Diseño y desarrollo de un sistema automático basado en algoritmos de "deep learning" para identificar distintos grados de tumor "budding" en cáncer de vejiga

[ES] El patrón de crecimiento tumor budding (TB) es una característica histológica que, por medio de una transición epitelio mesénquima en el frente tumoral, le confiere una alta agresividad y la gran capacidad de invadir tejidos y de provocar metástasis, dando lugar a un mal pronóstico del cáncer. Este patrón ha demostrado ser importante en los modelos tumorales más incidentes. En el presente trabajo, se pretenden aplicar las técnicas de deep learning para diseñar y desarrollar algoritmos que sean capaces de detectar automáticamente los principales patrones de crecimiento en el cáncer de vejiga a partir una imagen histológica. Dichos patrones están ligados al grado de malignidad del tumor, por lo tanto, el principal propósito del proyecto es proporcionar a los expertos una herramienta que ayude a obtener un diagnóstico seguro mediante la clasificación automática del tumor. En cuanto a la metodología empleada para la tesis, se desarrollan varias funciones para llevar a cabo el preprocesado de la base de datos con el fin de conseguir una base de datos acondicionada para los modelos. Posteriormente, se aplican algoritmos, tanto supervisados como no supervisados, que sean capaces de clasificar la presencia del cáncer y, además, diagnosticar la agresividad. Finalmente, con la totalidad de los resultados conseguidos, se evalúa su funcionalidad mediante análisis cualitativos y cuantitativos de los modelos, y se realiza una comparación entre los diferentes resultados y modelos implementados.[CA] El patró de creixement tumor budding (TB) és una característica histològica que, per mitjà d’una transició epiteli mesènquima en el front tumoral, li confereix una alta agressivitat i la gran capacitat d’envair teixits i de provocar metàstasis, donant lloc a un mal pronòstic del càncer. Aquest patró ha demostrat ser important en els models tumorals més incidents. En el present treball, es pretenen aplicar les tècniques de deep learning per a dissenyar i desenvolupar algorismes que siguen capaços de detectar automàticament els principals patrons de creixement en el càncer de bufeta a partir una imatge histològica. Aquests patrons estan lligats al grau de malignitat del tumor, per tant, el principal propòsit del projecte és proporcionar als experts una eina que ajude a obtindre un diagnòstic segur mitjançant la classificació automàtica del tumor. Quant a la metodologia emprada per a la tesi, es desenvolupen diverses funcions per a dur a terme el preprocessat de la base de dades amb la finalitat d’aconseguir una base de dades condicionada per als models. Posteriorment, s’apliquen algorismes, tant supervisats com no supervisats, que siguen capaços de classificar la presència del càncer i, a més, diagnosticar l’agressivitat. Finalment, amb la totalitat dels resultats aconseguits, s’avalua la seua funcionalitat mitjançant anàlisis qualitatives i quantitatives dels models, i es realitza una comparació entre els diferents resultats i models implementats.[EN] The growth pattern of tumor budding (TB) is a histologic characteristic that, by means of a mesenchyme epithelium transition in the tumor front, confers a higher aggressiveness and the great ability to invade tissues and cause metastasis, leading to a poor prognosis of cancer. This pattern has been shown to be important in the most incident tumor models. In this paper, it is intended to apply deep learning techniques to design and develop algorithms that are able to automatically detect the main growth patterns in bladder cancer from a histological image. These patterns are linked to the degree of malignancy of the tumor, so the main purpose of the project is to provide experts with a tool to help obtain a safe diagnosis by automatically classifying the tumor. Regarding the methodology used for the thesis, several functions are developed to carry out the preprocessing of the database in order to obtain a database conditioned for the models. Subsequently, both supervised and unsupervised algorithms are applied that are able to classify the presence of cancer and, in addition, diagnose aggression. Finally, with all the results obtained, its functionality is evaluated by qualitative and quantitative analysis of the models, and a comparison is made between the different results and models implemented.Esteve Domínguez, A. (2020). Diseño y desarrollo de un sistema automático basado en algoritmos de "deep learning" para identificar distintos grados de tumor "budding" en cáncer de vejiga. Universitat Politècnica de València. http://hdl.handle.net/10251/161921TFG

Modelado y simulación de los efectos del síndrome de Brugada en el potencial de acción cardíaco

[ES] La muerte súbita cardíaca (MSC) supone el 88% de las defunciones que se producen por patologías cardíacas en España, suponiendo por ello un gran problema en el sistema sanitario público. Entre la muerte súbita y el Síndrome de Brugada (SBr) existe una relación muy estrecha, ya que dicho síndrome está relacionado con canalopatías poco comunes en las enfermedades cardiovasculares. Además, se considera generalmente una patología asintomática donde su principal síntoma es precisamente la muerte súbita. Por tanto, es de vital interés poder prediagnosticar dichas canalopatías para poder prevenir un episodio arrítmico asociado al síndrome, poder tratar la enfermedad y poder conseguir que el paciente tenga una vida totalmente normal. Este trabajo de fin de grado se centra en el desarrollo de distintos modelos computacionales, basado tanto en corazón canino como en corazón humano, que permitan simular la actividad bioeléctrica (potenciales de acción y corrientes iónicas) e investigar los patrones eléctricos típicos del SBr para su diagnóstico. Los modelos han sido formulados para todas las zonas diferentes de los ventrículos cardíacos y permiten simular las variaciones que provocan las mutaciones específicas del SBr, particularmente la sobreexpresión de los canales de potasio transitorios de salida (Ito) o la subexpresión de los canales de sodio (INa). El software que se ha programado para implementar los modelos se ha desarrollado mediante la herramienta Matlab R . Los modelos usados para este proyecto incluyen un modelo de potencial de acción de célula ventricular cardiaca humana (el modelo de Ten Tusscher (2004)) y un modelo equivalente al anterior pero para células caninas (modelo de Decker (2008)). Los resultados obtenidos con el software desarrollado sirvieron para estudiar la relación entre el grado de sobreexpresión/subexpresión de los canales Ito/INa y la morfología del potencial de acción ventricular. Los resultados muestran que a partir de una cierta combinación de sobreexpresión de los canales Ito y subexpresión de los INa, el potencial de acción pierde bruscamente su configuración normal de “espiga y domo”, reduciéndose ampliamente su duración. Además, esta pérdida de la meseta no ocurre simultáneamente en las tres zonas de la pared cardiaca (endocardio, midmiocardio y epicardio), por lo que la patología genera una dispersión transmural del potencial de acción que podría conducir a la aparición de las arritmias mortales asociadas al SBr[EN] Sudden cardiac death (MSC) accounts for 88% of deaths that occur due to heart disease in Spain, which is therefore a major problem in the public health system. There is a very close relationship between sudden death and Brugada Syndrome (SBr), since this latter syndrome is related to uncommon canalopathies in cardiovascular diseases. In addition, it is generally considered an asymptomatic pathology where its main symptom is precisely sudden death. Therefore, it is of vital interest to be able to predict these canalopathies in order to prevent a arrhythmic episode associated with the syndrome, to be able to treat the disease and to be able to ensure that the patient has a completely normal life. This final project focuses on the development of different computational models, based on both the canine heart and the human heart, which allow simulating bioelectric activity (action potentials and ionic currents) and investigating the electrical patterns typical of SBr for diagnosis. The models have been formulated for all the different zones of the cardiac ventricles and allow to simulate the variations that cause the specific mutations of the SBr, particularly the overexpression of the transient potassium outflow channels (ITo) or the underexpression of the sodium channels (INa). The software that has been programmed to implement the models has been developed using the MatlabR c tool. The models used for this project include a human cardiac ventricular cell action potential model (the Ten Tusscher model (2004)) and an equivalent model but for canine cells (Decker model (2008)). The results obtained with the developed software served to study the relationship between the degree of overexpression/underexpression of the ITo/(INa) channels and the morphology of the ventricular action potential.The results show that from a certain combination of overexpression of the Ito channels and underexpression of the INa, the action potential abruptly loses its normal configuration of “spike and dome”, greatly reducing its duration. In addition, this loss of the plateau does not occur simultaneously in the three areas of the cardiac wall (endocardium, midmyocardium and epicardium), thus the pathology generates a transmural dispersion of the action potential that could lead to the appearance of fatal arrhythmias associated with SBr.Esteve Domínguez, A. (2019). Modelado y simulación de los efectos del síndrome de Brugada en el potencial de acción cardíaco. http://hdl.handle.net/10251/127974TFG

Estudios Filológicos: revisión de las Memorias de grado

El trabajo llevado a cabo por la Red Estudios Filológicos ha consistido en la revisión de las memorias de los siguientes grados: Español: lengua y literaturas; Estudios árabes e islámicos; Estudios franceses; Estudios ingleses y Filología Catalana. Se han corregido erratas evidentes, se han unificado criterios compartidos por todos los grados y se han subsanado yerros tanto de forma como de contenido con el objetivo de proceder a una solicitud de modificación de estas Memorias

Atherosclerotic plaque development in mice is enhanced by myeloid ZEB1 downregulation.

Accumulation of lipid-laden macrophages within the arterial neointima is a critical step in atherosclerotic plaque formation. Here, we show that reduced levels of the cellular plasticity factor ZEB1 in macrophages increase atherosclerotic plaque formation and the chance of cardiovascular events. Compared to control counterparts (Zeb1WT/ApoeKO), male mice with Zeb1 ablation in their myeloid cells (Zeb1∆M/ApoeKO) have larger atherosclerotic plaques and higher lipid accumulation in their macrophages due to delayed lipid traffic and deficient cholesterol efflux. Zeb1∆M/ApoeKO mice display more pronounced systemic metabolic alterations than Zeb1WT/ApoeKO mice, with higher serum levels of low-density lipoproteins and inflammatory cytokines and larger ectopic fat deposits. Higher lipid accumulation in Zeb1∆M macrophages is reverted by the exogenous expression of Zeb1 through macrophage-targeted nanoparticles. In vivo administration of these nanoparticles reduces atherosclerotic plaque formation in Zeb1∆M/ApoeKO mice. Finally, low ZEB1 expression in human endarterectomies is associated with plaque rupture and cardiovascular events. These results set ZEB1 in macrophages as a potential target in the treatment of atherosclerosis.S

Generalization of Deep Learning Algorithms for Chest X-rays

The dataset is divided by institutions and by x-ray machines.-- Appropriate images were selected for this project using the MicroDicom software. All images are anonymised.-- Methods for processing the data: 1. Resize [512, 512], 2. Remove the letters by cropping the image 0.15%, 3. Resize [512, 512], 4. Max-min normalize, 5. Convert to JPG.Dataset con imágenes de rayos-X patológicas y de control de neumonía provocada por COVID-19 tomadas en distintos hospitales y equipos de adquisición de imagen. Todos los pacientes tenían PCR positiva a la hora de realizarles la radiografía.Peer reviewe

Head-CT 2D/3D images with and without ICH prepared for Deep Learning

For the "ICH detection" part, radiologists selected parients according to inclusion and exclusion criteria. - Images with HIC: - Inclusion criteria: Diagnosis of an ICH between 2010 and 2015. - Exclusion criteria: Significant motion artifact Significant postsurgical changes. - Images without ICH (healthy): - Inclusion criteria: Having done a head-CT between 2010 and 2015 reported as “normal” or “ICH is ruled out”. - Exclusion criteria: Significant motion artifact. Other major diagnoses (such as tumors). For the "prognosis" part, only the pathological head-CTs were used. All images are de-identified.In order to access this dataset, it is necessary to send an email to [email protected] specifying the intended use. Commercial use is prohibited under license: Creative Commons Attribution-NonCommercial-ShareAlike (CC-BY-NC-SA).Para poder acceder a este dataset es necesario enviar un email a [email protected] especificando el uso que se le va a dar. Se prohíbe su uso comercial bajo la licencia Creative Commons Attribution-NonCommercial-ShareAlike (CC-BY-NC-SA).[EN] This dataset contains two sets of images and tabular data anonymised and prepared for its use in the training and/or validation of artificial neural networks. The first set of images includes 3322 JPG files with 2D images of head computed tomography (CT) scans with and without intracranial hemorrhage (ICH), as well as a CSV with demographic data (age and gender) associated to each file. The second set of images consists of 262 NPY files with 3D images of head-CT scans with ICH, together with a CSV that includes clinical data related to each image.[ES] Este dataset contiene dos conjuntos de imágenes y datos tabulares anonimizados y preparados para su uso en el entrenamiento o validación de redes neuronales artificiales. El primer conjunto de imágenes incluye 3322 archivos JPG con imágenes 2D de tomografías computarizadas (TCs) craneales sin y con hemorragia intracranial (HIC), así como un CSV con datos demográficos (edad y sexo) asociados a cada archivo. El segundo conjunto de imágenes consiste en 262 archivos NPY con imágenes 3D de TCs con HIC junto con un CSV que incluye varios datos clínicos asociados a las imágenes.Peer reviewe

A deep learning model for prognosis prediction after intracranial hemorrhage

[Background and purpose]: Intracranial hemorrhage (ICH) is a common life-threatening condition that must be rapidly diagnosed and treated. However, there is still a lack of consensus regarding treatment, driven to some extent by prognostic uncertainty. While several prediction models for ICH detection have already been published, here we present a deep learning predictive model for ICH prognosis.[Methods]: We included patients with ICH (n = 262), and we trained a custom model for the classification of patients into poor prognosis and good prognosis, using a hybrid input consisting of brain CT images and other clinical variables. We compared it with two other models, one trained with images only (I-model) and the other with tabular data only (D-model).[Results]: Our hybrid model achieved an area under the receiver operating characteristic curve (AUC) of .924 (95% confidence interval [CI]: .831-.986), and an accuracy of .861 (95% CI: .760-.960). The I- and D-models achieved an AUC of .763 (95% CI: .622-.902) and .746 (95% CI: .598-.876), respectively.[Conclusions]: The proposed hybrid model was able to accurately classify patients into good and poor prognosis. To the best of our knowledge, this is the first ICH prognosis prediction deep learning model. We concluded that deep learning can be applied for prognosis prediction in ICH that could have a great impact on clinical decision-making. Further, hybrid inputs could be a promising technique for deep learning in medical imaging.Peer reviewe

Inflammatory macrophages reprogram to immunosuppression by reducing mitochondrial translation

Acknowledgements: We are grateful to Dr. F. Sanchez-Madrid (Hospital Princesa and CNIC, Madrid, Spain), Dr. D. Cebrian (CNIC, Madrid, Spain), and Dr. A. Valledor (University of Barcelona, Spain) for helpful insights on early versions of the manuscript. We thank Dr. C. Stephan-Otto Attolini (BIST-IRB, Barcelona, Spain) and Dr. J. Rios (IDIBAPS, Hospital Clinic, and Autonomous University of Barcelona, Barcelona, Spain) for their expert guidance on the statistical analyses of the data in the study. We also thank Dr. L. Ribas de Pouplana (BIST-IRB, Barcelona, Spain) for advice on mitochondrial translation experiments. We acknowledge technical assistance by staff in the Flow Cytometry Unit at IDIBAPS, the Molecular Interactions Services Unit at the Biomedical Research Institute of Bellvitge (IDIBELL), and the Transmission Electron Microscopy Unit at the University of Barcelona School of Medicine. We also thank A Téllez (Hospital Clinic, Barcelona, Spain) for his help in collecting samples from septic patients, and Dr. MJ Fernández-Aceñero (Hospital Clinico San Carlos, Madrid, Spain) for help in collecting skin samples from healthy controls, psoriatic patients, and melanoma patients. We are also thankful to Dr. DC Dean (University of Louisville, KY, USA) for his generous gift of an anti-ZEB1 polyclonal antibody. We thank Dr. A. Garcia for the artistic drawing of schematics in the article. IDIBAPS is partly funded by the CERCA Programme of Generalitat de Catalunya. The study was conducted at IDIBAPS’ Centre de Recerca Biomèdica Cellex building, which was partly funded by the Cellex Foundation. The different parts of this study were independently funded by grants to AP from the Leo Foundation (LF-OC-19-000166), the Catalan Agency for Management of University and Research Grants (AGAUR) (2017-SGR-1174 and 2021-SGR-01328), and the Spanish State Research Agency (AEI) of the Ministry of Science and Innovation (MICINN) (PID2020-116338RB-I00) as part of MICINN’s National Scientific and Technical Research and Innovation 2021-2023 Plan, which is co-financed by the European Regional Development Fund (ERDF) of the European Union Commission. AB is a recipient of a PhD scholarship from AGAUR (FI Program, 2021 FI_B 00514).Funder: Government of Catalonia | Agència de Gestió d&apos;Ajuts Universitaris i de Recerca (Agency for Management of University and Research Grants)AbstractAcute inflammation can either resolve through immunosuppression or persist, leading to chronic inflammation. These transitions are driven by distinct molecular and metabolic reprogramming of immune cells. The anti-diabetic drug Metformin inhibits acute and chronic inflammation through mechanisms still not fully understood. Here, we report that the anti-inflammatory and reactive-oxygen-species-inhibiting effects of Metformin depend on the expression of the plasticity factor ZEB1 in macrophages. Using mice lacking Zeb1 in their myeloid cells and human patient samples, we show that ZEB1 plays a dual role, being essential in both initiating and resolving inflammation by inducing macrophages to transition into an immunosuppressed state. ZEB1 mediates these diverging effects in inflammation and immunosuppression by modulating mitochondrial content through activation of autophagy and inhibition of mitochondrial protein translation. During the transition from inflammation to immunosuppression, Metformin mimics the metabolic reprogramming of myeloid cells induced by ZEB1. Mechanistically, in immunosuppression, ZEB1 inhibits amino acid uptake, leading to downregulation of mTORC1 signalling and a decrease in mitochondrial translation in macrophages. These results identify ZEB1 as a driver of myeloid cell metabolic plasticity, suggesting that targeting its expression and function could serve as a strategy to modulate dysregulated inflammation and immunosuppression.</jats:p

Inflammatory macrophages reprogram to immunosuppression by reducing mitochondrial translation

Acknowledgements: We are grateful to Dr. F. Sanchez-Madrid (Hospital Princesa and CNIC, Madrid, Spain), Dr. D. Cebrian (CNIC, Madrid, Spain), and Dr. A. Valledor (University of Barcelona, Spain) for helpful insights on early versions of the manuscript. We thank Dr. C. Stephan-Otto Attolini (BIST-IRB, Barcelona, Spain) and Dr. J. Rios (IDIBAPS, Hospital Clinic, and Autonomous University of Barcelona, Barcelona, Spain) for their expert guidance on the statistical analyses of the data in the study. We also thank Dr. L. Ribas de Pouplana (BIST-IRB, Barcelona, Spain) for advice on mitochondrial translation experiments. We acknowledge technical assistance by staff in the Flow Cytometry Unit at IDIBAPS, the Molecular Interactions Services Unit at the Biomedical Research Institute of Bellvitge (IDIBELL), and the Transmission Electron Microscopy Unit at the University of Barcelona School of Medicine. We also thank A Téllez (Hospital Clinic, Barcelona, Spain) for his help in collecting samples from septic patients, and Dr. MJ Fernández-Aceñero (Hospital Clinico San Carlos, Madrid, Spain) for help in collecting skin samples from healthy controls, psoriatic patients, and melanoma patients. We are also thankful to Dr. DC Dean (University of Louisville, KY, USA) for his generous gift of an anti-ZEB1 polyclonal antibody. We thank Dr. A. Garcia for the artistic drawing of schematics in the article. IDIBAPS is partly funded by the CERCA Programme of Generalitat de Catalunya. The study was conducted at IDIBAPS’ Centre de Recerca Biomèdica Cellex building, which was partly funded by the Cellex Foundation. The different parts of this study were independently funded by grants to AP from the Leo Foundation (LF-OC-19-000166), the Catalan Agency for Management of University and Research Grants (AGAUR) (2017-SGR-1174 and 2021-SGR-01328), and the Spanish State Research Agency (AEI) of the Ministry of Science and Innovation (MICINN) (PID2020-116338RB-I00) as part of MICINN’s National Scientific and Technical Research and Innovation 2021-2023 Plan, which is co-financed by the European Regional Development Fund (ERDF) of the European Union Commission. AB is a recipient of a PhD scholarship from AGAUR (FI Program, 2021 FI_B 00514).Funder: Government of Catalonia | Agència de Gestió d&apos;Ajuts Universitaris i de Recerca (Agency for Management of University and Research Grants)Acute inflammation can either resolve through immunosuppression or persist, leading to chronic inflammation. These transitions are driven by distinct molecular and metabolic reprogramming of immune cells. The anti-diabetic drug Metformin inhibits acute and chronic inflammation through mechanisms still not fully understood. Here, we report that the anti-inflammatory and reactive-oxygen-species-inhibiting effects of Metformin depend on the expression of the plasticity factor ZEB1 in macrophages. Using mice lacking Zeb1 in their myeloid cells and human patient samples, we show that ZEB1 plays a dual role, being essential in both initiating and resolving inflammation by inducing macrophages to transition into an immunosuppressed state. ZEB1 mediates these diverging effects in inflammation and immunosuppression by modulating mitochondrial content through activation of autophagy and inhibition of mitochondrial protein translation. During the transition from inflammation to immunosuppression, Metformin mimics the metabolic reprogramming of myeloid cells induced by ZEB1. Mechanistically, in immunosuppression, ZEB1 inhibits amino acid uptake, leading to downregulation of mTORC1 signalling and a decrease in mitochondrial translation in macrophages. These results identify ZEB1 as a driver of myeloid cell metabolic plasticity, suggesting that targeting its expression and function could serve as a strategy to modulate dysregulated inflammation and immunosuppression

Linking cell dynamics with gene coexpression networks to characterize key events in chronic virus infections

The host immune response against infection requires the coordinated action of many diverse cell subsets that dynamically adapt to a pathogen threat. Due to the complexity of such a response, most immunological studies have focused on a few genes, proteins, or cell types. With the development of "omic"-technologies and computational analysis methods, attempts to analyze and understand complex system dynamics are now feasible. However, the decomposition of transcriptomic data sets generated from complete organs remains a major challenge. Here, we combined Weighted Gene Coexpression Network Analysis (WGCNA) and Digital Cell Quantifier (DCQ) to analyze time-resolved mouse splenic transcriptomes in acute and chronic Lymphocytic Choriomeningitis Virus (LCMV) infections. This enabled us to generate hypotheses about complex immune functioning after a virus-induced perturbation. This strategy was validated by successfully predicting several known immune phenomena, such as effector cytotoxic T lymphocyte (CTL) expansion and exhaustion. Furthermore, we predicted and subsequently verified experimentally macrophage-CD8 T cell cooperativity and the participation of virus-specific CD8+ T cells with an early effector transcriptome profile in the host adaptation to chronic infection. Thus, the linking of gene expression changes with immune cell kinetics provides novel insights into the complex immune processes within infected tissues.This work is supported by a grant from the Spanish Ministry of Economy, Industry and Competitiveness and FEDER grant no. SAF2016-75505-R (AEI/MINEICO/FEDER, UE) and the María de Maeztu Programme for Units of Excellence in R&D (MDM-2014-0370). GB and AM are also supported by the Russian Science Foundation (grant 18-11-00171). AE-C and SH are also supported by Instituto de Salud Carlos III (ISCIII) grant from the Spanish Ministry of Economy, Industry and Competitiveness and FEDER grant no. PT17/0009/0019. IG-V is supported by European Research Council (637885