A bicyclic α-iminophosphonate improves cognitive decline in 5xFAD murine model of neurodegeneration

I2 receptors (I2-IR) are widely distributed in the central nervous system. I2-IR ligands are associated with a neuroprotective effect but, as I2-IR structure remains unknown, the discovery of better and more selective ligands is necessary to understand the pharmacological and molecular implications of I2-IR. Recently, we described a new imidazoline-structure family which showed high affinity and selectivity for I2-IR. In vivo studies in mice indicated a neuroprotective role and revealed beneficial effects in behaviour and cognition with a murine model of neurodegeneration, senescence-accelerated prone mouse (SAMP8). Herein, we report a novel non-imidazoline-structure of bicyclic α-iminophosphonates family with high affinities for I2-IR. In vivo studies in 5X-FAD mice (a transgenic representative model of AD) and SAMP8 mice (a model of neurodegeneration linked to aging) showed an improvement in behaviour and cognition, a reduction of AD hallmarks and of neuroinflammation markers for the mice treated with the lead compound B06. After evaluating several pathways associated with neurodegeneration, we demonstrated that CaN pathway plays a critical role on the neuroprotective effects of I2-IR ligands on SAMP8 mice model. To rule out warnings of the novel family, we calculated DMPK and physicochemical properties for the novel bicyclic α-iminophosphonates. As well, we carried out drug metabolism, safety studies and in vivo pharmacokinetics for lead compound B06. In summary, we present a novel family of I2-IR ligands, its effectiveness in in vivo models and the possible neuroprotective molecular mechanism mediated by them. This highlights that the modulation of I2-IR by bicyclic α-iminophosphonates may open a new therapeutic venue for unmet neurodegenerative conditions

Bicyclic alfa-iminophosphonates as high affinity imidazoline I2 receptor ligands for Alzheimer's disease

Imidazoline I2 receptors (I2-IR), widely distributed in the CNS and altered in patients that suffered from neurodegenerative disorders, are orphan from the structural point of view and new I2-IR ligands are urgently required for improving their pharmacological characterization. We report the synthesis and 3D-QSAR studies of a new family of bicyclic α-iminophosphonates endowed with relevant affinities for human brain I2-IR. Acute treatment in mice with a selected compound significantly decreased the FADD protein in the hippocampus, a key marker in neuroprotective actions. Additionally, in vivo studies in the familial Alzheimer's disease 5xFAD murine model revealed beneficial effects in behavior and cognition. These results are supported by changes in molecular pathways related to cognitive decline and Alzheimer's disease. Therefore bicyclic α-iminophosphonates are tools that may open new therapeutic avenues for I2-IR, particularly for unmet neurodegenerative conditions

Fusing actigraphy signals for outpatient monitoring

[EN] Actigraphy devices have been successfully used as effective tools in the treatment of diseases such as sleep disorders or major depression. Although several efforts have been made in recent years to develop smaller and more portable devices, the features necessary for the continuous monitoring of outpatients require a less intrusive, obstructive and stigmatizing acquisition system. A useful strategy to overcome these limitations is based on adapting the monitoring system to the patient lifestyle and behavior by providing sets of different sensors that can be worn simultaneously or alternatively. This strategy offers to the patient the option of using one device or other according to his/her particular preferences. However this strategy requires a robust multi-sensor fusion methodology capable of taking maximum profit from all of the recorded information. With this aim, this study proposes two actigraphy fusion models including centralized and distributed architectures based on artificial neural networks. These novel fusion methods were tested both on synthetic datasets and real datasets, providing a parametric characterization of the models' behavior, and yielding results based on real case applications. The results obtained using both proposed fusion models exhibit good performance in terms of robustness to signal degradation, as well as a good behavior in terms of the dependence of signal quality on the number of signals fused. The distributed and centralized fusion methods reduce the mean averaged error of the original signals to 44% and 46% respectively when using simulated datasets. The proposed methods may therefore facilitate a less intrusive and more dependable way of acquiring valuable monitoring information from outpatients.This work was partially funded by the European Commission: Help4Mood (Contract No. FP7-ICT-2009-4: 248765). E. FusterGarcia acknowledges Programa Torres Quevedo from Ministerio de Educacion y Ciencia, co-founded by the European Social Fund (PTQ-12-05693).Fuster García, E.; Bresó Guardado, A.; Martínez Miranda, JC.; Rosell-Ferrer, J.; Matheson, C.; García Gómez, JM. (2015). Fusing actigraphy signals for outpatient monitoring. Information Fusion. 23:69-80. https://doi.org/10.1016/j.inffus.2014.08.003S69802

Multiproject–multicenter evaluation of automatic brain tumor classification by magnetic resonance spectroscopy

Contains fulltext : 75361.pdf (publisher's version ) (Open Access)14 p

Automated Glioblastoma Segmentation Based on a Multiparametric Structured Unsupervised Classification

Automatic brain tumour segmentation has become a key component for the future of brain tumour treatment. Currently, most of brain tumour segmentation approaches arise from the supervised learning standpoint, which requires a labelled training dataset from which to infer the models of the classes. The performance of these models is directly determined by the size and quality of the training corpus, whose retrieval becomes a tedious and time-consuming task. On the other hand, unsupervised approaches avoid these limitations but often do not reach comparable results than the supervised methods. In this sense, we propose an automated unsupervised method for brain tumour segmentation based on anatomical Magnetic Resonance (MR) images. Four unsupervised classification algorithms, grouped by their structured or non-structured condition, were evaluated within our pipeline. Considering the non-structured algorithms, we evaluated K-means, Fuzzy K-means and Gaussian Mixture Model (GMM), whereas as structured classification algorithms we evaluated Gaussian Hidden Markov Random Field (GHMRF). An automated postprocess based on a statistical approach supported by tissue probability maps is proposed to automatically identify the tumour classes after the segmentations. We evaluated our brain tumour segmentation method with the public BRAin Tumor Segmentation (BRATS) 2013 Test and Leaderboard datasets. Our approach based on the GMM model improves the results obtained by most of the supervised methods evaluated with the Leaderboard set and reaches the second position in the ranking. Our variant based on the GHMRF achieves the first position in the Test ranking of the unsupervised approaches and the seventh position in the general Test ranking, which confirms the method as a viable alternative for brain tumour segmentation.EFG was supported by Programa Torres Quevedo, Ministerio de Educacion y Ciencia, co-funded by the European Social Fund (PTQ-1205693). EFG, JMGG, and JVM were supported by Red Tematica de Investigacion Cooperativa en Cancer, (RTICC) 2013-2016 (RD12/0036/0020). JMGG was supported by Project TIN2013-43457-R: Caracterizacion de firmas biologicas de glioblastomas mediante modelos no-supervisados de prediccion estructurada basados en biomarcadores de imagen, co-funded by the Ministerio de Economia y Competitividad of Spain; CON2014001 UPV-IISLaFe: Unsupervised glioblastoma tumor components segmentation based on perfusion multiparametric MRI and spatio/temporal constraints; and CON2014002 UPV-IISLaFe: Empleo de segmentacion no supervisada multiparametrica basada en perfusion RM para la caracterizacion del edema peritumoral de gliomas y metastasis cerebrales unicas, funded by Instituto de Investigacion Sanitaria H. Universitario y Politecnico La Fe. This work was partially supported by the Instituto de Aplicaciones de las Tecnologias de la Informacion y las Comunicaciones Avanzadas (ITACA). Veratech for Health S.L. provided support in the form of salaries for author EF-G, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of this author is articulated in the "author contributions" section. This does not alter the authors' adherence to PLOS ONE policies on sharing data and materials.Juan Albarracín, J.; Fuster García, E.; Manjón Herrera, JV.; Robles Viejo, M.; Aparici, F.; Marti-Bonmati, L.; García Gómez, JM. (2015). Automated Glioblastoma Segmentation Based on a Multiparametric Structured Unsupervised Classification. PLoS ONE. 10(5):1-20. https://doi.org/10.1371/journal.pone.0125143S120105Wen, P. Y., Macdonald, D. R., Reardon, D. A., Cloughesy, T. F., Sorensen, A. G., Galanis, E., … Chang, S. M. (2010). Updated Response Assessment Criteria for High-Grade Gliomas: Response Assessment in Neuro-Oncology Working Group. 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Fusing actigraphy signals for outpatient monitoring

Actigraphy devices have been successfully used as effective tools in the treatment of diseases such as sleep disorders or major depression. Although several efforts have been made in recent years to develop smaller and more portable devices, the features necessary for the continuous monitoring of outpatients require a less intrusive, obstructive and stigmatizing acquisition system. A useful strategy to overcome these limitations is based on adapting the monitoring system to the patient lifestyle and behavior by providing sets of different sensors that can be worn simultaneously or alternatively. This strategy offers to the patient the option of using one device or other according to his/her particular preferences. However this strategy requires a robust multi-sensor fusion methodology capable of taking maximum profit from all of the recorded information. With this aim, this study proposes two actigraphy fusion models including centralized and distributed architectures based on artificial neural networks. These novel fusion methods were tested both on synthetic datasets and real datasets, providing a parametric characterization of the models' behavior, and yielding results based on real case applications. The results obtained using both proposed fusion models exhibit good performance in terms of robustness to signal degradation, as well as a good behavior in terms of the dependence of signal quality on the number of signals fused. The distributed and centralized fusion methods reduce the mean averaged error of the original signals to 44% and 46% respectively when using simulated datasets. The proposed methods may therefore facilitate a less intrusive and more dependable way of acquiring valuable monitoring information from outpatients.Peer Reviewe

The prognostic relevance of a gene expression signature in MRI-defined highly vascularized glioblastoma

Background: The vascular heterogeneity of glioblastomas (GB) remains an important area of research, since tumor progression and patient prognosis are closely tied to this feature. With this study, we aim to identify gene expression profiles associated with MRI-defined tumor vascularity and to investigate its relationship with patient prognosis. Methods: The study employed MRI parameters calculated with DSC Perfusion Quantification of ONCOhabitats glioma analysis software and RNA-seq data from the TCGA-GBM project dataset. In our study, we had a total of 147 RNA-seq samples, which 15 of them also had MRI parameter information. We analyzed the gene expression profiles associated with MRI-defined tumor vascularity using differential gene expression analysis and performed Log-rank tests to assess the correlation between the identified genes and patient prognosis. Results: The findings of our research reveal a set of 21 overexpressed genes associated with the high vascularity pattern. Notably, several of these overexpressed genes have been previously implicated in worse prognosis based on existing literature. Our log-rank test further validates that the collective upregulation of these genes is indeed correlated with an unfavorable prognosis. This set of genes includes a variety of molecules, such as cytokines, receptors, ligands, and other molecules with diverse functions. Conclusions: Our findings suggest that the set of 21 overexpressed genes in the High Vascularity group could potentially serve as prognostic markers for GB patients. These results highlight the importance of further investigating the relationship between the molecules such as cytokines or receptors underlying the vascularity in GB and its observation through MRI and developing targeted therapies for this aggressive disease

Automatic tumour class isolation process.

<p>Automatic tumour class isolation process.</p

Example of feature extraction and dimensionality reduction from a patient of the BRATS 2013 dataset.

<p>Example of feature extraction and dimensionality reduction from a patient of the BRATS 2013 dataset.</p