Trombosis venosa profunda de repetición en miembros superiores

Deep vein thrombosis in the upper limbs is a rare disease. It can be idiopathic or secondary to neoplasms, devices (catheters, pacemakers), or thrombophilia. We describe an unusual case of recurrent deep vein thrombosis in the upper limbs and its association with sternoclavicular hyperostosis. Our studies confirmed a mediastinal mass with negative histopathological results for neoplasia but positive for fibrosis. After starting anticoagulation, the clinical condition was resolved, and we ruled out oncological, infectious, and thrombophilic diseases. Therefore, we assumed focal fibrous mediastinitis as the cause of recurrent thrombosis and clavicular hyperostosis syndrome.La trombosis venosa profunda en miembros superiores es una entidad poco frecuente, pudiendo ser idiopática o bien secundaria a neoplasias, dispositivos (catéteres, marcapasos) o trombofilia. Se describe el caso de una trombosis venosa profunda recidivante en miembros superiores y su asociación con una hiperostosis esternoclavicular. Durante su estudio se constató la presencia de una masa mediastínica con resultados histopatológicos sugestivos de fibrosis. El cuadro clínico se resolvió tras el inicio de la anticoagulación, descartando asimismo la asociación del evento trombótico con causas oncológicas, infecciosas o diátesis trombofílicas. Se asume finalmente el diagnóstico de mediastinítis fibrosa focal como causa de la trombosis recidivante y el síndrome de hiperostosis clavicular

Mild systemic inflammation enhances response to OnabotulinumtoxinA in chronic migraineurs

The anti-inflammatory effect of OnabotulinumtoxinA (OnabotA) has been a matter of discussion for many years. In chronic migraine, however, increased pro-inflammatory state is associated with good response to OnabotA. We aimed to investigate whether a mild systemic inflammatory state elicited by a common oral infection (periodontitis) could enhance treatment response to OnabotA. In this study, we included 61 chronic migraineurs otherwise healthy treated with OnabotA of which 7 were poor responders and 54 good responders. Before receiving OnabotA therapy, all participants underwent a full-mouth periodontal examination and blood samples were collected to determine serum levels of calcitonin gene-related peptide (CGRP), interleukin 6 (IL-6), IL-10 and high sensitivity C-reactive protein (hs-CRP). Periodontitis was present in 70.4% of responders and 28.6% of non-responders (P = 0.042). Responders showed greater levels of inflammation than non-responders (IL-6: 15.3 ± 8.7 vs. 9.2 ± 4.7 ng/mL, P = 0.016; CGRP: 18.8 ± 7.6 vs. 13.0 ± 3.1 pg/mL, P = 0.002; and hs-CRP: 3.9 ± 6.6 vs. 0.9 ± 0.8 mg/L, P = 0.003). A linear positive correlation was found between the amount of periodontal tissue inflamed in the oral cavity and markers of inflammation (IL-6: r = 0.270, P = 0.035; CGRP: r = 0.325, P = 0.011; and hs-CRP: r = 0.370, P = 0.003). This report shows that in presence of elevated systemic inflammatory markers related to periodontitis, OnabotA seems to reduce migraine attacks. The changes of scheduled inflammatory parameters after treatment and subsequent assessment during an adequate period still need to be doneThis study was partially supported by a grant from the Spanish Ministry of Economy and Competitiveness—Institute of Health Carlos III (PI15/01578). YL holds a Senior Clinical Research Fellowship supported by the UCL Biomedical Research Centre who receives funding from the NIHR (NIHR-INF-0387) and a research contract with Fundación Instituto de Investigación Sanitaria de Santiago de Compostela (fIDIS). TS (CPII17/00027) and FC (CP14/00154) are recipients of research contracts from Miguel Servet Program of Institute of Health Carlos IIIS

Random Forest-Based Prediction of Stroke Outcome

[Abstract] We research into the clinical, biochemical and neuroimaging factors associated with the outcome of stroke patients to generate a predictive model using machine learning techniques for prediction of mortality and morbidity 3-months after admission. The dataset consisted of patients with ischemic stroke (IS) and non-traumatic intracerebral hemorrhage (ICH) admitted to Stroke Unit of a European Tertiary Hospital prospectively registered. We identified the main variables for machine learning Random Forest (RF), generating a predictive model that can estimate patient mortality/morbidity according to the following groups: (1) IS + ICH, (2) IS, and (3) ICH. A total of 6022 patients were included: 4922 (mean age 71.9 ± 13.8 years) with IS and 1100 (mean age 73.3 ± 13.1 years) with ICH. NIHSS at 24, 48 h and axillary temperature at admission were the most important variables to consider for evolution of patients at 3-months. IS + ICH group was the most stable for mortality prediction [0.904 ± 0.025 of area under the receiver operating characteristics curve (AUC)]. IS group presented similar results, although variability between experiments was slightly higher (0.909 ± 0.032 of AUC). ICH group was the one in which RF had more problems to make adequate predictions (0.9837 vs. 0.7104 of AUC). There were no major differences between IS and IS + ICH groups according to morbidity prediction (0.738 and 0.755 of AUC) but, after checking normality with a Shapiro Wilk test with the null hypothesis that the data follow a normal distribution, it was rejected with W = 0.93546 (p-value < 2.2e−16). Conditions required for a parametric test do not hold, and we performed a paired Wilcoxon Test assuming the null hypothesis that all the groups have the same performance. The null hypothesis was rejected with a value < 2.2e−16, so there are statistical differences between IS and ICH groups. In conclusion, machine learning algorithms RF can be effectively used in stroke patients for long-term outcome prediction of mortality and morbidity.This study was partially supported by grants from the Spanish Ministry of Science and Innovation (SAF2017-84267-R), Xunta de Galicia (Axencia Galega de Innovación (GAIN): IN607A2018/3), Instituto de Salud Carlos III (ISCIII) (PI17/00540, PI17/01103), Spanish Research Network on Cerebrovascular Diseases RETICS-INVICTUS PLUS (RD16/0019) and by the European Union FEDER program. T. Sobrino (CPII17/00027), F. Campos (CPII19/00020) are recipients of research contracts from the Miguel Servet Program (Instituto de Salud Carlos III). General Directorate of Culture, Education and University Management of Xunta de Galicia (ED431G/01,252 ED431D 2017/16), “Galician Network for Colorectal Cancer Research" (Ref. ED431D 2017/23), Competitive Reference Groups (ED431C 2018/49), Spanish Ministry of Economy and Competitiveness via funding of the unique installation BIOCAI (UNLC08-1E-002, UNLC13-13–3503), European Regional Development Funds (FEDER).Xunta de Galicia; IN607A2018/3Xunta de Galicia; ED431G/01,252Xunta de Galicia; ED431D 2017/1

Temperature-Induced Changes in Reperfused Stroke: Inflammatory and Thrombolytic Biomarkers

Although hyperthermia is associated with poor outcomes in ischaemic stroke (IS), some studies indicate that high body temperature may benefit reperfusion therapies. We assessed the association of temperature with effective reperfusion (defined as a reduction of ≥8 points in the National Institute of Health Stroke Scale (NIHSS) within the first 24 h) and poor outcome (modified Rankin Scale (mRS) > 2) in 875 retrospectively-included IS patients. We also studied the influence of temperature on thrombolytic (cellular fibronectin (cFn); matrix metalloproteinase 9 (MMP-9)) and inflammatory biomarkers (tumour necrosis factor-alpha (TNF-α), interleukin 6 (IL-6)) and their relationship with effective reperfusion. Our results showed that a higher temperature at 24 but not 6 h after stroke was associated with failed reperfusion (OR: 0.373, p = 0.001), poor outcome (OR: 2.190, p = 0.005) and higher IL-6 levels (OR: 0.958, p 37.5 °C at 24 h, but not at 6 h after stroke, is correlated with reperfusion failure, poor clinical outcome, and infarct size. Mild hyperthermia (36.5–37.5 °C) in the first 6 h window might benefit drug reperfusion therapies by promoting clot lysisThis study was partially supported by grants from the Spanish Ministry of Science and Innovation (SAF2017-84267-R), Xunta de Galicia (Consellería Educación: IN607A2018/3), Instituto de Salud Carlos III (ISCIII) (PI17/00540 and PI17/01103), Spanish Research Network on Cerebrovascular Diseases RETICS-INVICTUS PLUS (RD16/0019), and by the European Union FEDER program. Furthermore, Tomás. Sobrino (CPII17/00027) and Francisco Campos (CPII19/00020) are recipients of research contracts from the Miguel Servet Program of Instituto de Salud Carlos III. María Pérez-Mato is a Sara Borrell Researcher (CD19/00033)S

Perfusion-weighted software written in Python for DSC-MRI analysis

IntroductionDynamic susceptibility-weighted contrast-enhanced (DSC) perfusion studies in magnetic resonance imaging (MRI) provide valuable data for studying vascular cerebral pathophysiology in different rodent models of brain diseases (stroke, tumor grading, and neurodegenerative models). The extraction of these hemodynamic parameters via DSC-MRI is based on tracer kinetic modeling, which can be solved using deconvolution-based methods, among others. Most of the post-processing software used in preclinical studies is home-built and custom-designed. Its use being, in most cases, limited to the institution responsible for the development. In this study, we designed a tool that performs the hemodynamic quantification process quickly and in a reliable way for research purposes.MethodsThe DSC-MRI quantification tool, developed as a Python project, performs the basic mathematical steps to generate the parametric maps: cerebral blood flow (CBF), cerebral blood volume (CBV), mean transit time (MTT), signal recovery (SR), and percentage signal recovery (PSR). For the validation process, a data set composed of MRI rat brain scans was evaluated: i) healthy animals, ii) temporal blood–brain barrier (BBB) dysfunction, iii) cerebral chronic hypoperfusion (CCH), iv) ischemic stroke, and v) glioblastoma multiforme (GBM) models. The resulting perfusion parameters were then compared with data retrieved from the literature.ResultsA total of 30 animals were evaluated with our DSC-MRI quantification tool. In all the models, the hemodynamic parameters reported from the literature are reproduced and they are in the same range as our results. The Bland–Altman plot used to describe the agreement between our perfusion quantitative analyses and literature data regarding healthy rats, stroke, and GBM models, determined that the agreement for CBV and MTT is higher than for CBF.ConclusionAn open-source, Python-based DSC post-processing software package that performs key quantitative perfusion parameters has been developed. Regarding the different animal models used, the results obtained are consistent and in good agreement with the physiological patterns and values reported in the literature. Our development has been built in a modular framework to allow code customization or the addition of alternative algorithms not yet implemented

Adenopatía supraclavicular como forma de presentación de un carcinoma de cérvix asociado al complejo esclerosis tuberosa con linfangioleiomiomatosis

La linfangioleiomiomatosis es una proliferación del tejido muscular broncovascular que recientemente se ha definido como una expresión incompleta de la entidad “complejo esclerosis tuberosa”, una facomatosis a la que se asocian diversas neoplasias. Presentamos un caso de carcinoma de cérvix con metástasis supraclaviculares y cervicales, asociado a linfangioleiomiomatosis en el contexto de un “complejo esclerosis tuberosa”