaCAM) is highly expressed in proliferative GBM cells within the tumor core. In contrast, GBM cells with low levels of GlialCAM robustly invade into surrounding brain tissue along blood vessels and white matter. Quantitative RNA sequencing identifies various GlialCAM-regulated genes with functions in cell-cell adhesion and signaling. These data reveal that GlialCAM and associated signaling partners, including Mlc1 and aquaporin-4, are key factors that determine proliferative and invasive cell states in GBM.

The alpha7 integrin subunit in astrocytes promotes endothelial blood-brain barrier integrity.

The blood-brain barrier (BBB) is a vascular endothelial cell boundary that partitions the circulation from the central nervous system to promote normal brain health. We have a limited understanding of how the BBB is formed during development and maintained in adulthood. We used quantitative transcriptional profiling to investigate whether specific adhesion molecules are involved in BBB functions, with an emphasis on understanding how astrocytes interact with endothelial cells. Our results reveal a striking enrichment of multiple genes encoding laminin subunits as well as the laminin receptor gene Itga7, which encodes the alpha7 integrin subunit, in astrocytes. Genetic ablation of Itga7 in mice led to aberrant BBB permeability and progressive neurological pathologies. Itga7-/- mice also showed a reduction in laminin protein expression in parenchymal basement membranes. Blood vessels in the Itga7-/- brain showed separation from surrounding astrocytes and had reduced expression of the tight junction proteins claudin 5 and ZO-1. We propose that the alpha7 integrin subunit in astrocytes via adhesion to laminins promotes endothelial cell junction integrity, all of which is required to properly form and maintain a functional BBB.

Signal Quality Analysis for Long-Term ECG Monitoring Using a Health Patch in Cardiac Patients.

Cardiovascular diseases (CVD) represent a serious health problem worldwide, of which atrial fibrillation (AF) is one of the most common conditions. Early and timely diagnosis of CVD is essential for successful treatment. When implemented in the healthcare system this can ease the existing socio-economic burden on health institutions and government. Therefore, developing technologies and tools to diagnose CVD in a timely way and detect AF is an important research topic. ECG monitoring patches allowing ambulatory patient monitoring over several days represent a novel technology, while we witness a significant proliferation of ECG monitoring patches on the market and in the research labs, their performance over a long period of time is not fully characterized. This paper analyzes the signal quality of ECG signals obtained using a single-lead ECG patch featuring self-adhesive dry electrode technology collected from six cardiac patients for 5 days. In particular, we provide insights into signal quality degradation over time, while changes in the average ECG quality per day were present, these changes were not statistically significant. It was observed that the quality was higher during the nights, confirming the link with motion artifacts. These results can improve CVD diagnosis and AF detection in real-world scenarios.

Data Augmentation and Transfer Learning for Data Quality Assessment in Respiratory Monitoring.

Changes in respiratory rate have been found to be one of the early signs of health deterioration in patients. In remote environments where diagnostic tools and medical attention are scarce, such as deep space exploration, the monitoring of the respiratory signal becomes crucial to timely detect life-threatening conditions. Nowadays, this signal can be measured using wearable technology; however, the use of such technology is often hampered by the low quality of the recordings, which leads more often to wrong diagnosis and conclusions. Therefore, to apply these data in diagnosis analysis, it is important to determine which parts of the signal are of sufficient quality. In this context, this study aims to evaluate the performance of a signal quality assessment framework, where two machine learning algorithms (support vector machine-SVM, and convolutional neural network-CNN) were used. The models were pre-trained using data of patients suffering from chronic obstructive pulmonary disease. The generalization capability of the models was evaluated by testing them on data from a different patient population, presenting normal and pathological breathing. The new patients underwent bariatric surgery and performed a controlled breathing protocol, displaying six different breathing patterns. Data augmentation (DA) and transfer learning (TL) were used to increase the size of the training set and to optimize the models for the new dataset. The effect of the different breathing patterns on the performance of the classifiers was also studied. The SVM did not improve when using DA, however, when using TL, the performance improved significantly (p < 0.05) compared to DA. The opposite effect was observed for CNN, where the biggest improvement was obtained using DA, while TL did not show a significant change. The models presented a low performance for shallow, slow and fast breathing patterns. These results suggest that it is possible to classify respiratory signals obtained with wearable technologies using pre-trained machine learning models. This will allow focusing on the relevant data and avoid misleading conclusions because of the noise, when designing bio-monitoring systems.

Situación epidemiológica de la Tuberculosis infantil en el departamento de Caldas 2016-2018 / Epidemiologic situation about Tuberculosis in children at the department of Caldas 2016-2018

Resumen Objetivo: Describir la epidemiología de la tuberculosis infantil en el Departamento de Caldas (2016-2018). Materiales y métodos: Estudio observacional-descriptivo. Resultados: se registraron 41 casos (4%, 41/1.029). La incidencia fue 2,96/100.000 (2016), 6,31/100.000 (2017) y 4,34 /100.000 (2018). La mediana de edad fue 10 años (RI 2-16). El sexo femenino aporto 53,66%, predominó el estrato socioeconómico bajo (90,24%, 37/41), Manizales aportó la mayor carga de casos (43,9%, 18/41). La TB pulmonar se presentó en 39,02% de los casos de tuberculosis infantil. 68,29% (28/41) reportaron haber tenido síntomas alrededor de 15 días de evolución. 70,73% (29/41) presentarón tos con expectoración y 65,85% (27/41) reportaron astenia o disnea. La PPD se midió en 65,85% de la población (27/41) siendo positiva (> 10 mm) en 62,96% (17/27). El diagnóstico radiológico se realizó en 56,1% (23/41). Se reportaron 10 casos de TB extrapulmonar, la presentación más frecuente fue ganglionar (7/10). Se presentaron dos casos fatales. Discusión: La tuberculosis infantil en Caldas refleja la naturaleza de la enfermedad, afectándose principalmente niñas con vulnerabilidades sociales. El síntoma más común es tos húmeda con expectoración, similar a otros estudios desarrollados en Colombia. No se cuenta con una herramienta diagnostica con rendimiento suficiente, por lo que este sigue siendo un desafío clínico.

Abstract Objective: To describe the epidemiology of childhood TB in the Department of Caldas (2016-2018). Materials and methods: Observational-descriptive study. Results: 41 cases were registered (4%, 41 / 1,029). The incidence was 2.96 / 100,000 (2016), 6.31 / 100,000 (2017) and 4.34 / 100,000 (2018). The median age was 10 years (IR 2-16). The female sex contributed 53.66%, the low socioeconomic status predominated (90.24%, 37/41). Manizales contributed the highest case load (43.9%, 18/41). Pulmonary TB occurred in 39.02% of childhood TB cases. 68.29% (28/41) reported having symptoms around 15 days of evolution. 70.73% (29/41) presented cough with expectoration and 65.85% (27/41) reported asthenia or dyspnea. PPD was measured in 65.85% of the population (27/41), being positive (> 10 mm) in 62.96% (17/27). The radiological diagnosis was made in 56.1% (23/41). 10 cases of extrapulmonary TB were reported, the most frequent presentation was lymph node TB (7/10). Two fatal cases occurred. Discussion: Childhood tuberculosis in Caldas reflects the nature of the disease, mainly affecting girls with social vulnerabilities. The most common symptom is wet cough with expectoration, similar to other studies developed in Colombia. There is not a diagnostic tool with sufficient performance, so this remains a clinical challenge.

The ß8 integrin cytoplasmic domain activates extracellular matrix adhesion to promote brain neurovascular development.

In the developing mammalian brain, neuroepithelial cells interact with blood vessels to regulate angiogenesis, blood-brain barrier maturation and other key neurovascular functions. Genetic studies in mice have shown that neurovascular development is controlled, in part, by Itgb8, which encodes the neuroepithelial cell-expressed integrin ß8 subunit. However, these studies have involved complete loss-of-function Itgb8 mutations, and have not discerned the relative roles for the ß8 integrin extracellular matrix (ECM) binding region versus the intracellular signaling tail. Here, Cre/lox strategies have been employed to selectively delete the cytoplasmic tail of murine Itgb8 without perturbing its transmembrane and extracellular domains. We report that the ß8 integrin cytoplasmic domain is essential for inside-out modulation of adhesion, including activation of latent-TGFßs in the ECM. Quantitative sequencing of the brain endothelial cell transcriptome identifies TGFß-regulated genes with putative links to blood vessel morphogenesis, including several genes linked to Wnt/ß-catenin signaling. These results reveal that the ß8 integrin cytoplasmic domain is essential for the regulation of TGFß-dependent gene expression in endothelial cells and suggest that cross-talk between TGFßs and Wnt pathways is crucial for neurovascular development.

Mlc1-Expressing Perivascular Astrocytes Promote Blood-Brain Barrier Integrity.

In the mammalian brain, perivascular astrocytes (PAs) closely juxtapose blood vessels and are postulated to have important roles in the control of vascular physiology, including regulation of the blood-brain barrier (BBB). Deciphering specific functions for PAs in BBB biology, however, has been limited by the ability to distinguish these cells from other astrocyte populations. In order to characterize selective roles for PAs in vivo, a new mouse model has been generated in which the endogenous megalencephalic leukoencephalopathy with subcortical cysts 1 (Mlc1) gene drives expression of Cre fused to a mutated estrogen ligand-binding domain (Mlc1-T2A-CreERT2). This knock-in mouse model, which we term MLCT, allows for selective identification and tracking of PAs in the postnatal brain. We also demonstrate that MLCT-mediated ablation of PAs causes severe defects in BBB integrity, resulting in premature death. PA loss results in aberrant localization of Claudin 5 and -VE-Cadherin in endothelial cell junctions as well as robust microgliosis. Collectively, these data reveal essential functions for Mlc1-expressing PAs in regulating endothelial barrier integrity in mice and indicate that primary defects in astrocytes that cause BBB breakdown may contribute to human neurologic disorders.SIGNIFICANCE STATEMENT Interlaced among the billions of neurons and glia in the mammalian brain is an elaborate network of blood vessels. Signals from the brain parenchyma control the unique permeability properties of cerebral blood vessels known as the blood-brain barrier (BBB). However, we understand very little about the relative contributions of different neural cell types in the regulation of BBB functions. Here, we show that a specific subpopulation of astrocyte is essential for control of BBB integrity, with ablation of these cells leading to defects in endothelial cell junctions, BBB breakdown, and resulting neurologic deficits.

Technical aspects of cardiorespiratory estimation using subspace projections and cross entropy.

Background.Respiratory sinus arrhythmia (RSA) is a form of cardiorespiratory coupling. Its quantification has been suggested as a biomarker to diagnose different diseases. Two state-of-the-art methods, based on subspace projections and entropy, are used to estimate the RSA strength and are evaluated in this paper. Their computation requires the selection of a model order, and their performance is strongly related to the temporal and spectral characteristics of the cardiorespiratory signals.Objective.To evaluate the robustness of the RSA estimates to the selection of model order, delays, changes of phase and irregular heartbeats as well as to give recommendations for their interpretation on each case.Approach.Simulations were used to evaluate the model order selection when calculating the RSA estimates introduced before, as well as three different scenarios that can occur in signals acquired in non-controlled environments and/or from patient populations: the presence of irregular heartbeats; the occurrence of delays between heart rate variability (HRV) and respiratory signals; and the changes over time of the phase between HRV and respiratory signals.Main results.It was found that using a single model order for all the calculations suffices to characterize RSA correctly. In addition, the RSA estimation in signals containing more than 5 irregular heartbeats in a period of 5 min might be misleading. Regarding the delays between HRV and respiratory signals, both estimates are robust. For the last scenario, the two approaches tolerate phase changes up to 54°, as long as this lasts less than one fifth of the recording duration.Significance.Guidelines are given to compute the RSA estimates in non-controlled environments and patient populations.

Cardiopulmonary coupling indices to assess weaning readiness from mechanical ventilation.

The ideal moment to withdraw respiratory supply of patients under Mechanical Ventilation at Intensive Care Units (ICU), is not easy to be determined for clinicians. Although the Spontaneous Breathing Trial (SBT) provides a measure of the patients' readiness, there is still around 15-20% of predictive failure rate. This work is a proof of concept focused on adding new value to the prediction of the weaning outcome. Heart Rate Variability (HRV) and Cardiopulmonary Coupling (CPC) methods are evaluated as new complementary estimates to assess weaning readiness. The CPC is related to how the mechanisms regulating respiration and cardiac pumping are working simultaneously, and it is defined from HRV in combination with respiratory information. Three different techniques are used to estimate the CPC, including Time-Frequency Coherence, Dynamic Mutual Information and Orthogonal Subspace Projections. The cohort study includes 22 patients in pressure support ventilation, ready to undergo the SBT, analysed in the 24 h previous to the SBT. Of these, 13 had a successful weaning and 9 failed the SBT or needed reintubation -being both considered as failed weaning. Results illustrate that traditional variables such as heart rate, respiratory frequency, and the parameters derived from HRV do not differ in patients with successful or failed weaning. Results revealed that HRV parameters can vary considerably depending on the time at which they are measured. This fact could be attributed to circadian rhythms, having a strong influence on HRV values. On the contrary, significant statistical differences are found in the proposed CPC parameters when comparing the values of the two groups, and throughout the whole recordings. In addition, differences are greater at night, probably because patients with failed weaning might be experiencing more respiratory episodes, e.g. apneas during the night, which is directly related to a reduced respiratory sinus arrhythmia. Therefore, results suggest that the traditional measures could be used in combination with the proposed CPC biomarkers to improve weaning readiness.

Benchmarking Transfer Entropy Methods for the Study of Linear and Nonlinear Cardio-Respiratory Interactions.

Transfer entropy (TE) has been used to identify and quantify interactions between physiological systems. Different methods exist to estimate TE, but there is no consensus about which one performs best in specific applications. In this study, five methods (linear, k-nearest neighbors, fixed-binning with ranking, kernel density estimation and adaptive partitioning) were compared. The comparison was made on three simulation models (linear, nonlinear and linear + nonlinear dynamics). From the simulations, it was found that the best method to quantify the different interactions was adaptive partitioning. This method was then applied on data from a polysomnography study, specifically on the ECG and the respiratory signals (nasal airflow and respiratory effort around the thorax). The hypothesis that the linear and nonlinear components of cardio-respiratory interactions during light and deep sleep change with the sleep stage, was tested. Significant differences, after performing surrogate analysis, indicate an increased TE during deep sleep. However, these differences were found to be dependent on the type of respiratory signal and sampling frequency. These results highlight the importance of selecting the appropriate signals, estimation method and surrogate analysis for the study of linear and nonlinear cardio-respiratory interactions.

Artefact Detection in Impedance Pneumography Signals: A Machine Learning Approach.

Impedance pneumography has been suggested as an ambulatory technique for the monitoring of respiratory diseases. However, its ambulatory nature makes the recordings more prone to noise sources. It is important that such noisy segments are identified and removed, since they could have a huge impact on the performance of data-driven decision support tools. In this study, we investigated the added value of machine learning algorithms to separate clean from noisy bio-impedance signals. We compared three approaches: a heuristic algorithm, a feature-based classification model (SVM) and a convolutional neural network (CNN). The dataset consists of 47 chronic obstructive pulmonary disease patients who performed an inspiratory threshold loading protocol. During this protocol, their respiration was recorded with a bio-impedance device and a spirometer, which served as a gold standard. Four annotators scored the signals for the presence of artefacts, based on the reference signal. We have shown that the accuracy of both machine learning approaches (SVM: 87.77 ± 2.64% and CNN: 87.20 ± 2.78%) is significantly higher, compared to the heuristic approach (84.69 ± 2.32%). Moreover, no significant differences could be observed between the two machine learning approaches. The feature-based and neural network model obtained a respective AUC of 92.77±2.95% and 92.51±1.74%. These findings show that a data-driven approach could be beneficial for the task of artefact detection in respiratory thoracic bio-impedance signals.

Linear and Non-linear Quantification of the Respiratory Sinus Arrhythmia Using Support Vector Machines.

Respiratory sinus arrhythmia (RSA) is a form of cardiorespiratory coupling. It is observed as changes in the heart rate in synchrony with the respiration. RSA has been hypothesized to be due to a combination of linear and nonlinear effects. The quantification of the latter, in turn, has been suggested as a biomarker to improve the assessment of several conditions and diseases. In this study, a framework to quantify RSA using support vector machines is presented. The methods are based on multivariate autoregressive models, in which the present samples of the heart rate variability are predicted as combinations of past samples of the respiration. The selection and tuning of a kernel in these models allows to solve the regression problem taking into account only the linear components, or both the linear and the nonlinear ones. The methods are tested in simulated data as well as in a dataset of polysomnographic studies taken from 110 obstructive sleep apnea patients. In the simulation, the methods were able to capture the nonlinear components when a weak cardiorespiratory coupling occurs. When the coupling increases, the nonlinear part of the coupling is not detected and the interaction is found to be of linear nature. The trends observed in the application in real data show that, in the studied dataset, the proposed methods captured a more prominent linear interaction than the nonlinear one.

Gastric accumulation of enteral nutrition reduces pressure changes induced by phasic contractility in an isovolumetric intragastric balloon.

BACKGROUND: An isovolumetric intragastric balloon to continuously measure gastric phasic contractility was recently developed by us. We aimed to investigate the readout of this technique in relation to gastric content and gastric emptying. METHODS: In this crossover investigation, the VIPUNTM Gastric Monitoring System, which comprises a double lumen nasogastric feeding tube with integrated intragastric balloon, was used to assess phasic gastric contractility by interpretation of the pressure in an isovolumetric balloon in 10 healthy subjects. Balloon pressure was recorded in fasted state, during a 2-hour intragastric nutrient infusion (1 kcal/ml at 25, 75, or 250 ml/h) and 4 hours post-infusion, and quantified as Gastric Balloon Motility Index (GBMI), ranging from 0 (no contractility) to 1 (maximal contractility). Gastric accumulation was quantified with magnetic resonance imaging and gastric emptying with a13 C-breath test. Results are expressed as mean(SD). KEY RESULTS: GBMI was significantly lower during infusion at 250 ml/h compared to baseline (0.13(0.05) versus 0.46(0.12)) and compared to infusion at 25 (0.54(0.21)) and 75 ml/h (0.43(0.20)), all P < 0.005. Gastric content volume was larger after infusion at 250 versus 75 ml/h (P < 0.001). Half-emptying time and accumulation were both negatively correlated with postprandial contractility. Postprandial GBMI was significantly lower when GCV>0 ml compared to when the stomach was empty. CONCLUSIONS AND INFERENCES: Enteral nutrition dose-dependently decreased the contractility readout. This decrease was linked to gastric accumulation of enteral nutrition.

Supervised SVM Transfer Learning for Modality-Specific Artefact Detection in ECG.

The electrocardiogram (ECG) is an important diagnostic tool for identifying cardiac problems. Nowadays, new ways to record ECG signals outside of the hospital are being investigated. A promising technique is capacitively coupled ECG (ccECG), which allows ECG signals to be recorded through insulating materials. However, as the ECG is no longer recorded in a controlled environment, this inevitably implies the presence of more artefacts. Artefact detection algorithms are used to detect and remove these. Typically, the training of a new algorithm requires a lot of ground truth data, which is costly to obtain. As many labelled contact ECG datasets exist, we could avoid the use of labelling new ccECG signals by making use of previous knowledge. Transfer learning can be used for this purpose. Here, we applied transfer learning to optimise the performance of an artefact detection model, trained on contact ECG, towards ccECG. We used ECG recordings from three different datasets, recorded with three recording devices. We showed that the accuracy of a contact-ECG classifier improved between 5 and 8% by means of transfer learning when tested on a ccECG dataset. Furthermore, we showed that only 20 segments of the ccECG dataset are sufficient to significantly increase the accuracy.

Continuous Assessment of Gastric Motility and Its Relation to Gastric Emptying in Adult Critically Ill Patients.

BACKGROUND: Critically ill patients frequently develop feeding intolerance, which is difficult to predict. In healthy subjects, gastric motility, assessed by nasogastric balloon tube, correlated with gastric emptying. We now investigated this correlation in critically ill patients, as well as the feasibility and safety of such application in a pilot study. METHODS: Endotracheally intubated adults scheduled to receive enteral nutrition (EN) were included. After insertion of a double-lumen nasogastric balloon tube and radiographic confirmation of position, balloon pressure was recorded for 10 hours after inflation (4 hours fasted, 2 hours during administration of 13 C-labeled EN, and 4 hours postprandially). Gastric motility was expressed as Gastric Balloon Motility Index (GBMI), reflecting the fraction of time in which phasic gastric contractions occurred. Gastric emptying was assessed by 13 C-octanoate breath test and expressed as gastric half-emptying time (GET½). Correlation between GBMI (assessed in different time intervals) and GET½ was investigated by Pearson/Spearman correlation. Feasibility was defined as the success of tube placement and pressure recording. Safety was assessed based on adverse device effects. RESULTS: Thirty patients were enrolled, of whom 19 had paired GBMI and GET½ data. There was no correlation between GBMI and GET½. The tube was successfully placed in 28/30 (93.3%) patients. In 3/28 (10.7%) patients, balloon leakage precluded analysis. Two safety events were directly linked to the device. CONCLUSION: This pilot study showed no significant correlation between balloon-assessed gastric motility and emptying in critically ill patients. The feasibility/safety profile of the balloon tube appears similar to that of standard nasogastric tubes.

Model-Based Evaluation of Methods for Respiratory Sinus Arrhythmia Estimation.

OBJECTIVE: Respiratory sinus arrhythmia (RSA) refers to heart rate oscillations synchronous with respiration, and it is one of the major representations of cardiorespiratory coupling. Its strength has been suggested as a biomarker to monitor different conditions, and diseases. Some approaches have been proposed to quantify the RSA, but it is unclear which one performs best in specific scenarios. The main objective of this study is to compare seven state-of-the-art methods for RSA quantification using data generated with a model proposed to simulate, and control the RSA. These methods are also compared, and evaluated on a real-life application, for their ability to capture changes in cardiorespiratory coupling during sleep. METHODS: A simulation model is used to create a dataset of heart rate variability, and respiratory signals with controlled RSA, which is used to compare the RSA estimation approaches. To compare the methods objectively in real-life applications, regression models trained on the simulated data are used to map the estimates to the same measurement scale. Results, and conclusion: RSA estimates based on cross entropy, time-frequency coherence, and subspace projections showed the best performance on simulated data. In addition, these estimates captured the expected trends in the changes in cardiorespiratory coupling during sleep similarly. SIGNIFICANCE: An objective comparison of methods for RSA quantification is presented to guide future analyses. Also, the proposed simulation model can be used to compare existing, and newly proposed RSA estimates. It is freely accessible online.

Megalencephalic leukoencephalopathy with subcortical cysts 1 (MLC1) promotes glioblastoma cell invasion in the brain microenvironment.

Glioblastoma (GBM), or grade IV astrocytoma, is a malignant brain cancer that contains subpopulations of proliferative and invasive cells that coordinately drive primary tumor growth, progression, and recurrence after therapy. Here, we have analyzed functions for megalencephalic leukoencephalopathy with subcortical cysts 1 (Mlc1), an eight-transmembrane protein normally expressed in perivascular brain astrocyte end feet that is essential for neurovascular development and physiology, in the pathogenesis of GBM. We show that Mlc1 is expressed in human stem-like GBM cells (GSCs) and is linked to the development of primary and recurrent GBM. Genetically inhibiting MLC1 in GSCs using RNAi-mediated gene silencing results in diminished growth and invasion in vitro as well as impaired tumor initiation and progression in vivo. Biochemical assays identify the receptor tyrosine kinase Axl and its intracellular signaling effectors as important for MLC1 control of GSC invasive growth. Collectively, these data reveal key functions for MLC1 in promoting GSC growth and invasion, and suggest that targeting the Mlc1 protein or its associated signaling effectors may be a useful therapy for blocking tumor progression in patients with primary or recurrent GBM.

The vascular endothelial cell-expressed prion protein doppel promotes angiogenesis and blood-brain barrier development.

The central nervous system (CNS) contains a complex network of blood vessels that promote normal tissue development and physiology. Abnormal control of blood vessel morphogenesis and maturation is linked to the pathogenesis of various neurodevelopmental diseases. The CNS-specific genes that regulate blood vessel morphogenesis in development and disease remain largely unknown. Here, we have characterized functions for the gene encoding prion protein 2 (Prnd) in CNS blood vessel development and physiology. Prnd encodes the glycosylphosphatidylinositol (GPI)-linked protein doppel, which is expressed on the surface of angiogenic vascular endothelial cells, but is absent in quiescent endothelial cells of the adult CNS. During CNS vascular development, doppel interacts with receptor tyrosine kinases and activates cytoplasmic signaling pathways involved in endothelial cell survival, metabolism and migration. Analysis of mice genetically null for Prnd revealed impaired CNS blood vessel morphogenesis and associated endothelial cell sprouting defects. Prnd-/- mice also displayed defects in endothelial barrier integrity. Collectively, these data reveal novel mechanisms underlying doppel control of angiogenesis in the developing CNS, and may provide new insights about dysfunctional pathways that cause vascular-related CNS disorders.

Repeated Gastric Motility Measurement Affects Gastric Motility and Epigastric Symptom Sensation.

Background: Gastric motility is an important determinant of gastric emptying, epigastric symptom generation, and intolerance to food. Motility is classically assessed directly using manometry or an intragastric balloon. These diagnostic methods are perceived as stressful and invasive, which, by itself might influence the readout of these assessments. Our hypothesis was that with repeated exposure to an invasive motility test the outcome would be different. Methods: Gastric motility was assessed with a custom-made orogastric balloon catheter in 10 healthy subjects naive to intubation. A motility index ranging from 0 (no motility) to 1 (maximum motility) was calculated in the fasted state for 3.5 h. Symptoms were surveyed with visual analog scales of 100 mm. Results are presented as median (interquartile range). Results: Motility index during visit 1 [0.40 (0.37-0.59)] was lower compared to visit 2 [0.50 (0.45-0.66); not significant] and 3 [0.63 (0.50-0.71); p = 0.016]. Nausea and pain scores were significantly higher during visit 1 (35 (2.8-126) and 103 (88-125) mm, respectively) compared with visit 3 [1 (2.8-26) mm (p = 0.016) and 75 (30-100) mm (p = 0.008), respectively]. No adverse events were observed. Conclusions: Repeated exposure to an invasive method to assess motility resulted in more vigorous motility and lower symptom scores. Caution is warranted when interpreting functional assessments, as prior exposure to invasive tests might confound the obtained results through habituation.