Perspectivas de innovación en gestión, educación ambiental para la adaptación y la mitigación

Esta publicación del libro-foro sobre ciudad y cambio climático responde al aporte de los diferentes profesionales de las entidades públicas y privadas que participaron en calidad de conferencistas, ponentes, panelistas y expositores y compartieron sus experiencias en la ciudad como una contribución al conocimiento de las comunidades acerca de la creciente importancia y consideración de la adaptación y mitigación. Se consideraron acciones de políticas públicas por parte de las administraciones públicas, los sectores económicos y la sociedad, grupos ecológicos y fundaciones ecológicas y de igual forma las acciones y grandes esfuerzos realizados por el Ministerio del Ambiente, el IDEAM, la CAR, la Secretaría de Ambiente, el Jardín Botánico, la Red RAUS y de los grupos de investigación de las universidades

MOESM4 of Brain metabolic pattern analysis using a magnetic resonance spectra classification software in experimental stroke

Additional file 4. Test set data used for the Brain Regions Classifier. Contains data from 56 processed spectral data vectors corresponding to non-infarcted parenchyma (n=16), SVZ (n=28) and infarcted parenchyma (n=12)

MOESM3 of Brain metabolic pattern analysis using a magnetic resonance spectra classification software in experimental stroke

Additional file 3. Training set data used for the Brain Regions Classifier. Contains data from 108 processed spectral data vectors corresponding to non-infarcted parenchyma (n=32), SVZ (n=54) and infarcted parenchyma (n=22)

MOESM1 of Brain metabolic pattern analysis using a magnetic resonance spectra classification software in experimental stroke

Additional file 1. Training set used for the infarct Evolution classifier. Contains data from 54 processed spectral data vectors corresponding to non-infarcted parenchyma (n=32), acute phase of infarct (n=13) and subacute phase of infarct spectra (n=9)

MOESM2 of Brain metabolic pattern analysis using a magnetic resonance spectra classification software in experimental stroke

Additional file 2. Test set data used for the Infarct Evolution Classifier. Contains data from 28 processed spectral data vectors corresponding to non-infarcted parenchyma (n=16), acute phase of infarct (n=7) and subacute phase of infarct spectra (n=5)

Brain metabolic pattern analysis using a magnetic resonance spectra classification software in experimental stroke

Ajuts: EU FEDER funds Redes Temáticas de Investigación Cooperativa Sanitaria RETICS-INVICTUS-RD12/014/0002Magnetic resonance spectroscopy (MRS) provides non-invasive information about the metabolic pattern of the brain parenchyma in vivo. The SpectraClassifier software performs MRS pattern-recognition by determining the spectral features (metabolites) which can be used objectively to classify spectra. Our aim was to develop an Infarct Evolution Classifier and a Brain Regions Classifier in a rat model of focal ischemic stroke using SpectraClassifier. A total of 164 single-voxel proton spectra obtained with a 7 Tesla magnet at an echo time of 12 ms from non-infarcted parenchyma, subventricular zones and infarcted parenchyma were analyzed with SpectraClassifier (). The spectra corresponded to Sprague-Dawley rats (healthy rats, n = 7) and stroke rats at day 1 post-stroke (acute phase, n = 6 rats) and at days 7 ± 1 post-stroke (subacute phase, n = 14). In the Infarct Evolution Classifier, spectral features contributed by lactate + mobile lipids (1.33 ppm), total creatine (3.05 ppm) and mobile lipids (0.85 ppm) distinguished among non-infarcted parenchyma (100% sensitivity and 100% specificity), acute phase of infarct (100% sensitivity and 95% specificity) and subacute phase of infarct (78% sensitivity and 100% specificity). In the Brain Regions Classifier, spectral features contributed by myoinositol (3.62 ppm) and total creatine (3.04/3.05 ppm) distinguished among infarcted parenchyma (100% sensitivity and 98% specificity), non-infarcted parenchyma (84% sensitivity and 84% specificity) and subventricular zones (76% sensitivity and 93% specificity). SpectraClassifier identified candidate biomarkers for infarct evolution (mobile lipids accumulation) and different brain regions (myoinositol content). The online version of this article (doi:10.1186/s12868-016-0328-x) contains supplementary material, which is available to authorized users

Assessment of plasma chitotriosidase activity, CCL18/PARC concentration and NP-C suspicion index in the diagnosis of Niemann-Pick disease type C : A prospective observational study

Niemann-Pick disease type C (NP-C) is a rare, autosomal recessive neurodegenerative disease caused by mutations in either the NPC1 or NPC2 genes. The diagnosis of NP-C remains challenging due to the non-specific, heterogeneous nature of signs/symptoms. This study assessed the utility of plasma chitotriosidase (ChT) and Chemokine (C-C motif) ligand 18 (CCL18)/pulmonary and activation-regulated chemokine (PARC) in conjunction with the NP-C suspicion index (NP-C SI) for guiding confirmatory laboratory testing in patients with suspected NP-C. In a prospective observational cohort study, incorporating a retrospective determination of NP-C SI scores, two different diagnostic approaches were applied in two separate groups of unrelated patients from 51 Spanish medical centers (n = 118 in both groups). From Jan 2010 to Apr 2012 (Period 1), patients with ≥2 clinical signs/symptoms of NP-C were considered 'suspected NP-C' cases, and NPC1/NPC2 sequencing, plasma chitotriosidase (ChT), CCL18/PARC and sphingomyelinase levels were assessed. Based on findings in Period 1, plasma ChT and CCL18/PARC, and NP-C SI prediction scores were determined in a second group of patients between May 2012 and Apr 2014 (Period 2), and NPC1 and NPC2 were sequenced only in those with elevated ChT and/or elevated CCL18/PARC and/or NP-C SI ≥70. Filipin staining and 7-ketocholesterol (7-KC) measurements were performed in all patients with NP-C gene mutations, where possible. In total across Periods 1 and 2, 10/236 (4%) patients had a confirmed diagnosis o NP-C based on gene sequencing (5/118 [4.2%] in each Period): all of these patients had two causal NPC1 mutations. Single mutant NPC1 alleles were detected in 8/236 (3%) patients, overall. Positive filipin staining results comprised three classical and five variant biochemical phenotypes. No NPC2 mutations were detected. All patients with NPC1 mutations had high ChT activity, high CCL18/PARC concentrations and/or NP-C SI scores ≥70. Plasma 7-KC was higher than control cut-off values in all patients with two NPC1 mutations, and in the majority of patients with single mutations. Family studies identified three further NP-C patients. This approach may be very useful for laboratories that do not have mass spectrometry facilities and therefore, they cannot use other NP-C biomarkers for diagnosis