Retos COVID-19

El 15 de marzo del 2020 marcó un antes y un después en nuestras vidas con el coronavirus como gran protagonista a nivel internacional. Nos acostamos un domingo por la noche, después de haber disfrutado del fin de semana al aire libre, y amanecimos encerrados en nuestras casas durante un periodo de tres meses. La población andaba perdida y las fake news se expandían a mayor velocidad que la transmisión del virus. Este libro presenta un total de cincuenta y dos artículos que, recogidos a lo largo de un año de pandemia, ofrecen al lector un dibujo del panorama vivido y de las inquietudes por las que hemos pasado. Desde las primeras medidas de protección individual (mascarilla sí, mascarilla no; cierre total sí versus cierre parcial mejor) a los primeros datos sobre la sintomatología de los enfermos o los tratamientos que se empezaron a aplicar sin conocer exactamente sus bondades. Investigaciones sobre el origen del virus, las promesas de que las vacunas iban a estar listas en breve¿ tantos interrogantes planteados a lo largo de unos meses que han ido pasando, situándonos en una rápida desescalada y, casi sin darnos cuenta, en la segunda ola después del verano.Poco después llegó Navidad y, tras ella, sin apenas margen temporal, la tercera ola, la cuarta y la quinta al inicio del verano, cuando gran parte de la población estaba vacunada y pensábamos que el verano era nuestro. Pero después de estos contagios sucesivos se nos plantean muchas dudas distintas. Todos estos temas son los que hemos plasmado en Retos COVID-19 y que inspiran este libro, siendo la vía diseñada para canalizar con rigor la correcta información a través de webinars, una página específica dentro de la web de la Fundación QUAES y newsletters semanalesMoratal Pérez, D. (2022). Retos COVID-19. Editorial Universitat Politècnica de València. https://doi.org/10.4995/2022.669901EDITORIA

Link-Level Functional Connectivity Neuroalterations in Autism Spectrum Disorder: A Developmental Resting-State fMRI Study

[EN] Autism spectrum disorder (ASD) is a neurological and developmental disorder whose late diagnosis is based on subjective tests. In seeking for earlier diagnosis, we aimed to find objective biomarkers via analysis of resting-state functional MRI (rs-fMRI) images obtained from the Autism Brain Image Data Exchange (ABIDE) database. Thus, we estimated brain functional connectivity (FC) between pairs of regions as the statistical dependence between their neural-related blood-oxygen-level-dependent (BOLD) signals. We compared FC of individuals with ASD and healthy controls, matched by age and intelligence quotient (IQ), and split into three age groups (50 children, 98 adolescents, and 32 adults), from a developmental perspective. After estimating the correlation, we observed hypoconnectivities in children and adolescents with ASD between regions belonging to the default mode network (DMN). Concretely, in children, FC decreased between the left middle temporal gyrus and right frontal pole (p = 0.0080), and between the left orbitofrontal cortex and right superior frontal gyrus (p = 0.0144). In adolescents, this decrease was observed between bilateral postcentral gyri (p = 0.0012), and between the right precuneus and right middle temporal gyrus (p = 0.0236). These results help to gain a better understanding of the involved regions on autism and its connection with the affected superior cognitive brain functions.This research was partially funded by the Ministerio de Economia y Competitividad (MINECO), through the project BFU2015-64380-C2-2-R. U.P.-R. is funded by the Spanish Ministerio de Educacion, Cultura y Deporte (MECD) under grant FPU13/03537. We are thankful to the initiative ABIDE to provide the huge public release and open sharing autism database what has made possible to carry out this study in better conditions and improve the results obtained significantly.Borràs-Ferrís, L.; Pérez-Ramírez, MÚ.; Moratal, D. (2019). Link-Level Functional Connectivity Neuroalterations in Autism Spectrum Disorder: A Developmental Resting-State fMRI Study. Diagnostics. 9(1):1-10. https://doi.org/10.3390/diagnostics9010032S11091Hull, J. V., Dokovna, L. B., Jacokes, Z. J., Torgerson, C. M., Irimia, A., & Van Horn, J. D. (2017). Resting-State Functional Connectivity in Autism Spectrum Disorders: A Review. Frontiers in Psychiatry, 7. doi:10.3389/fpsyt.2016.00205Mertz, L. (2017). Sharing Data to Solve the Autism Riddle: An Interview with Adriana Di Martino and Michael Milham of ABIDE. IEEE Pulse, 8(6), 6-9. doi:10.1109/mpul.2017.2750819Cociu, B. A., Das, S., Billeci, L., Jamal, W., Maharatna, K., Calderoni, S., … Muratori, F. (2018). Multimodal Functional and Structural Brain Connectivity Analysis in Autism: A Preliminary Integrated Approach With EEG, fMRI, and DTI. IEEE Transactions on Cognitive and Developmental Systems, 10(2), 213-226. doi:10.1109/tcds.2017.2680408Dekhil, O., Hajjdiab, H., Shalaby, A., Ali, M. T., Ayinde, B., Switala, A., … El-Baz, A. (2018). Using resting state functional MRI to build a personalized autism diagnosis system. PLOS ONE, 13(10), e0206351. doi:10.1371/journal.pone.0206351Rogers, B. P., Morgan, V. L., Newton, A. T., & Gore, J. C. (2007). Assessing functional connectivity in the human brain by fMRI. Magnetic Resonance Imaging, 25(10), 1347-1357. doi:10.1016/j.mri.2007.03.007Cheng, W., Rolls, E. T., Gu, H., Zhang, J., & Feng, J. (2015). Autism: reduced connectivity between cortical areas involved in face expression, theory of mind, and the sense of self. Brain, 138(5), 1382-1393. doi:10.1093/brain/awv051Lynch, C. J., Uddin, L. Q., Supekar, K., Khouzam, A., Phillips, J., & Menon, V. (2013). Default Mode Network in Childhood Autism: Posteromedial Cortex Heterogeneity and Relationship with Social Deficits. Biological Psychiatry, 74(3), 212-219. doi:10.1016/j.biopsych.2012.12.013Uddin, L. Q., Supekar, K., Lynch, C. J., Khouzam, A., Phillips, J., Feinstein, C., … Menon, V. (2013). Salience Network–Based Classification and Prediction of Symptom Severity in Children With Autism. JAMA Psychiatry, 70(8), 869. doi:10.1001/jamapsychiatry.2013.104Di Martino, A., Kelly, C., Grzadzinski, R., Zuo, X.-N., Mennes, M., Mairena, M. A., … Milham, M. P. (2011). Aberrant Striatal Functional Connectivity in Children with Autism. Biological Psychiatry, 69(9), 847-856. doi:10.1016/j.biopsych.2010.10.029Washington, S. D., Gordon, E. M., Brar, J., Warburton, S., Sawyer, A. T., Wolfe, A., … VanMeter, J. W. (2013). Dysmaturation of the default mode network in autism. Human Brain Mapping, 35(4), 1284-1296. doi:10.1002/hbm.22252Assaf, M., Jagannathan, K., Calhoun, V. D., Miller, L., Stevens, M. C., Sahl, R., … Pearlson, G. D. (2010). Abnormal functional connectivity of default mode sub-networks in autism spectrum disorder patients. NeuroImage, 53(1), 247-256. doi:10.1016/j.neuroimage.2010.05.067Weng, S.-J., Wiggins, J. L., Peltier, S. J., Carrasco, M., Risi, S., Lord, C., & Monk, C. S. (2010). Alterations of resting state functional connectivity in the default network in adolescents with autism spectrum disorders. Brain Research, 1313, 202-214. doi:10.1016/j.brainres.2009.11.057Kennedy, D. P., & Courchesne, E. (2008). The intrinsic functional organization of the brain is altered in autism. NeuroImage, 39(4), 1877-1885. doi:10.1016/j.neuroimage.2007.10.052Mueller, S., Keeser, D., Samson, A. C., Kirsch, V., Blautzik, J., Grothe, M., … Meindl, T. (2013). Convergent Findings of Altered Functional and Structural Brain Connectivity in Individuals with High Functioning Autism: A Multimodal MRI Study. PLoS ONE, 8(6), e67329. doi:10.1371/journal.pone.0067329Von dem Hagen, E. A. H., Stoyanova, R. S., Baron-Cohen, S., & Calder, A. J. (2012). Reduced functional connectivity within and between ‘social’ resting state networks in autism spectrum conditions. Social Cognitive and Affective Neuroscience, 8(6), 694-701. doi:10.1093/scan/nss053Uddin, L. Q., Supekar, K., & Menon, V. (2013). Reconceptualizing functional brain connectivity in autism from a developmental perspective. Frontiers in Human Neuroscience, 7. doi:10.3389/fnhum.2013.00458Kana, R. K., Uddin, L. Q., Kenet, T., Chugani, D., & Müller, R.-A. (2014). Brain connectivity in autism. Frontiers in Human Neuroscience, 8. doi:10.3389/fnhum.2014.00349Beckmann, C. F., & Smith, S. M. (2004). Probabilistic Independent Component Analysis for Functional Magnetic Resonance Imaging. IEEE Transactions on Medical Imaging, 23(2), 137-152. doi:10.1109/tmi.2003.822821Cole. (2010). Advances and pitfalls in the analysis and interpretation of resting-state FMRI data. Frontiers in Systems Neuroscience. doi:10.3389/fnsys.2010.00008Etzel, J. A., Gazzola, V., & Keysers, C. (2009). An introduction to anatomical ROI-based fMRI classification analysis. Brain Research, 1282, 114-125. doi:10.1016/j.brainres.2009.05.090Di 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.2013.78Cameron, C., Yassine, B., Carlton, C., Francois, C., Alan, E., András, J., … Pierre, B. (2013). The Neuro Bureau Preprocessing Initiative: open sharing of preprocessed neuroimaging data and derivatives. Frontiers in Neuroinformatics, 7. doi:10.3389/conf.fninf.2013.09.00041Xu, T., Yang, Z., Jiang, L., Xing, X.-X., & Zuo, X.-N. (2015). A Connectome Computation System for discovery science of brain. Science Bulletin, 60(1), 86-95. doi:10.1007/s11434-014-0698-3Desikan, R. S., Ségonne, F., Fischl, B., Quinn, B. T., Dickerson, B. C., Blacker, D., … Killiany, R. J. (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage, 31(3), 968-980. doi:10.1016/j.neuroimage.2006.01.021Cole, M. W., Yang, G. J., Murray, J. D., Repovš, G., & Anticevic, A. (2016). Functional connectivity change as shared signal dynamics. Journal of Neuroscience Methods, 259, 22-39. doi:10.1016/j.jneumeth.2015.11.011Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E. J., Johansen-Berg, H., … Matthews, P. M. (2004). Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage, 23, S208-S219. doi:10.1016/j.neuroimage.2004.07.051Jenkinson, M., Beckmann, C. F., Behrens, T. E. J., Woolrich, M. W., & Smith, S. M. (2012). FSL. NeuroImage, 62(2), 782-790. doi:10.1016/j.neuroimage.2011.09.015Winkler, A. M., Ridgway, G. R., Webster, M. A., Smith, S. M., & Nichols, T. E. (2014). Permutation inference for the general linear model. NeuroImage, 92, 381-397. doi:10.1016/j.neuroimage.2014.01.060Bhaumik, R., Pradhan, A., Das, S., & Bhaumik, D. K. (2018). Predicting Autism Spectrum Disorder Using Domain-Adaptive Cross-Site Evaluation. Neuroinformatics, 16(2), 197-205. doi:10.1007/s12021-018-9366-0Cavanna, A. E., & Trimble, M. R. (2006). The precuneus: a review of its functional anatomy and behavioural correlates. Brain, 129(3), 564-583. doi:10.1093/brain/awl004Rolls, E. T. (2004). The functions of the orbitofrontal cortex. Brain and Cognition, 55(1), 11-29. doi:10.1016/s0278-2626(03)00277-xBeer, J. S., John, O. P., Scabini, D., & Knight, R. T. (2006). Orbitofrontal Cortex and Social Behavior: Integrating Self-monitoring and Emotion-Cognition Interactions. Journal of Cognitive Neuroscience, 18(6), 871-879. doi:10.1162/jocn.2006.18.6.871Li, W., Qin, W., Liu, H., Fan, L., Wang, J., Jiang, T., & Yu, C. (2013). Subregions of the human superior frontal gyrus and their connections. NeuroImage, 78, 46-58. doi:10.1016/j.neuroimage.2013.04.011Di Martino, A., O’Connor, D., Chen, B., Alaerts, K., Anderson, J. S., Assaf, M., … Milham, M. P. (2017). Enhancing studies of the connectome in autism using the autism brain imaging data exchange II. Scientific Data, 4(1). doi:10.1038/sdata.2017.1

Automatic positioning device for cutting three-dimensional tissue in living or fixed samples. Proof of concept

"© 2017 IEEE. Personal use of this material is permitted. Permissíon from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertisíng or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works."[EN] The study and analysis of tissues has always been an important part of the subject in biology. For this reason, obtaining specimens of tissue has been vital to morphological and functionality research. Historically, the main tools used to obtain slices of tissue have been microtomes and vibratomes. However, they are largely unsatisfactory. This is because it is impossible to obtain a full, three-dimensional structure of a tissue sample with these devices. This paper presents an automatic positioning device for a three-dimensional cut in living or fixed tissue samples, which can be applied mainly in histology, anatomy, biochemistry and pharmacology. The system consists of a platform on which the tissue samples can be deposited, plus two containers. An electromechanical system with motors and gears gives the platform the ability to change the orientation of a sample. These orientation changes were tested with movement sensors to ensure that accurate changes were made. This device paves the way for researchers to make cuts in the sample tissue along different planes and in different directions by maximizing the surface of the tract that appears in a slice.Research supported in part by the Spanish Ministerio de Economia y Competitividad (MINECO) and FEDER funds under grants BFU2015-64380-C2-2-R and BFU2015-64380-C2-1-R. Santiago Canals acknowledges financial support from the Spanish State Research Agency, through the "Severo Ochoa" Programme for Centres of Excellence in R&D (ref. SEV- 2013-0317). Dario Quinones is supported by grant Ayudas para la formacion de personal investigador (FPI) from Universitat Politecnica de Valencia. We are grateful to Begoña Fernández (Neuroscience Institute, Consejo Superior de Investigaciones Científicas - CSIC, Alicante, Spain) for her excellent technical assistance.Quiñones, DR.; Pérez Feito, R.; García Manrique, JA.; Canals-Gamoneda, S.; Moratal, D. (2017). Automatic positioning device for cutting three-dimensional tissue in living or fixed samples. Proof of concept. Proceedings Intenational Anual Conference of IEEE Engineering in Medicine and Biology Society. 1372-1375. https://doi.org/10.1109/EMBC.2017.8037088S1372137

Automatic left ventricle volume calculation with explainability through a deep learning weak-supervision methodology

[EN] Background and objective: Magnetic resonance imaging is the most reliable imaging technique to assess the heart. More specifically there is great importance in the analysis of the left ventricle, as the main pathologies directly affect this region. In order to characterize the left ventricle, it is necessary to extract its volume. In this work we present a neural network architecture that is capable of directly estimating the left ventricle volume in short axis cine Magnetic Resonance Imaging in the end-diastolic frame and provide a segmentation of the region which is the basis of the volume calculation, thus offering explain-ability to the estimated value. Methods: The network was designed to directly target the volumes to estimate, not requiring any labeled segmentation on the images. The network was based on a 3D U-net with extra layers defined in a scan-ning module that learned features like the circularity of the objects and the volumes to estimate in a weakly-supervised manner. The only targets defined were the left ventricle volumes and the circularity of the object detected through the estimation of the pi value derived from its shape. We had access to 397 cases corresponding to 397 different subjects. We randomly selected 98 cases to use as test set. Results: The results show a good match between the real and estimated volumes in the test set, with a mean relative error of 8% and a mean absolute error of 9.12 ml with a Pearson correlation coefficient of 0.95. The derived segmentations obtained by the network achieved Dice coefficients with a mean value of 0.79. Conclusions: The proposed method is capable of obtaining the left ventricle volume biomarker in the end-diastole and offer an explanation of how it obtains the result in the form of a segmentation mask without the need of segmentation labels to train the algorithm, making it a potentially more trustworthy method for clinicians and a way to train neural networks more easily when segmentation labels are not readily available.The authors acknowledge financial support from the Consel-leria d'Educacio, Investigacio, Cultura i Esport, Generalitat Valenciana (grants AEST/2019/037 and AEST/2020/029) , from the Agencia Valenciana de la Innovacion, Generalitat Valenciana (ref. INNCAD00/19/085) , and from the Centro para el Desarrollo Tecnologico Industrial (Programa Eurostars2, actuacion Interempresas Internacional) , Spanish Ministerio de Ciencia, Innovacion y Universidades (ref. CIIP-20192020) .Pérez-Pelegrí, M.; Monmeneu, JV.; López-Lereu, MP.; Pérez-Pelegrí, L.; Maceira, AM.; Bodi, V.; Moratal, D. (2021). Automatic left ventricle volume calculation with explainability through a deep learning weak-supervision methodology. Computer Methods and Programs in Biomedicine. 208:1-8. https://doi.org/10.1016/j.cmpb.2021.106275S1820

A Tangible Educative 3D Printed Atlas of the Rat Brain

[EN] In biology and neuroscience courses, brain anatomy is usually explained using Magnetic Resonance (MR) images or histological sections of different orientations. These can show the most important macroscopic areas in an animals¿ brain. However, this method is neither dynamic nor intuitive. In this work, an anatomical 3D printed rat brain with educative purposes is presented. Hand manipulation of the structure, facilitated by the scale up of its dimensions, and the ability to dismantle the ¿brain¿ into some of its constituent parts, facilitates the understanding of the 3D organization of the nervous system. This is an alternative method for teaching students in general and biologists in particular the rat brain anatomy. The 3D printed rat brain has been developed with eight parts, which correspond to the most important divisions of the brain. Each part has been fitted with interconnections, facilitating assembling and disassembling as required. These solid parts were smoothed out, modified and manufactured through 3D printing techniques with poly(lactic acid) (PLA). This work presents a methodology that could be expanded to almost any field of clinical and pre-clinical research, and moreover it avoids the need for dissecting animals to teach brain anatomy.This work was supported in part by the Spanish Ministerio de Economia y Competitividad (MINECO) and FEDER funds under grants BFU2015-64380-C2-2-R (D.M.) and BFU2015-64380-C2-1-R and EU Horizon 2020 Program 668863-SyBil-AA grant (S.C.). S.C. acknowledges financial support from the Spanish State Research Agency, through the "Severo Ochoa" Programme for Centres of Excellence in R&D (ref. SEV-2013-0317). D.R.Q. was supported by grant "Ayudas para la formacion de personal investigador (FPI)" from the Vicerrectorado de Investigacion, Innovacion y Transferencia of the Universitat Politecnica de Valencia.Quiñones, DR.; Ferragud-Agulló, J.; Pérez Feito, R.; García Manrique, JA.; Canals-Gamoneda, S.; Moratal, D. (2018). A Tangible Educative 3D Printed Atlas of the Rat Brain. Materials. 11(9):1531-1542. https://doi.org/10.3390/ma11091531S15311542119Perrin, R. J., Fagan, A. M., & Holtzman, D. M. (2009). Multimodal techniques for diagnosis and prognosis of Alzheimer’s disease. Nature, 461(7266), 916-922. doi:10.1038/nature08538Linden, D. E. J. (2012). The Challenges and Promise of Neuroimaging in Psychiatry. Neuron, 73(1), 8-22. doi:10.1016/j.neuron.2011.12.014Teipel, S., Drzezga, A., Grothe, M. J., Barthel, H., Chételat, G., Schuff, N., … Fellgiebel, A. (2015). Multimodal imaging in Alzheimer’s disease: validity and usefulness for early detection. The Lancet Neurology, 14(10), 1037-1053. doi:10.1016/s1474-4422(15)00093-9Woo, C.-W., Chang, L. J., Lindquist, M. A., & Wager, T. D. (2017). Building better biomarkers: brain models in translational neuroimaging. Nature Neuroscience, 20(3), 365-377. doi:10.1038/nn.4478Ivanov, I. (2017). The Neuroimaging Gap - Where do we go from Here? Acta Psychopathologica, 03(03). doi:10.4172/2469-6676.100090Kastrup, O., Wanke, I., & Maschke, M. (2005). Neuroimaging of infections. NeuroRX, 2(2), 324-332. doi:10.1602/neurorx.2.2.324Preece, D., Williams, S. B., Lam, R., & Weller, R. (2013). «Let»s Get Physical’: Advantages of a physical model over 3D computer models and textbooks in learning imaging anatomy. Anatomical Sciences Education, 6(4), 216-224. doi:10.1002/ase.1345Zheng, Y., Yu, D., Zhao, J., Wu, Y., & Zheng, B. (2016). 3D Printout Models vs. 3D-Rendered Images: Which Is Better for Preoperative Planning? Journal of Surgical Education, 73(3), 518-523. doi:10.1016/j.jsurg.2016.01.003Li, Z., Li, Z., Xu, R., Li, M., Li, J., Liu, Y., … Chen, Z. (2015). Three-dimensional printing models improve understanding of spinal fracture—A randomized controlled study in China. Scientific Reports, 5(1). doi:10.1038/srep11570Kettenbach, J., Wong, T., Kacher, D., Hata, N., Schwartz, R. ., Black, P. M., … Jolesz, F. . (1999). Computer-based imaging and interventional MRI: applications for neurosurgery. Computerized Medical Imaging and Graphics, 23(5), 245-258. doi:10.1016/s0895-6111(99)00022-1Schwarz, A. J., Danckaert, A., Reese, T., Gozzi, A., Paxinos, G., Watson, C., … Bifone, A. (2006). A stereotaxic MRI template set for the rat brain with tissue class distribution maps and co-registered anatomical atlas: Application to pharmacological MRI. NeuroImage, 32(2), 538-550. doi:10.1016/j.neuroimage.2006.04.214Marro, A., Bandukwala, T., & Mak, W. (2016). Three-Dimensional Printing and Medical Imaging: A Review of the Methods and Applications. Current Problems in Diagnostic Radiology, 45(1), 2-9. doi:10.1067/j.cpradiol.2015.07.009Michalski, M. H., & Ross, J. S. (2014). The Shape of Things to Come. JAMA, 312(21), 2213. doi:10.1001/jama.2014.9542Ratto, M., & Ree, R. (2012). Materializing information: 3D printing and social change. First Monday, 17(7). doi:10.5210/fm.v17i7.3968Rengier, F., Mehndiratta, A., von Tengg-Kobligk, H., Zechmann, C. M., Unterhinninghofen, R., Kauczor, H.-U., & Giesel, F. L. (2010). 3D printing based on imaging data: review of medical applications. International Journal of Computer Assisted Radiology and Surgery, 5(4), 335-341. doi:10.1007/s11548-010-0476-xMannoor, M. S., Jiang, Z., James, T., Kong, Y. L., Malatesta, K. A., Soboyejo, W. O., … McAlpine, M. C. (2013). 3D Printed Bionic Ears. Nano Letters, 13(6), 2634-2639. doi:10.1021/nl4007744Guy, J. R., Sati, P., Leibovitch, E., Jacobson, S., Silva, A. C., & Reich, D. S. (2016). Custom fit 3D-printed brain holders for comparison of histology with MRI in marmosets. Journal of Neuroscience Methods, 257, 55-63. doi:10.1016/j.jneumeth.2015.09.002Friston, K. J., Holmes, A. P., Worsley, K. J., Poline, J.-P., Frith, C. D., & Frackowiak, R. S. J. (1994). Statistical parametric maps in functional imaging: A general linear approach. Human Brain Mapping, 2(4), 189-210. doi:10.1002/hbm.460020402Flandin, G., & Novak, M. J. U. (2013). fMRI Data Analysis Using SPM. fMRI, 51-76. doi:10.1007/978-3-642-34342-1_6Mueller, B. (2012). Additive Manufacturing Technologies – Rapid Prototyping to Direct Digital Manufacturing. Assembly Automation, 32(2). doi:10.1108/aa.2012.03332baa.010Gulanová, J., Kister, I., Káčer, N., & Gulan, L. (2018). A Comparative Study of various AM Technologies Based on Their Accuracy. Procedia CIRP, 67, 238-243. doi:10.1016/j.procir.2017.12.206D’Urso, P. S., Barker, T. M., Earwaker, W. J., Bruce, L. J., Atkinson, R. L., Lanigan, M. W., … Effeney, D. J. (1999). Stereolithographic biomodelling in cranio-maxillofacial surgery: a prospective trial. Journal of Cranio-Maxillofacial Surgery, 27(1), 30-37. doi:10.1016/s1010-5182(99)80007-9Müller, A., Krishnan, K. G., Uhl, E., & Mast, G. (2003). The Application of Rapid Prototyping Techniques in Cranial Reconstruction and Preoperative Planning in Neurosurgery. Journal of Craniofacial Surgery, 14(6), 899-914. doi:10.1097/00001665-200311000-00014Guarino, J., Tennyson, S., McCain, G., Bond, L., Shea, K., & King, H. (2007). Rapid Prototyping Technology for Surgeries of the Pediatric Spine and Pelvis. Journal of Pediatric Orthopaedics, 27(8), 955-960. doi:10.1097/bpo.0b013e3181594cedCanstein, C., Cachot, P., Faust, A., Stalder, A. F., Bock, J., Frydrychowicz, A., … Markl, M. (2008). 3D MR flow analysis in realistic rapid-prototyping model systems of the thoracic aorta: Comparison with in vivo data and computational fluid dynamics in identical vessel geometries. Magnetic Resonance in Medicine, 59(3), 535-546. doi:10.1002/mrm.21331Giesel, F. L., Mehndiratta, A., von Tengg-Kobligk, H., Schaeffer, A., Teh, K., Hoffman, E. A., … Wild, J. M. (2009). Rapid Prototyping Raw Models on the Basis of High Resolution Computed Tomography Lung Data for Respiratory Flow Dynamics. Academic Radiology, 16(4), 495-498. doi:10.1016/j.acra.2008.10.008Malyala, S. K., Ravi Kumar, Y., & Rao, C. S. P. (2017). Organ Printing With Life Cells: A Review. Materials Today: Proceedings, 4(2), 1074-1083. doi:10.1016/j.matpr.2017.01.122Foster, K. R. (2016). 3-Dimensional Printing in Medicine: Hype, Hope, and the Challenge of Personalized Medicine. Philosophy and Engineering, 211-228. doi:10.1007/978-3-319-45193-0_1

Open Source 3D Printed Lung Tumor Movement Simulator for Radiotherapy Quality Assurance

[EN] In OECD (Organization for Economic Co-operation and Development) countries, cancer is one of the main causes of death, lung cancer being one of the most aggressive. There are several techniques for the treatment of lung cancer, among which radiotherapy is one of the most effective and least invasive for the patient. However, it has associated difficulties due to the moving target tumor. It is possible to reduce the side effects of radiotherapy by effectively tracking a tumor and reducing target irradiation margins. This paper presents a custom electromechanical system that follows the movement of a lung tumor. For this purpose, a hysteresis loop of human lung movement during breathing was studied to obtain its characteristic movement equation. The system is controlled by an Arduino, steppers motors and a customized 3D printed mechanism to follow the characteristic human breathing, obtaining an accurate trajectory. The developed device helps the verification of individualized radiation treatment plans and permits the improvement of radiotherapy quality assurance procedures.This work was supported in part by the Spanish Ministerio de Economia y Competitividad (MINECO) and FEDER funds under grants BFU2015-64380-C2-2-R (D.M.). D.R.Q. was supported by grant "Ayudas para la formacion de personal investigador (FPI)" from the Vicerrectorado de Investigacion, Innovacion y Transferencia of the Universitat Politecnica de Valencia.Quiñones Colomer, DR.; Soler-Egea, D.; González-Pérez, V.; Reibke, J.; Simarro-Mondejar, E.; Pérez Feito, R.; García Manrique, JA.... (2018). Open Source 3D Printed Lung Tumor Movement Simulator for Radiotherapy Quality Assurance. Materials. 11(8 (1317)):1-11. https://doi.org/10.3390/ma11081317S111118 (1317

Fibronectin-matrix sandwich-like microenvironments to manipulate cell fate

[EN] Conventional 2D substrates fail to represent the natural environment of cells surrounded by the 3D extracellular matrix (ECM). We have proposed sandwich-like microenvironments as a versatile tool to study cell behaviour under quasi-3D conditions. This is a system that provides a broad range of dorsal and ventral independent spatio-temporal stimuli. Here, we use this sandwich technology to address the role of dorsal stimuli in cell adhesion, cell proliferation and ECM reorganisation. Under certain conditions, dorsal stimuli within sandwich microenvironments prevent the formation of focal plaques as well as the development of the actin cytoskeleton, whereas alpha(5) versus alpha(v) integrin expression is increased compared to the corresponding 2D controls. Cell signaling is similarly enhanced after dorsal stimuli (measured by the pFAK/FAK level) for cells sandwiched after 3 h of 2D ventral adhesion, but not when sandwiched immediately after cell seeding (similar levels to the 2D control). Cell proliferation, studied by the 5-bromo-2-deoxyuridine (BrdU) incorporation assay, was significantly reduced within sandwich conditions as compared to 2D substrates. In addition, these results were found to depend on the ability of cells to reorganise the dorsal layer of proteins at the material interface, which could be tuned by adsorbing FN on material surfaces that results in a qualitatively different conformation and distribution of FN. Overall, sandwich-like microenvironments switch cell behaviour (cell adhesion, morphology and proliferation) towards 3D-like patterns, demonstrating the importance of this versatile, simple and robust approach to mimic cell microenvironments in vivo.The support from ERC through HealInSynergy (306990) and the FPU program AP2009-3626 are acknowledged.Ballester Beltrán, J.; Moratal Pérez, D.; Lebourg, MM.; Salmerón Sánchez, M. (2014). Fibronectin-matrix sandwich-like microenvironments to manipulate cell fate. Biomaterials Science. 2(3):381-389. https://doi.org/10.1039/C3BM60248FS3813892

PSPU-Net for Automatic Short Axis Cine MRI Segmentation of Left and Right Ventricles

[EN] Characterization of the heart anatomy and function is mostly done with magnetic resonance image cine series. To achieve a correct characterization, the volume of the right and left ventricle need to be segmented, which is a timeconsuming task. We propose a new convolutional neural network architecture that combines U-net with PSP modules (PSPU-net) for the segmentation of left and right ventricle cavities and left ventricle myocardium in the diastolic frame of short-axis cine MRI images and compare its results against a classic 3D U-net architecture. We used a dataset containing 399 cases in total. The results showed higher quality results in both segmentation and final volume estimation for a test set of 99 cases in the case of the PSPU-net, with global dice metrics of 0.910 and median absolute relative errors in volume estimations of 0.026 and 0.039 for the left ventricle cavity and myocardium and 0.051 for the right ventricles cavity.DM acknowledges financial support from the Conselleria d'Educacio, Investigacio, Cultura i Esport, Generalitat Valenciana (grants AEST/2019/037 and AEST/2020/029), from the Agencia Valenciana de la Innovacion, Generalitat Valenciana (ref. INNCAD00/19/085), and from the Centro para el Desarrollo Tecnologico Industrial (Programa Eurostars-2, actuacion Interempresas Internacional), Spanish Ministerio de Ciencia, Innovacion y Universidades (ref. CIIP20192020). We are grateful to Andres Larroza for his valuable technical assistance in the project.Pérez-Pelegrí, M.; Monmeneu, JV.; López-Lereu, MP.; Ruiz-España, S.; Del-Canto, I.; Bodi, V.; Moratal, D. (2020). PSPU-Net for Automatic Short Axis Cine MRI Segmentation of Left and Right Ventricles. IEEE Computer Society. 1048-1053. https://doi.org/10.1109/BIBE50027.2020.00177S1048105

Design and Assembly Procedures for Large-Sized Biohybrid Scaffolds as Patches for Myocardial Infarct

[EN] Objective: To assemble a biohybrid cardiac patch consisting of a large (5x5 cm) elastomer scaffold whose pores are filled with a self-assembling peptide (SAP) gel entrapping adipose stem cells, to be used as a novel implant in a big animal model (sheep) of myocardial infarction. The study focuses on the way to determine optimal procedures for incorporating the SAP solution and the cells in the patch to ensure cell colonization and a homogeneous cell distribution in the construct before implantation. The problems associated with the scale-up of the different procedures raised by the large size of the construct are discussed. Materials and Methods: Experiments were performed to choose between different assembling alternatives: incorporation of the SAP gel before cell seeding or simultaneous SAP and cell loading of the scaffold; surface seeding of cells or cell injection into the scaffold pores; dissemination of the cells throughout the scaffold before incubation by gentle shaking or by centrifugation. Immunocytochemistry techniques and confocal and scanning electron microscopies were employed to assess and quantify cell colonization of the material and early cell distribution. Cell concentrations and the uniformity of cellular distribution throughout the scaffold were taken as the main criteria to decide between the different alternative procedures. Results: The combination of peptide preloading, cell injection, and shaking before incubation yielded the best results in terms of greater cell density and the most uniform distribution after 24 h of culture compared with the other methods. These techniques could be scaled-up to obtain large biohybrid cardiac patches with success. Conclusions: The results obtained after the different seeding methods allowed us to establish an effective protocol for the assembly of large biohybrid patches for their subsequent implantation in the heart of a big animal model of myocardial infarct in the context of a preclinical study.The authors acknowledge the financing from the European Commission through the ‘‘Regeneration of cardiac tissue assisted by bioactive implants’’ (RECATABI) FP7 NMP3-SL-2009-229239 project. MMP acknowledges support of Instituto de Salud Carlos III with assistance from the European Regional Development Fund through CIBER-BBN initiative.Martínez Ramos, C.; Rodríguez Pérez, E.; Perez Garnes, M.; Chachques, JC.; Moratal Pérez, D.; Vallés Lluch, A.; Monleón Pradas, M. (2014). Design and Assembly Procedures for Large-Sized Biohybrid Scaffolds as Patches for Myocardial Infarct. Tissue Engineering Part C Methods. 20(10):817-827. https://doi.org/10.1089/ten.TEC.2013.0489S817827201

Different theta frameworks coexist in the rat hippocampus and are coordinated during memory-guided and novelty tasks

[EN] Hippocampal firing is organized in theta sequences controlled by internal memory processes and by external sensory cues, but how these computations are coordinated is not fully understood. Although theta activity is commonly studied as a unique coherent oscillation, it is the result of complex interactions between different rhythm generators. Here, by separating hippocampal theta activity in three different current generators, we found epochs with variable theta frequency and phase coupling, suggesting flexible interactions between theta generators. We found that epochs of highly synchronized theta rhythmicity preferentially occurred during behavioral tasks requiring coordination between internal memory representations and incoming sensory information. In addition, we found that gamma oscillations were associated with specific theta generators and the strength of theta-gamma coupling predicted the synchronization between theta generators. We propose a mechanism for segregating or integrating hippocampal computations based on the flexible coordination of different theta frameworks to accommodate the cognitive needs.European Regional Development Fund BFU2015-64380-C2-1-R Santiago Canals European Regional Development Fund BFU2015-64380-C2-2-R David Moratal European Regional Development Fund PGC2018-101055-B-I00 Santiago Canals Horizon 2020 Framework Programme 668863 (SyBil-AA) Santiago Canals Agencia Estatal de Investigacion SEV-2017-0723 Santiago Canals Ministerio de Economia y Competitividad TEC2016-80063-C3-3-R Claudio R Mirasso Ministerio de Economia y Competitividad TEC2016-80063-C3-2-R Ernesto Pereda Agencia Estatal de Investigacion MDM-2017-0711 Claudio R Mirasso Ministerio de Economi ' a y Competitividad SAF2016-80100-R Oscar Herreras The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.López-Madrona, VJ.; Pérez-Montoyo, E.; Alvarez-Salvado, E.; Moratal, D.; Herreras, O.; Pereda, E.; Mirasso, CR.... 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