32 research outputs found

    The APC/C cofactor Cdh1 prevents replicative stress and p53-dependent cell death in neural progenitors

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    The E3-ubiquitin ligase APC/C-Cdh1 is essential for endoreduplication but its relevance in the mammalian mitotic cell cycle is still unclear. Here we show that genetic ablation of Cdh1 in the developing nervous system results in hypoplastic brain and hydrocephalus. These defects correlate with enhanced levels of Cdh1 substrates and increased entry into the S phase in neural progenitors. However, cell division is prevented in the absence of Cdh1 due to hyperactivation of cyclin-dependent kinases, replicative stress, induction of p53, G2 arrest and apoptotic death of these progenitor cells. Concomitant ablation of p53 rescues apoptosis but not replicative stress, resulting in the presence of damaged neurons throughout the adult brain. These data indicate that the inactivation of Cdh1 in vivo results in replicative stress, cell cycle arrest and cell death, supporting recent therapeutic proposals aimed to inhibit the APC/C in tumours.M.E. was supported by the Spanish Ministry of Economy and Competitiveness (MINECO). This work was funded by grants from the Foundation Ramón Areces and MINECO SAF2012-38215 to M.M.). The Cell Division and Cancer Group of the CNIO are supported by the OncoCycle Programme (S2010/BMD-2470) from the Comunidad de Madrid, the OncoBIO Consolider-Ingenio 2010 Programme (MINECO, CSD2007-00017) and the European Union Seventh Framework Programme (MitoSys project; HEALTH-F5-2010-241548).Peer Reviewe

    Shortage of dNTPs underlies altered replication dynamics and DNA breakage in the absence of the APC/C cofactor Cdh1

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    This research was funded by grants from the Spanish Ministry of Economy and Competitiveness MINECO (CSD2007-0015, BFU2011-28274 and BFU2014-55439) and Junta de Castilla y León (CSI151U13 and CSI084U16), the Swedish Cancer Society, the Knut and Alice Wallenberg Foundation and the Swedish Research Council (A.C.). I.G.H is supported by Fundación Científica de la Asociación Española contra el Cáncer (AECC). J.G. and R.R were recipients of CSIC JAE and FPU predoctoral fellowships (MINECO)Peer reviewedPostprin

    Targeting Mitotic Exit Leads to Tumor Regression In Vivo: Modulation by Cdk1, Mastl, and the PP2A/B55α,δ Phosphatase

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    SummaryTargeting mitotic exit has been recently proposed as a relevant therapeutic approach against cancer. By using genetically engineered mice, we show that the APC/C cofactor Cdc20 is essential for anaphase onset in vivo in embryonic or adult cells, including progenitor/stem cells. Ablation of Cdc20 results in efficient regression of aggressive tumors, whereas current mitotic drugs display limited effects. Yet, Cdc20 null cells can exit from mitosis upon inactivation of Cdk1 and the kinase Mastl (Greatwall). This mitotic exit depends on the activity of PP2A phosphatase complexes containing B55α or B55δ regulatory subunits. These data illustrate the relevance of critical players of mitotic exit in mammals and their implications in the balance between cell death and mitotic exit in tumor cells

    Reduced chromosome cohesion measured by interkinetochore distance is associated with aneuploidy even in oocytes from young mice

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    It is becoming clear that reduced chromosome cohesion is an important factor in the rise of maternal age-related aneuploidy. This reduction in cohesion has been observed both in human and mouse oocytes, and it can be measured directly by an increase with respect to maternal age in interkinetochore (iKT) distance between a sister chromatid pair. We have observed variations in iKT distance even in oocytes from young mice and wondered if such differences may predispose those oocytes displaying the greatest iKT distances to be becoming aneuploid. Therefore, we used two methods, one pharmacological (Aurora kinase inhibitor) and one genetic (Fzr1 knockout), to raise aneuploidy rates in oocytes from young mice (age, 1-3 mo) and to examine if those oocytes that were aneuploid had greater iKT distances. We observed that for both Aurora kinase inhibition and Fzr1 knockout, iKT distances were significantly greater in those oocytes that became aneuploid compared to those that remained euploid. Based on these results, we propose that individual oocytes undergo loss in chromosomal cohesion at different rates and that the greater this loss, the greater the risk for becoming aneuploid.Supported by an NHMRC project grant (569202) to K.T.J., S.M., and E.A.M. J.E.H. is supported by an Australian Research Council DECRA Fellowship. I.G.-H. and S.M. are supported by grants BFU2007-67464, BFU2008-01808, Consolider CSD2007-00015, and Junta de Castilla y León Grupo de Excelencia GR 265.Peer Reviewe

    Application of Supervised Machine Learning for Behavioral Biomarkers of Autism Spectrum Disorder Based on Electrodermal Activity and Virtual Reality

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    [EN] Objective: Sensory processing is the ability to capture, elaborate, and integrate information through the five senses and is impaired in over 90% of children with autism spectrum disorder (ASD). The ASD population shows hyper¿hypo sensitiveness to sensory stimuli that can generate alteration in information processing, affecting cognitive and social responses to daily life situations. Structured and semi-structured interviews are generally used for ASD assessment, and the evaluation relies on the examiner¿s subjectivity and expertise, which can lead to misleading outcomes. Recently, there has been a growing need for more objective, reliable, and valid diagnostic measures, such as biomarkers, to distinguish typical from atypical functioning and to reliably track the progression of the illness, helping to diagnose ASD. Implicit measures and ecological valid settings have been showing high accuracy on predicting outcomes and correctly classifying populations in categories. Methods: Two experiments investigated whether sensory processing can discriminate between ASD and typical development (TD) populations using electrodermal activity (EDA) in two multimodal virtual environments (VE): forest VE and city VE. In the first experiment, 24 children with ASD diagnosis and 30 TDs participated in both virtual experiences, and changes in EDA have been recorded before and during the presentation of visual, auditive, and olfactive stimuli. In the second experiment, 40 children have been added to test the model of experiment 1. Results: The first exploratory results on EDA comparison models showed that the integration of visual, auditive, and olfactive stimuli in the forest environment provided higher accuracy (90.3%) on sensory dysfunction discrimination than specific stimuli. In the second experiment, 92 subjects experienced the forest VE, and results on 72 subjects showed that stimuli integration achieved an accuracy of 83.33%. The final confirmatory test set (n = 20) achieved 85% accuracy, simulating a real application of the models. Further relevant result concerns the visual stimuli condition in the first experiment, which achieved 84.6% of accuracy in recognizing ASD sensory dysfunction. Conclusion: According to our studies¿ results, implicit measures, such as EDA, and ecological valid settings can represent valid quantitative methods, along with traditional assessment measures, to classify ASD population, enhancing knowledge on the development of relevant specific treatments.This work was supported by the Spanish Ministry of Economy, Industry, and Competitiveness-funded project Immersive Virtual Environment for the Evaluation and Training of Children with Autism Spectrum Disorder: T Room (IDI-20170912) and by the Generalitat Valenciana-funded project REBRAND (PROMETEU/2019/105).Alcañiz Raya, ML.; Chicchi-Giglioli, IA.; Marín-Morales, J.; Higuera-Trujillo, JL.; Olmos-Raya, E.; Minissi, ME.; Teruel García, G.... (2020). Application of Supervised Machine Learning for Behavioral Biomarkers of Autism Spectrum Disorder Based on Electrodermal Activity and Virtual Reality. Frontiers in Human Neuroscience. 14:1-16. https://doi.org/10.3389/fnhum.2020.00090S11614Allen, R., Davis, R., & Hill, E. (2012). The Effects of Autism and Alexithymia on Physiological and Verbal Responsiveness to Music. Journal of Autism and Developmental Disorders, 43(2), 432-444. doi:10.1007/s10803-012-1587-8Anagnostou, E., Zwaigenbaum, L., Szatmari, P., Fombonne, E., Fernandez, B. A., Woodbury-Smith, M., … Scherer, S. W. (2014). Autism spectrum disorder: advances in evidence-based practice. Canadian Medical Association Journal, 186(7), 509-519. doi:10.1503/cmaj.121756Ashwin, C., Chapman, E., Howells, J., Rhydderch, D., Walker, I., & Baron-Cohen, S. (2014). Enhanced olfactory sensitivity in autism spectrum conditions. Molecular Autism, 5(1), 53. doi:10.1186/2040-2392-5-53Baron-Cohen, S. (1990). Autism: A Specific Cognitive Disorder of & lsquo;Mind-Blindness’. International Review of Psychiatry, 2(1), 81-90. doi:10.3109/09540269009028274Baron-Cohen, S., Ashwin, E., Ashwin, C., Tavassoli, T., & Chakrabarti, B. (2009). Talent in autism: hyper-systemizing, hyper-attention to detail and sensory hypersensitivity. Philosophical Transactions of the Royal Society B: Biological Sciences, 364(1522), 1377-1383. doi:10.1098/rstb.2008.0337Barry, R. J., & James, A. L. (1988). Coding of stimulus parameters in autistic, retarded, and normal children: evidence for a two-factor theory of autism. International Journal of Psychophysiology, 6(2), 139-149. doi:10.1016/0167-8760(88)90045-1Bekele, E., Crittendon, J., Zheng, Z., Swanson, A., Weitlauf, A., Warren, Z., & Sarkar, N. (2014). Assessing the Utility of a Virtual Environment for Enhancing Facial Affect Recognition in Adolescents with Autism. Journal of Autism and Developmental Disorders, 44(7), 1641-1650. doi:10.1007/s10803-014-2035-8Benedek, M., & Kaernbach, C. (2010). A continuous measure of phasic electrodermal activity. Journal of Neuroscience Methods, 190(1), 80-91. doi:10.1016/j.jneumeth.2010.04.028Blascovich, J., Loomis, J., Beall, A. C., Swinth, K. R., Hoyt, C. L., & Bailenson, J. N. (2002). TARGET ARTICLE: Immersive Virtual Environment Technology as a Methodological Tool for Social Psychology. Psychological Inquiry, 13(2), 103-124. doi:10.1207/s15327965pli1302_01Boucsein, W. (2012). Electrodermal Activity. doi:10.1007/978-1-4614-1126-0Brunswik, E. (1955). Representative design and probabilistic theory in a functional psychology. Psychological Review, 62(3), 193-217. doi:10.1037/h0047470BUJNAKOVA, I., ONDREJKA, I., MESTANIK, M., VISNOVCOVA, Z., MESTANIKOVA, A., HRTANEK, I., … TONHAJZEROVA, I. (2016). Autism Spectrum Disorder Is Associated With Autonomic Underarousal. Physiological Research, S673-S682. doi:10.33549/physiolres.933528Chang, C.-C., & Lin, C.-J. (2011). LIBSVM. ACM Transactions on Intelligent Systems and Technology, 2(3), 1-27. doi:10.1145/1961189.1961199Chang, M. C., Parham, L. D., Blanche, E. I., Schell, A., Chou, C.-P., Dawson, M., & Clark, F. (2012). Autonomic and Behavioral Responses of Children With Autism to Auditory Stimuli. American Journal of Occupational Therapy, 66(5), 567-576. doi:10.5014/ajot.2012.004242CHAYTOR, N., SCHMITTEREDGECOMBE, M., & BURR, R. (2006). Improving the ecological validity of executive functioning assessment. Archives of Clinical Neuropsychology, 21(3), 217-227. doi:10.1016/j.acn.2005.12.002Chen, C. P., Keown, C. L., Jahedi, A., Nair, A., Pflieger, M. E., Bailey, B. A., & Müller, R.-A. (2015). Diagnostic classification of intrinsic functional connectivity highlights somatosensory, default mode, and visual regions in autism. NeuroImage: Clinical, 8, 238-245. doi:10.1016/j.nicl.2015.04.002Chita-Tegmark, M. (2016). Attention Allocation in ASD: a Review and Meta-analysis of Eye-Tracking Studies. Review Journal of Autism and Developmental Disorders, 3(3), 209-223. doi:10.1007/s40489-016-0077-xFenwick, T. (2014). Social Media and Medical Professionalism. Academic Medicine, 89(10), 1331-1334. doi:10.1097/acm.0000000000000436Delobel-Ayoub, M., Ehlinger, V., Klapouszczak, D., Maffre, T., Raynaud, J.-P., Delpierre, C., & Arnaud, C. (2015). Socioeconomic Disparities and Prevalence of Autism Spectrum Disorders and Intellectual Disability. PLOS ONE, 10(11), e0141964. doi:10.1371/journal.pone.0141964Di Martino, A., Yan, C.-G., Li, Q., Denio, E., Castellanos, F. X., Alaerts, K., … Milham, M. P. (2013). The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular Psychiatry, 19(6), 659-667. doi:10.1038/mp.2013.78Dudova, I., Vodicka, J., Havlovicova, M., Sedlacek, Z., Urbanek, T., & Hrdlicka, M. (2011). Odor detection threshold, but not odor identification, is impaired in children with autism. European Child & Adolescent Psychiatry, 20(7), 333-340. doi:10.1007/s00787-011-0177-1Fagius, J., & Wallin, B. G. (1980). Sympathetic reflex latencies and conduction velocities in normal man. Journal of the Neurological Sciences, 47(3), 433-448. doi:10.1016/0022-510x(80)90098-2Fenning, R. M., Baker, J. K., Baucom, B. R., Erath, S. A., Howland, M. A., & Moffitt, J. (2017). Electrodermal Variability and Symptom Severity in Children with Autism Spectrum Disorder. Journal of Autism and Developmental Disorders, 47(4), 1062-1072. doi:10.1007/s10803-016-3021-0Forscher, P. S., Lai, C. K., Axt, J. R., Ebersole, C. R., Herman, M., Devine, P. G., & Nosek, B. A. (2019). A meta-analysis of procedures to change implicit measures. Journal of Personality and Social Psychology, 117(3), 522-559. doi:10.1037/pspa0000160Francis, K. (2007). Autism interventions: a critical update. Developmental Medicine & Child Neurology, 47(7), 493-499. doi:10.1111/j.1469-8749.2005.tb01178.xFriston, K. J., Stephan, K. E., Montague, R., & Dolan, R. J. (2014). Computational psychiatry: the brain as a phantastic organ. The Lancet Psychiatry, 1(2), 148-158. doi:10.1016/s2215-0366(14)70275-5Gillberg, C., & Rasmussen, P. (1994). Brief report: Four case histories and a literature review of williams syndrome and autistic behavior. Journal of Autism and Developmental Disorders, 24(3), 381-393. doi:10.1007/bf02172235Hirstein, W., Iversen, P., & Ramachandran, V. S. (2001). Autonomic responses of autistic children to people and objects. Proceedings of the Royal Society of London. Series B: Biological Sciences, 268(1479), 1883-1888. doi:10.1098/rspb.2001.1724Hubert, B. E., Wicker, B., Monfardini, E., & Deruelle, C. (2009). Electrodermal reactivity to emotion processing in adults with autistic spectrum disorders. Autism, 13(1), 9-19. doi:10.1177/1362361308091649Hyde, K. K., Novack, M. N., LaHaye, N., Parlett-Pelleriti, C., Anden, R., Dixon, D. R., & Linstead, E. (2019). Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: a Review. Review Journal of Autism and Developmental Disorders, 6(2), 128-146. doi:10.1007/s40489-019-00158-xJOSEPH, R. M., EHRMAN, K., MCNALLY, R., & KEEHN, B. (2008). Affective response to eye contact and face recognition ability in children with ASD. Journal of the International Neuropsychological Society, 14(6), 947-955. doi:10.1017/s1355617708081344Kandalaft, M. R., Didehbani, N., Krawczyk, D. C., Allen, T. T., & Chapman, S. B. (2012). Virtual Reality Social Cognition Training for Young Adults with High-Functioning Autism. Journal of Autism and Developmental Disorders, 43(1), 34-44. doi:10.1007/s10803-012-1544-6Kreibig, S. D. (2010). Autonomic nervous system activity in emotion: A review. Biological Psychology, 84(3), 394-421. doi:10.1016/j.biopsycho.2010.03.010Kylliäinen, A., & Hietanen, J. K. (2006). Skin Conductance Responses to Another Person’s Gaze in Children with Autism. Journal of Autism and Developmental Disorders, 36(4), 517-525. doi:10.1007/s10803-006-0091-4Kylliäinen, A., Wallace, S., Coutanche, M. N., Leppänen, J. M., Cusack, J., Bailey, A. J., & Hietanen, J. K. (2012). Affective-motivational brain responses to direct gaze in children with autism spectrum disorder. Journal of Child Psychology and Psychiatry, 53(7), 790-797. doi:10.1111/j.1469-7610.2011.02522.xLedoux, K., Coderre, E., Bosley, L., Buz, E., Gangopadhyay, I., & Gordon, B. (2015). The concurrent use of three implicit measures (eye movements, pupillometry, and event-related potentials) to assess receptive vocabulary knowledge in normal adults. Behavior Research Methods, 48(1), 285-305. doi:10.3758/s13428-015-0571-6Leekam, S. R., Nieto, C., Libby, S. J., Wing, L., & Gould, J. (2006). Describing the Sensory Abnormalities of Children and Adults with Autism. Journal of Autism and Developmental Disorders, 37(5), 894-910. doi:10.1007/s10803-006-0218-7Levy, A., & Perry, A. (2011). Outcomes in adolescents and adults with autism: A review of the literature. Research in Autism Spectrum Disorders, 5(4), 1271-1282. doi:10.1016/j.rasd.2011.01.023Li, B., Sharma, A., Meng, J., Purushwalkam, S., & Gowen, E. (2017). Applying machine learning to identify autistic adults using imitation: An exploratory study. PLOS ONE, 12(8), e0182652. doi:10.1371/journal.pone.0182652Liu, W., Li, M., & Yi, L. (2016). Identifying children with autism spectrum disorder based on their face processing abnormality: A machine learning framework. Autism Research, 9(8), 888-898. doi:10.1002/aur.1615Lord, C., Risi, S., DiLavore, P. S., Shulman, C., Thurm, A., & Pickles, A. (2006). Autism From 2 to 9 Years of Age. Archives of General Psychiatry, 63(6), 694. doi:10.1001/archpsyc.63.6.694Lord, C., Rutter, M., & Le Couteur, A. (1994). Autism Diagnostic Interview-Revised: A revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. Journal of Autism and Developmental Disorders, 24(5), 659-685. doi:10.1007/bf02172145Loth, E., Spooren, W., Ham, L. M., Isaac, M. B., Auriche-Benichou, C., Banaschewski, T., … Murphy, D. G. M. (2015). Identification and validation of biomarkers for autism spectrum disorders. Nature Reviews Drug Discovery, 15(1), 70-70. doi:10.1038/nrd.2015.7Louwerse, A., van der Geest, J. N., Tulen, J. H. M., van der Ende, J., Van Gool, A. R., Verhulst, F. C., & Greaves-Lord, K. (2013). Effects of eye gaze directions of facial images on looking behaviour and autonomic responses in adolescents with autism spectrum disorders. Research in Autism Spectrum Disorders, 7(9), 1043-1053. doi:10.1016/j.rasd.2013.04.013Lydon, S., Healy, O., Reed, P., Mulhern, T., Hughes, B. M., & Goodwin, M. S. (2014). A systematic review of physiological reactivity to stimuli in autism. Developmental Neurorehabilitation, 19(6), 335-355. doi:10.3109/17518423.2014.971975McCarthy, C., Pradhan, N., Redpath, C., & Adler, A. (2016). Validation of the Empatica E4 wristband. 2016 IEEE EMBS International Student Conference (ISC). doi:10.1109/embsisc.2016.7508621McCormick, C., Hessl, D., Macari, S. L., Ozonoff, S., Green, C., & Rogers, S. J. (2014). Electrodermal and Behavioral Responses of Children With Autism Spectrum Disorders to Sensory and Repetitive Stimuli. Autism Research, 7(4), 468-480. doi:10.1002/aur.1382Miller, L. J., Anzalone, M. E., Lane, S. J., Cermak, S. A., & Osten, E. T. (2007). Concept Evolution in Sensory Integration: A Proposed Nosology for Diagnosis. American Journal of Occupational Therapy, 61(2), 135-140. doi:10.5014/ajot.61.2.135Montague, P. R., Dolan, R. J., Friston, K. J., & Dayan, P. (2012). Computational psychiatry. Trends in Cognitive Sciences, 16(1), 72-80. doi:10.1016/j.tics.2011.11.018Möricke, E., Buitelaar, J. K., & Rommelse, N. N. J. (2015). Do We Need Multiple Informants When Assessing Autistic Traits? The Degree of Report Bias on Offspring, Self, and Spouse Ratings. Journal of Autism and Developmental Disorders, 46(1), 164-175. doi:10.1007/s10803-015-2562-yMurphy, D., & Spooren, W. (2012). EU-AIMS: a boost to autism research. Nature Reviews Drug Discovery, 11(11), 815-816. doi:10.1038/nrd3881Nakai, Y., Takiguchi, T., Matsui, G., Yamaoka, N., & Takada, S. (2017). Detecting Abnormal Word Utterances in Children With Autism Spectrum Disorders. Perceptual and Motor Skills, 124(5), 961-973. doi:10.1177/0031512517716855Nikula, R. (1991). Psychological Correlates of Nonspecific Skin Conductance Responses. Psychophysiology, 28(1), 86-90. doi:10.1111/j.1469-8986.1991.tb03392.xNosek, B. A., Hawkins, C. B., & Frazier, R. S. (2011). Implicit social cognition: from measures to mechanisms. Trends in Cognitive Sciences, 15(4), 152-159. doi:10.1016/j.tics.2011.01.005Palkovitz, R. J., & Wiesenfeld, A. R. (1980). Differential autonomic responses of autistic and normal children. Journal of Autism and Developmental Disorders, 10(3), 347-360. doi:10.1007/bf02408294Parsons, S. (2016). Authenticity in Virtual Reality for assessment and intervention in autism: A conceptual review. Educational Research Review, 19, 138-157. doi:10.1016/j.edurev.2016.08.001Parsons, T. D. (2016). Telemedicine, Mobile, and Internet-Based Neurocognitive Assessment. Clinical Neuropsychology and Technology, 99-111. doi:10.1007/978-3-319-31075-6_6Paulhus, D. L. (1991). Measurement and Control of Response Bias. Measures of Personality and Social Psychological Attitudes, 17-59. doi:10.1016/b978-0-12-590241-0.50006-xPicard, R. W., Fedor, S., & Ayzenberg, Y. (2015). Multiple Arousal Theory and Daily-Life Electrodermal Activity Asymmetry. Emotion Review, 8(1), 62-75. doi:10.1177/1754073914565517Reaven, J. A., Hepburn, S. L., & Ross, R. G. (2008). Use of the ADOS and ADI-R in Children with Psychosis: Importance of Clinical Judgment. Clinical Child Psychology and Psychiatry, 13(1), 81-94. doi:10.1177/1359104507086343Redish, A. D., & Gordon, J. A. (Eds.). (2016). Computational Psychiatry. doi:10.7551/mitpress/9780262035422.001.0001Riby, D. M., Whittle, L., & Doherty-Sneddon, G. (2012). Physiological reactivity to faces via live and video-mediated communication in typical and atypical development. Journal of Clinical and Experimental Neuropsychology, 34(4), 385-395. doi:10.1080/13803395.2011.645019Rogers, S. J., & Ozonoff, S. (2005). Annotation: What do we know about sensory dysfunction in autism? A critical review of the empirical evidence. Journal of Child Psychology and Psychiatry, 46(12), 1255-1268. doi:10.1111/j.1469-7610.2005.01431.xSchmidt, L., Kirchner, J., Strunz, S., Broźus, J., Ritter, K., Roepke, S., & Dziobek, I. (2015). Psychosocial Functioning and Life Satisfaction in Adults With Autism Spectrum Disorder Without Intellectual Impairment. Journal of Clinical Psychology, 71(12), 1259-1268. doi:10.1002/jclp.22225Schoen, S. A. (2009). Physiological and behavioral differences in sensory processing: a comparison of children with Autism Spectrum Disorder and Sensory Processing Disorder. Frontiers in Integrative Neuroscience, 3. doi:10.3389/neuro.07.029.2009Schölkopf, B., Smola, A. J., Williamson, R. C., & Bartlett, P. L. (2000). New Support Vector Algorithms. Neural Computation, 12(5), 1207-1245. doi:10.1162/089976600300015565Slater, M. (2009). Place illusion and plausibility can lead to realistic behaviour in immersive virtual environments. Philosophical Transactions of the Royal Society B: Biological Sciences, 364(1535), 3549-3557. doi:10.1098/rstb.2009.0138Slater, M., & Wilbur, S. (1997). A Framework for Immersive Virtual Environments (FIVE): Speculations on the Role of Presence in Virtual Environments. Presence: Teleoperators and Virtual Environments, 6(6), 603-616. doi:10.1162/pres.1997.6.6.603Stevens, S., & Gruzelier, J. (1984). Electrodermal activity to auditory stimuli in autistic, retarded, and normal children. Journal of Autism and Developmental Disorders, 14(3), 245-260. doi:10.1007/bf02409577Tomchek, S. D., & Dunn, W. (2007). Sensory Processing in Children With and Without Autism: A Comparative Study Using the Short Sensory Profile. American Journal of Occupational Therapy, 61(2), 190-200. doi:10.5014/ajot.61.2.190Tomchek, S. D., Huebner, R. A., & Dunn, W. (2014). Patterns of sensory processing in children with an autism spectrum disorder. Research in Autism Spectrum Disorders, 8(9), 1214-1224. doi:10.1016/j.rasd.2014.06.006Van Engeland, H., Roelofs, J. W., Verbaten, M. N., & Slangen, J. L. (1991). Abnormal electrodermal reactivity to novel visual stimuli in autistic children. Psychiatry Research, 38(1), 27-38. doi:10.1016/0165-1781(91)90050-yVan Hecke, A. V., Stevens, S., Carson, A. M., Karst, J. S., Dolan, B., Schohl, K., … Brockman, S. (2013). Measuring the Plasticity of Social Approach: A Randomized Controlled Trial of the Effects of the PEERS Intervention on EEG Asymmetry in Adolescents with Autism Spectrum Disorders. Journal of Autism and Developmental Disorders, 45(2), 316-335. doi:10.1007/s10803-013-1883-yVolkmar, F. R., State, M., & Klin, A. (2009). Autism and autism spectrum disorders: diagnostic issues for the coming decade. Journal of Child Psychology and Psychiatry, 50(1-2), 108-115. doi:10.1111/j.1469-7610.2008.02010.xWang, X.-J., & Krystal, J. H. (2014). Computational Psychiatry. Neuron, 84(3), 638-654. doi:10.1016/j.neuron.2014.10.018Wang, Y., Hensley, M. K., Tasman, A., Sears, L., Casanova, M. F., & Sokhadze, E. M. (2015). Heart Rate Variability and Skin Conductance During Repetitive TMS Course in Children with Autism. Applied Psychophysiology and Biofeedback, 41(1), 47-60. doi:10.1007/s10484-015-9311-zWhite, M. P., Yeo, N., Vassiljev, P., Lundstedt, R., Wallergård, M., Albin, M., & Lõhmus, M. (2018). A prescription for “nature” – the potential of using virtual nature in therapeutics. Neuropsychiatric Disease and Treatment, Volume 14, 3001-3013. doi:10.2147/ndt.s179038White, S. W., Mazefsky, C. A., Dichter, G. S., Chiu, P. H., Richey, J. A., & Ollendick, T. H. (2014). Social‐cognitive, physiological, and neural mechanisms underlying emotion regulation impairments: understanding anxiety in autism spectrum disorder. International Journal of Developmental Neuroscience, 39(1), 22-36. doi:10.1016/j.ijdevneu.2014.05.012Wing, L., Gould, J., & Gillberg, C. (2011). Autism spectrum disorders in the DSM-V: Better or worse than the DSM-IV? Research in Developmental Disabilities, 32(2), 768-773. doi:10.1016/j.ridd.2010.11.003Yan, K., & Zhang, D. (2015). Feature selection and analysis on correlated gas sensor data with recursive feature elimination. Sensors and Actuators B: Chemical, 212, 353-363. doi:10.1016/j.snb.2015.02.025Zahn, T. P., Rumsey, J. M., & Van Kammen, D. P. (1987). Autonomic nervous system activity in autistic, schizophrenic, and normal men: Effects of stimulus significance. Journal of Abnormal Psychology, 96(2), 135-144. doi:10.1037/0021-843x.96.2.13

    Nutrición parenteral domiciliaria en España, 2019: informe del Grupo de Nutrición Artificial Domiciliaria y Ambulatoria NADYA

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    RESUMEN Objetivo: comunicar los datos de nutrición parenteral domiciliaria (NPD) obtenidos del registro del grupo NADYA-SENPE (www.nadyasenpe.com) del año 2019. Material y métodos: análisis descriptivo de los datos recogidos de pacientes adultos y pediátricos con NPD en el registro NADYA-SENPE desde el 1 de enero al 31 de diciembre de 2019. Resultados: se registraron 283 pacientes (51,9 %, mujeres), 31 niños y 252 adultos procedentes de 47 hospitales españoles, lo que representa una tasa de prevalencia de 6,01 pacientes/millón de habitantes/año 2019. El diagnóstico más frecuente en los adultos fue “oncológico paliativo” y “otros” (21,0 %). En los niños fue la enfermedad de Hirschsprung junto a la enterocolitis necrotizante, las alteraciones de la motilidad intestinal y la pseudoobstrucción intestinal crónica, con 4 casos cada uno (12,9 %). El primer motivo de indicación fue el síndrome del intestino corto tanto en los niños (51,6 %) como en los adultos (37,3 %). El tipo de catéter más utilizado fue el tunelizado tanto en los niños (75,9 %) como en los adultos (40,8 %). Finalizaron 68 episodios, todos en adultos: la causa más frecuente fue el fallecimiento (54,4 %). Pasaron a la vía oral el 38,2 %. Conclusiones: el número de centros y profesionales colaboradores con el registro NADYA va incrementándose. Se mantienen estables las principales indicaciones y los motivos de finalización de la NPD

    Papel de la quinasa del receptor (beta) adrenergico (bark) en los procesos de desensibilización homóloga de dicho receptor. Caracterización de su localización subcelular

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    Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Facultad de Ciencias, Departamento de Biología Molecular. Fecha de lectura: 14-10-199

    Genomic stability and tumour suppression by the APC/C cofactor Cdh1

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    The anaphase promoting complex or cyclosome (APC/C) is a ubiquitin protein ligase that, together with Cdc20 or Cdh1, targets cell-cycle proteins for degradation. APC/C-Cdh1 specifically promotes protein degradation in late mitosis and G1. Mutant embryos lacking Cdh1 die at E9.5-E10.5 due to defects in the endoreduplication of trophoblast cells and placental malfunction. This lethality is prevented when Cdh1 is expressed in the placenta. Cdh1-deficient cells proliferate inefficiently and accumulate numeric and structural chromosomal aberrations, indicating that Cdh1 contributes to the maintenance of genomic stability. Cdh1 heterozygous animals show increased susceptibility to spontaneous tumours, suggesting that Cdh1 functions as a haploinsufficient tumour suppressor. These heterozygous mice also show several defects in behaviour associated with increased proliferation of stem cells in the nervous system. These results indicate that Cdh1 is required for preventing unscheduled proliferation of specific progenitor cells and protecting mammalian cells from genomic instability.I.G.H. is supported by a Ramón y Cajal contract (Ministerio de Educación y Ciencia). E.M. is supported by an FIS fellowship (Ministerio de Sanidad). This work was supported by grants from the Association pour la Recherche contre le Cancer and the Région Aquitaine (to P.D.), Ministerio de Educación y Ciencia (SAF2004-05611 to I.G.H.; BFU2004-04886 to J.M.; BFU2005-03195 and GEN2003-20243-C08-05 to S.M.; and SAF2006-05186 to M.M.), the Consolider-Ingenio 2010 Programme (CSD2007-00015 to J.M. and S.M.; and CSD2007-00017 to M.M.), Comunidad de Madrid (OncoCycle Programme; S-BIO-0283-2006), Fundación Ramón Areces, and Fundación Médica Mutua Madrileña Automovilística (to M.M.); and Fundación Científica de la Asociación Española contra el Cáncer (to S.M. and M.M.).Peer Reviewe

    Replication defects in the absence of the APC/C cofactor CDH1

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    Resumen del trabajo presentado al Ramón Areces Foundation International Symposium: Cell Proliferation and Genome Integrity, celebrado en Santander (España) del 3 al 4 de abril de 2014.The anaphase promoting complex/cyclosome (APC/C) is an E3 ubiquitin ligase that promotes the degradation of different substrates by the proteasome. Its activity is limited to the cell cycle period comprised between anaphase and the end of the G1 phase, and is controlled by the regulated binding of two co-factors: Cdc20 or Cdh1. APC/C-Cdc20 is active during anaphase, while APC/C-Cdh1 is active from anaphase until the end of G1. APC/C-Cdh1 is known to play an important role in cell cycle progression, controling the levels of mitotic cyclins and other critical cell-cycle regulators. To get further insight into the biological function of Cdh1 in mammals, our group has generated a Cdh1 knockout mouse model (García-Higuera et al, 2008) that we are characterising at the cellular as well as the organismal level. Our results indicate that cells derived from mutant embryos (MEFs) accumulation of DNA damage, and activation of the DNA damage response, suggesting the presence of replication stress in Cdh1 deficient cells. To verify that this was the case, we performed a direct analysis of the DNA replication process and found altered replication dynamics in Cdh1 mutant cells. In particular, Cdh1 deficiency reduces the rate of replication fork progression while increasing the frequency of origing firing. These observations further confirm the importance of the APC/C-Cdh1 complex in preventing replication defects. We are now trying to understand at the molecular level how the absence of Cdh1 triggers these defects.Peer reviewe
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