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In the context of increasing the performance of structured packing used in distillation columns, we develop a state-of-the-art flow diagnostic method to investigate the intricate fluid dynamics of a falling film interacting with a perforation. Perforations are one of the several key microstructure elements in industrial structured packing whose role in mass transfer enhancement and liquid redistribution is yet not well understood. We focus the present investigation on the liquid curtain, or "twin film", blanketing the perforation. Twin films are prone to strong capillary waves and recirculation zones, and experiments of fundamental nature are required to assess the fluid dynamics in the curtain formation in the perforations and ultimately uncover mechanisms that intensify interfacial mass transfer. Here, a three-dimensional two-component (3D-2C PTV) optical-flow-based technique using the concept of defocus is implemented on a test bench designed to study the effect of perforations on falling liquid films. This method provides an opportunity to evaluate the hydrodynamics of the liquid curtain through streamline patterns and dynamic modes and to describe key features that could promote mass transfer intensification in structured packing. • We present experiments of a falling liquid film interacting with a perforation for twin films. • The flow field inside and around the perforation is investigated using a novel single-camera 3D2C flow diagnostic technique. • Detailed flow features such as recirculations and reverse flow areas are observed and quantified. • Dynamic structures triggered by the perforation are analyzed using Dynamic Mode Decomposition. [ABSTRACT FROM AUTHOR]

Video-based measurement has received increased attention for modal analysis and nondestructive evaluation, playing an important role in the development of the next-generation structural sensing technologies. As these techniques have evolved, more quantitative approaches based on computer vision techniques have emerged on full-field unsupervised structural identification, exploiting the benefits provided by the use of video cameras such as high spatial sensor density and low installation costs. More recent work has started to explore the use of laser point cloud data for 3D mapping of scenes and structures. Sensors such as LIDAR provide huge amounts of measurements at high spatial resolution from which it is possible to estimate accurate structural geometry for applications such as the generation of CAD models. Unfortunately to-date, the frame rate and depth resolution of LIDAR and other sensors capable of 3D geometry measurements has not been sufficient for measuring structural dynamics. In this paper, we introduce an approach for efficient and extremely high resolution 3D structural dynamic identification/modal analysis from point cloud data acquired using a commercial, low-cost, time-of-flight imager. Vibration mode shapes and modal coordinates are extracted from this data by creating virtual Lagrangian sensors based on the point clouds parameters. First, time-varying point cloud data are collected from a vibrating structure. Then, a mesh of virtual sensors is created based on the dynamic point cloud data for tracking the structure's displacement over time. Next solutions to the blind source separation problem are employed to estimate high resolution 3D mode shapes, modal coordinates, and resonant frequencies. We demonstrate the potential of our proposed approach on laboratory tests and compare the results to the data collected from conventional laser displacement sensors. This technique represents an advance towards efficiently exploring the full advantages of using dynamic point cloud data for practical monitoring applications and has the potential to be extended for a wide range of 3D motion decomposition problems. • Computer vision technique for estimating 3D vibration modes. • This is the first attempt to exploit dynamic point clouds for structural dynamics. • A time-of-flight imager is employed to obtain dynamic measurements. • Vibration modes are estimated from the dynamic data by forming virtual sensors. • Modal estimation is blindly achieved by dimension reduction and BSS algorithms. • The technique is experimentally compared to results obtained from laser data. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 a13C‐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. [ABSTRACT FROM AUTHOR]

• A novel way for ECG quality assessment is proposed, based on the posterior probability of an artefact detection classifier. • A good performance was obtained when testing the classifier on two independent (re)labelled datasets, thereby showing its robustness. The performance was better, compared to a heuristic method and comparable to another machine learning algorithm. • A significant correlation was observed between the proposed quality assessment and the annotators level of agreement. • Significant decreases in quality level were observed for different noise levels. The presence of noise sources could reduce the diagnostic capability of the ECG signal and result in inappropriate treatment decisions. To mitigate this problem, automated algorithms to detect artefacts and quantify the quality of the recorded signal are needed. In this study we present an automated method for the detection of artefacts and quantification of the signal quality. The suggested methodology extracts descriptive features from the autocorrelation function and feeds these to a RUSBoost classifier. The posterior probability of the clean class is used to create a continuous signal quality assessment index. Firstly, the robustness of the proposed algorithm is investigated and secondly, the novel signal quality assessment index is evaluated. Data were used from three different studies: a Sleep study, the PhysioNet 2017 Challenge and a Stress study. Binary labels, clean or contaminated, were available from different annotators with experience in ECG analysis. Two types of realistic ECG noise from the MIT-BIH Noise Stress Test Database (NSTDB) were added to the Sleep study to test the quality index. Firstly, the model was trained on the Sleep dataset and subsequently tested on a subset of the other two datasets. Secondly, all recording conditions were taken into account by training the model on a subset derived from the three datasets. Lastly, the posterior probabilities of the model for the different levels of agreement between the annotators were compared. AUC values between 0.988 and 1.000 were obtained when training the model on the Sleep dataset. These results were further improved when training on the three datasets and thus taking all recording conditions into account. A Pearson correlation coefficient of 0.8131 was observed between the score of the clean class and the level of agreement. Additionally, significant quality decreases per noise level for both types of added noise were observed. The main novelty of this study is the new approach to ECG signal quality assessment based on the posterior clean class probability of the classifier. [ABSTRACT FROM AUTHOR]

A rapidly growing number of TV weathercasters are reporting on the local implications of climate change, although little is known about the effectiveness of such communication. To test the impact of localized climate reporting, we conducted an internet-based randomized controlled experiment in which local TV news viewers (n = 1,200) from two American cities (Chicago and Miami) watched either three localized climate reports or three standard weather reports featuring a prominent TV weathercaster from their city; each of the videos was between 1 and 2 min in duration. Participants' understanding of climate change as real, human-caused, and locally relevant was assessed with a battery of questions after watching the set of three videos. Compared to participants who watched weather reports, participants who watched climate reports became significantly more likely to 1) understand that climate change is happening, is human-caused, and is causing harm in their community; 2) feel that climate change is personally relevant and express greater concern about it; and 3) feel that they understand how climate change works and express greater interest in learning more about it. In short, our findings demonstrate that watching even a brief amount of localized climate reporting (less than 6 min) delivered by TV weathercasters helps viewers develop a more accurate understanding of global climate change as a locally and personally relevant problem, and offer strong support for this promising approach to promoting enhanced public understanding of climate change through public media. [ABSTRACT FROM AUTHOR]

Lightning phenomenon is the main cause of power systems faults. These faults may cause momentary or permanent service interruptions, thus system reliability is inherently interdependent with lightning phenomenon. A literature review will show that state-of-the-art solutions have yet to present mathematical explicit models for the interdependency of such two phenomena. In this context, this paper presents a multi-objective mixed integer linear programing (MILP) model for distribution systems reliability optimization while considering lightning phenomenon interdependency. The presented multi-objective MILP model aims the simultaneous minimization of SAIFI and MAIFI E reliability indices and associated costs. These goals are achieved by optimizing the lightning protection system design, which includes selection and allocation of different types of grounding systems along distribution feeders, while considering simultaneously network characteristics. Validation is done using real-life 81 bus 23 kV distribution network data. Test results highlight the efficiency of the presented model in improving system reliability while reducing associated costs. The ease of implementation of design, formulation of parameters and encouraging test results indicate potential for real-life application. The multi-objective MILP model is currently used by a distribution utility as a reliability-oriented tool. [ABSTRACT FROM AUTHOR]

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., Competing Interests: Competing interests The authors declare no competing or financial interests., (© 2023. Published by The Company of Biologists Ltd.)

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 β8 subunit. However, these studies have involved complete loss-of-function Itgb8 mutations, and have not discerned the relative roles for the β8 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 β8 integrin cytoplasmic domain is essential for inside-out modulation of adhesion, including activation of latent-TGFβs in the ECM. Quantitative sequencing of the brain endothelial cell transcriptome identifies TGFβ-regulated genes with putative links to blood vessel morphogenesis, including several genes linked to Wnt/β-catenin signaling. These results reveal that the β8 integrin cytoplasmic domain is essential for the regulation of TGFβ-dependent gene expression in endothelial cells and suggest that cross-talk between TGFβs and Wnt pathways is crucial for neurovascular development., Competing Interests: Competing interests The authors declare no competing or financial interests., (© 2022. Published by The Company of Biologists Ltd.)

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., Competing Interests: Authors RGW and WG were employed by the company Imec The Netherlands/Holst Centre. Authors CS and DR, were employed by the company Ziekenhuis Oost-Limburg. Authors SJ and VM were employed by the company Nederlandse Obesitas Kliniek. Author CVH was employed by the companies Imec OnePlanet, Imec. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Rozo, Moeyersons, Morales, Garcia van der Westen, Lijnen, Smeets, Jantzen, Monpellier, Ruttens, Van Hoof, Van Huffel, Groenendaal and Varon.)

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., (© 2021 Institute of Physics and Engineering in Medicine.)

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.

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Morales, Borzée, Testelmans, Buyse, Van Huffel and Varon.)

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 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., Competing Interests: Competing interestsThe authors declare no competing or financial interests., (© 2020. Published by The Company of Biologists Ltd.)

Cardiorespiratory monitoring is crucial for the diagnosis and management of multiple conditions such as stress and sleep disorders. Therefore, the development of ambulatory systems providing continuous, comfortable, and inexpensive means for monitoring represents an important research topic. Several techniques have been proposed in the literature to derive respiratory information from the ECG signal. Ten methods to compute single-lead ECG-derived respiration (EDR) were compared under multiple conditions, including different recording systems, baseline wander, normal and abnormal breathing patterns, changes in breathing rate, noise, and artifacts. Respiratory rates, wave morphology, and cardiorespiratory information were derived from the ECG and compared to those extracted from a reference respiratory signal. Three datasets were considered for analysis, involving a total 59 482 one-min, single-lead ECG segments recorded from 156 subjects. The results indicate that the methods based on QRS slopes outperform the other methods. This result is particularly interesting since simplicity is crucial for the development of ECG-based ambulatory systems.

The High Frequency (HF) band of the power spectrum of the Heart Rate Variability (HRV) is widely accepted to contain information related to the respiration. However, it is known that this often results in misleading estimations of the strength of the Respiratory Sinus Arrhythmia (RSA). In this paper, different approaches to characterize the change of the RSA with age, combining HRV and respiratory signals, are studied. These approaches are the bandwidths in the power spectral density estimations, bivariate phase rectified signal averaging, information dynamics, a time-frequency representation, and a heart rate decomposition based on subspace projections. They were applied to a dataset of sleep apnea patients, specifically to periods without apneas and during NREM sleep. Each estimate reflected a different relationship between RSA and age, suggesting that they all capture the cardiorespiratory information in a different way. The comparison of the estimates indicates that the approaches based on the extraction of respiratory information from HRV provide a better characterization of the age-dependent degradation of the RSA.

Peritoneal carcinomatosis (PC) is a malignant entity with a high rate of morbimortality. It is considered an end-stage common to several abdominal and pelvic malignant tumours, such as epithelial ovarian, fallopian tubal and peritoneal cancer. Although many of these tumors have a good response to chemotherapy, prognosis is poor due to the high rate of recurrence. Surgeons, gynecologists and oncologists are increasingly concerned with improving the survival. The surgical technique described by Sugarbaker in the eighties is a plausible option. It aims for a complete resection of macroscopic carcinomatosis (cytoreductive surgery) followed by intraoperative or perioperative intraperitoneal chemotherapy. This therapeutic option necessarily involves specific multidisciplinary units; histopathology of specimens from this surgical technique is now more frequent in our department. We describe our initial experience with PC originating from epithelial ovarian, tubal and peritoneal cancer treated with the modified Sugarbaker surgery employed in our hospital. We outline our protocol designed to achieve uniformity in procedure, and summarize the initial results., (Copyright © 2018 Sociedad Española de Anatomía Patológica. Publicado por Elsevier España, S.L.U. All rights reserved.)

Disruption of the blood-brain barrier (BBB) by cancer cells is linked to metastatic tumor initiation and progression; however, the pathways that drive these events remain poorly understood. Here, we have developed novel patient-derived xenograft (PDX) models of brain metastases that recapitulate pathological growth features found in original patient samples, thus allowing for analysis of BBB disruption by tumor cells. We report that the BBB is selectively disrupted in brain metastases, in part, via inhibition of the endothelial cell-expressed docosahexaenoic acid (DHA) transporter, major facilitator superfamily domain 2a (Mfsd2a). Loss of Mfsd2a expression in the tumor endothelium results in enhanced BBB leakage, but reduced DHA transport and altered lipid metabolism within metastases. Mfsd2a expression in normal cerebral endothelial cells is cooperatively regulated by TGFβ and bFGF signaling pathways, and these pathways are pathologically diminished in the brain metastasis endothelium. These results not only reveal a fundamental pathway underlying BBB disruption by metastatic cancer cells, but also suggest that restoring DHA metabolism in the brain tumor microenvironment may be a novel therapeutic strategy to block metastatic cell growth and survival.

BACKGROUND Primary malignant melanoma of the esophagus (PMME) is a rare cancer with a poor prognosis. It is often difficult to differentiate from non-epithelial malignant tumors, and immunohistochemical staining may be needed to diagnose the condition. The mainstay of treatment is usually surgical with curative or palliative intent, since radio- and chemotherapy do not really improve the outcome. The average survival rate after surgery is 34.5 months. At the time of diagnosis, 40-80% of cases have local regional lymph node metastases. CASE REPORT The case of a 67-year-old male patient with PMME is reported. He presented with progressive dysphagia. A computerized tomography was performed in which a polypoid mass was observed in the distal esophagus. It was originally suspected to be an adenocarcinoma, but was subsequently correctly diagnosed by immunohistochemical staining with HMB-45 antibody and by the presence of S-100 protein. A subtotal esophagectomy was performed. CONCLUSIONS Very few cases of PMME have been reported in the literature and there is only limited clinical experience with this disease. Therefore, it is very difficult to establish clear criteria for clinical recognition of this type of melanoma. Early histopathological confirmation of the character is essential for further treatment in case of confirmation of malignancy.

Mixed tumours of the parotid gland are rare in childhood and recurrence of this tumour is infrequent. Some authors report a higher rate of recurrence with some histological subtypes, like hypocellular variant. Female sex and young age at initial treatment are also risk factor for recurrence. Also the first surgical treatment, tumour enucleation or parotidectomy, has been implicated as a cause for recurrence. We present a case of a multicentric doubly recurrent parotid pleomorphic adenoma, 7 and 14 years after tumour enucleation, in a 9-year-old child. All the nodules resected showed the hypocellular variant of pleomorphic adenoma. We consider the relationships between the choice of treatment, the histologic subtypes of pleomorphic adenomas and multifocal tumours and recurrence. In this case, we believe the recurrence was related to surgical enucleation previously performed and the hypocellular histological subtype. We conclude that tumour enucleation is a risk factor for recurrence and total parotidectomy is the treatment of choice for pleomorphic parotid adenoma also in childhood.