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122 results on '"Ortiz-de-Solórzano, Carlos"'

3. The Cell Tracking Challenge: 10 years of objective benchmarking

Author

Maška, Martin, Ulman, Vladimír, Delgado-Rodriguez, Pablo, Gómez-de-Mariscal, Estibaliz, Nečasová, Tereza, Guerrero Peña, Fidel A., Ren, Tsang Ing, Meyerowitz, Elliot M., Scherr, Tim, Löffler, Katharina, Mikut, Ralf, Guo, Tianqi, Wang, Yin, Allebach, Jan P., Bao, Rina, Al-Shakarji, Noor M., Rahmon, Gani, Toubal, Imad Eddine, Palaniappan, Kannappan, Lux, Filip, Matula, Petr, Sugawara, Ko, Magnusson, Klas E. G., Aho, Layton, Cohen, Andrew R., Arbelle, Assaf, Ben-Haim, Tal, Raviv, Tammy Riklin, Isensee, Fabian, Jäger, Paul F., Maier-Hein, Klaus H., Zhu, Yanming, Ederra, Cristina, Urbiola, Ainhoa, Meijering, Erik, Cunha, Alexandre, Muñoz-Barrutia, Arrate, Kozubek, Michal, and Ortiz-de-Solórzano, Carlos

Published
2023
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4. NaroNet: Discovery of tumor microenvironment elements from highly multiplexed images

Author

Jiménez-Sánchez, Daniel, Ariz, Mikel, Chang, Hang, Matias-Guiu, Xavier, de Andrea, Carlos E., and Ortiz-de-Solórzano, Carlos

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Quantitative Biology - Quantitative Methods, I.4.9
Abstract

Many efforts have been made to discover tumor-specific microenvironment elements (TMEs) from immunostained tissue sections. However, the identification of yet unknown but relevant TMEs from multiplex immunostained tissues remains a challenge, due to the number of markers involved (tens) and the complexity of their spatial interactions. We present NaroNet, which uses machine learning to identify and annotate known as well as novel TMEs from self-supervised embeddings of cells, organized at different levels (local cell phenotypes and cellular neighborhoods). Then it uses the abundance of TMEs to classify patients based on biological or clinical features. We validate NaroNet using synthetic patient cohorts with adjustable incidence of different TMEs and two cancer patient datasets. In both synthetic and real datasets, NaroNet unsupervisedly identifies novel TMEs, relevant for the user-defined classification task. As NaroNet requires only patient-level information, it renders state-of-the-art computational methods accessible to a broad audience, accelerating the discovery of biomarker signatures., Comment: 37 pages, 4 figures

Published
2021

5. Synplex: A synthetic simulator of highly multiplexed histological images

Author

Jiménez-Sánchez, Daniel, Ariz, Mikel, and Ortiz-de-Solórzano, Carlos

Subjects
Computer Science - Computer Vision and Pattern Recognition, Quantitative Biology - Quantitative Methods, Quantitative Biology - Tissues and Organs, I.6.3
Abstract

Multiplex tissue immunostaining is a technology of growing relevance as it can capture in situ the complex interactions existing between the elements of the tumor microenvironment. The existence and availability of large, annotated image datasets is key for the objective development and benchmarking of bioimage analysis algorithms. Manual annotation of multiplex images, is however, laborious, often impracticable. In this paper, we present Synplex, a simulation system able to generate multiplex immunostained in situ tissue images based on user-defined parameters. This includes the specification of structural attributes, such as the number of cell phenotypes, the number and level of expression of cellular markers, or the cell morphology. Synplex consists of three sequential modules, each being responsible for a separate task: modeling of cellular neighborhoods, modeling of cell phenotypes, and synthesis of realistic cell/tissue textures. Synplex flexibility and accuracy are demonstrated qualitatively and quantitatively by generating synthetic tissues that simulate disease paradigms found in the real scenarios. Synplex is publicly available for scientific purposes, and we believe it will become a valuable tool for the training and/or validation of multiplex image analysis algorithms., Comment: 17 pages, 6 figures

Published
2021

8. Automatic Segmentation and Quantification of Nigrosome‐1 Neuromelanin and Iron in MRI: A Candidate Biomarker for Parkinson's Disease.

Author

Ariz, Mikel, Martínez, Martín, Alvarez, Ignacio, Fernández‐Seara, Maria A., Castellanos, Gabriel, Pastor, Pau, Pastor, Maria A., and Ortiz de Solórzano, Carlos

Subjects
PARKINSON'S disease, IRON, MAGNETIZATION transfer, LOGISTIC regression analysis, BIOMARKERS
Abstract

Background: There is a lack of automated tools for the segmentation and quantification of neuromelanin (NM) and iron in the nigrosome‐1 (N1). Existing tools evaluate the N1 sign, i.e., the presence or absence of the "swallow‐tail" in iron‐sensitive MRI, or globally analyze the MRI signal in an area containing the N1, without providing a volumetric delineation. Purpose: Present an automated method to segment the N1 and quantify differences in N1's NM and iron content between Parkinson's disease (PD) patients and healthy controls (HCs). Study whether N1 degeneration is clinically related to PD and could be used as a biomarker of the disease. Study Type: Prospective. Subjects: Seventy‐one PD (65.3 ± 10.3 years old, 34 female/37 male); 30 HC (62.7 ± 7.8 years old, 17 female/13 male). Field Strength/Sequence: 3 T Anatomical T1‐weighted MPRAGE, NM‐MRI T1‐weighted gradient with magnetization transfer, susceptibility‐weighted imaging (SWI). Assessment: N1 was automatically segmented in SWI images using a multi‐image atlas, populated with healthy N1 structures manually annotated by a neurologist. Relative NM and iron content were quantified and their diagnostic performance assessed and compared with the substantia nigra pars compacta (SNc). The association between image parameters and clinically relevant variables was studied. Statistical Tests: Nonparametric tests were used (Mann–Whitney's U, chi‐square, and Friedman tests) at P = 0.05. Results: N1's relative NM content decreased and relative iron content increased in PD patients compared with HCs (NM‐CRHC = 22.55 ± 1.49; NM‐CRPD = 19.79 ± 1.92; NM‐nVolHC = 2.69 × 10−5 ± 1.02 × 10−5; NM‐nVolPD = 1.18 × 10−5 ± 0.96 × 10−5; Iron‐CRHC = 10.51 ± 2.64; Iron‐CRPD = 19.35 ± 7.88; Iron‐nVolHC = 0.72 × 10−5 ± 0.81 × 10−5; Iron‐nVolPD = 2.82 × 10−5 ± 2.04 × 10−5). Binary logistic regression analyses combining N1 and SNc image parameters yielded a top AUC = 0.955. Significant correlation was found between most N1 parameters and both disease duration (ρNM‐CR = −0.31; ρiron‐CR = 0.43; ρiron‐nVol = 0.46) and the motor status (ρNM‐nVol = −0.27; ρiron‐CR = 0.33; ρiron‐nVol = 0.28), suggesting NM reduction along with iron accumulation in N1 as the disease progresses. Data Conclusion: This method provides a fully automatic N1 segmentation, and the analyses performed reveal that N1 relative NM and iron quantification improves diagnostic performance and suggest a relative NM reduction along with a relative iron accumulation in N1 as the disease progresses. Evidence Level: 1 Technical Efficacy: Stage 1 [ABSTRACT FROM AUTHOR]

Published
2024
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9. In vitro modeling of polyclonal infection dynamics within the human airways by Haemophilus influenzae differential fluorescent labeling

Author

Rapún-Araiz, Beatriz, primary, Sorzabal-Bellido, Ioritz, additional, Asensio-López, Javier, additional, Lázaro-Díez, María, additional, Ariz, Mikel, additional, Sobejano de la Merced, Carlos, additional, Euba, Begoña, additional, Fernández-Calvet, Ariadna, additional, Cortés-Domínguez, Ivan, additional, Burgui, Saioa, additional, Toledo-Arana, Alejandro, additional, Ortiz-de-Solórzano, Carlos, additional, and Garmendia, Junkal, additional

Published
2023
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10. Targeting aberrant DNA methylation in mesenchymal stromal cells as a treatment for myeloma bone disease

Author

Garcia-Gomez, Antonio, Li, Tianlu, de la Calle-Fabregat, Carlos, Rodríguez-Ubreva, Javier, Ciudad, Laura, Català-Moll, Francesc, Godoy-Tena, Gerard, Martín-Sánchez, Montserrat, San-Segundo, Laura, Muntión, Sandra, Morales, Xabier, Ortiz-de-Solórzano, Carlos, Oyarzabal, Julen, San José-Enériz, Edurne, Esteller, Manel, Agirre, Xabier, Prosper, Felipe, Garayoa, Mercedes, and Ballestar, Esteban

Published
2021
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12. Data from Tumor ENPP1 (CD203a)/Haptoglobin Axis Exploits Myeloid-Derived Suppressor Cells to Promote Post-Radiotherapy Local Recurrence in Breast Cancer

Author

Ruiz-Fernández de Córdoba, Borja, primary, Moreno, Haritz, primary, Valencia, Karmele, primary, Perurena, Naiara, primary, Ruedas, Pablo, primary, Walle, Thomas, primary, Pezonaga-Torres, Alberto, primary, Hinojosa, Juan, primary, Guruceaga, Elisabet, primary, Pineda-Lucena, Antonio, primary, Abengózar-Muela, Marta, primary, Cochonneau, Denis, primary, Zandueta, Carolina, primary, Martínez-Canarias, Susana, primary, Teijeira, Álvaro, primary, Ajona, Daniel, primary, Ortiz-Espinosa, Sergio, primary, Morales, Xabier, primary, Ortiz de Solórzano, Carlos, primary, Santisteban, Marta, primary, Ramos-García, Luis I., primary, Guembe, Laura, primary, Strnad, Vratislav, primary, Heymann, Dominique, primary, Hervás-Stubbs, Sandra, primary, Pío, Rubén, primary, Rodríguez-Ruiz, María E., primary, de Andrea, Carlos E., primary, Vicent, Silvestre, primary, Melero, Ignacio, primary, Lecanda, Fernando, primary, and Martínez-Monge, Rafael, primary

Published
2023
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13. Supplementary Data from Tumor ENPP1 (CD203a)/Haptoglobin Axis Exploits Myeloid-Derived Suppressor Cells to Promote Post-Radiotherapy Local Recurrence in Breast Cancer

Author

Ruiz-Fernández de Córdoba, Borja, primary, Moreno, Haritz, primary, Valencia, Karmele, primary, Perurena, Naiara, primary, Ruedas, Pablo, primary, Walle, Thomas, primary, Pezonaga-Torres, Alberto, primary, Hinojosa, Juan, primary, Guruceaga, Elisabet, primary, Pineda-Lucena, Antonio, primary, Abengózar-Muela, Marta, primary, Cochonneau, Denis, primary, Zandueta, Carolina, primary, Martínez-Canarias, Susana, primary, Teijeira, Álvaro, primary, Ajona, Daniel, primary, Ortiz-Espinosa, Sergio, primary, Morales, Xabier, primary, Ortiz de Solórzano, Carlos, primary, Santisteban, Marta, primary, Ramos-García, Luis I., primary, Guembe, Laura, primary, Strnad, Vratislav, primary, Heymann, Dominique, primary, Hervás-Stubbs, Sandra, primary, Pío, Rubén, primary, Rodríguez-Ruiz, María E., primary, de Andrea, Carlos E., primary, Vicent, Silvestre, primary, Melero, Ignacio, primary, Lecanda, Fernando, primary, and Martínez-Monge, Rafael, primary

Published
2023
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14. Supplementary Figure from Tumor ENPP1 (CD203a)/Haptoglobin Axis Exploits Myeloid-Derived Suppressor Cells to Promote Post-Radiotherapy Local Recurrence in Breast Cancer

Author

Ruiz-Fernández de Córdoba, Borja, primary, Moreno, Haritz, primary, Valencia, Karmele, primary, Perurena, Naiara, primary, Ruedas, Pablo, primary, Walle, Thomas, primary, Pezonaga-Torres, Alberto, primary, Hinojosa, Juan, primary, Guruceaga, Elisabet, primary, Pineda-Lucena, Antonio, primary, Abengózar-Muela, Marta, primary, Cochonneau, Denis, primary, Zandueta, Carolina, primary, Martínez-Canarias, Susana, primary, Teijeira, Álvaro, primary, Ajona, Daniel, primary, Ortiz-Espinosa, Sergio, primary, Morales, Xabier, primary, Ortiz de Solórzano, Carlos, primary, Santisteban, Marta, primary, Ramos-García, Luis I., primary, Guembe, Laura, primary, Strnad, Vratislav, primary, Heymann, Dominique, primary, Hervás-Stubbs, Sandra, primary, Pío, Rubén, primary, Rodríguez-Ruiz, María E., primary, de Andrea, Carlos E., primary, Vicent, Silvestre, primary, Melero, Ignacio, primary, Lecanda, Fernando, primary, and Martínez-Monge, Rafael, primary

Published
2023
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15. Modelling polyclonal infection dynamics within the human airways by haemophilus influenzae differntial fluorescent labelling

Author

Rapún-Araiz, Beatriz, Sorzabal-Bellido, Ioritz, Asensio-López, Javier, Lázaro-Díez, María, Ariz, Mikel, Sobejano de la Merced, Carlos, Burgui, Saioa, Toledo-Arana, Alejandro, Ortiz-de-Solórzano, Carlos, Garmendia, Juncal, Rapún-Araiz, Beatriz, Sorzabal-Bellido, Ioritz, Asensio-López, Javier, Lázaro-Díez, María, Ariz, Mikel, Sobejano de la Merced, Carlos, Burgui, Saioa, Toledo-Arana, Alejandro, Ortiz-de-Solórzano, Carlos, and Garmendia, Juncal

Abstract

Nontypeable Haemophilus infuenzae (NTHi) is a human-adapted bacterial pathogen causing airway infections. NTHi is an intracellular facultative pathogen, capable of both forming bioflms and invading airway epithelial cells, a combination of traits likely contributing to persistence. Standardised clinical procedures for antibiotic administration rely on pathogen identifcation and antibiotic susceptibility testing, often performed on single-colony bacterial isolates. For respiratory pathogens, this could be questionable as chronic patients may be persistently colonised by multiple clones. Moreover, diferences in the susceptibility of the diferent strains present within the same isolate have been reported for a variety of antibiotics. In vitro polyclonal infections can be assessed at the single cell level using bacterial strains engineered for diferential labelling. However, appropriate genetic tools amenable for H. infuenzae are currently limited. Here, we designed a novel plasmid toolkit named pTBH (toolbox for Haemophilus) for engineering H. infuenzae, with functional components (cargo module, two selectable markers, and a replication origin) assembled in modular combinations to create standardised and versatile plasmids. Using this structure, six reporter genes for fuorescent or bioluminescent labelling, expressed from a constitutive promoter, were independently assembled. This plasmid set was introduced into two phenotypically divergent H. infuenzae strains, and the engineered strains were used to analyse mixed bioflm growth architecture, antibiotic efcacy, and to visualise the dynamics of alveolar epithelial co-infection. The mixed bioflms showed a bilayer architecture and selective antibiotic efcacy associated to the respective susceptibility profles of their members. Furthermore, diferential kinetics of bacterial intracellular location within subcellular acidic compartments was quantifed upon co-infection of cultured airway epithelial cells by quantitative image

Published
2023

16. In vitro modeling of polyclonal infection dynamics within the human airways by Haemophilus influenzae differential fluorescent labeling

Author

Diputación Foral de Navarra, Ministerio de Ciencia e Innovación (España), Ministerio de Ciencia, Innovación y Universidades (España), Agencia Estatal de Investigación (España), European Commission, Instituto de Salud Carlos III, Garmendia, Juncal [0000-0002-7440-2737], Rapún-Araiz, Beatriz, Sorzabal-Bellido, Ioritz, Asensio-López, Javier, Lázaro-Díez, María, Ariz, Mikel, Sobejano de la Merced, Carlos, Euba, Begoña, Fernández-Calvet, Ariadna, Cortés-Domínguez, Ivan, Burgui, Saioa, Toledo-Arana, Alejandro, Ortiz-de-Solórzano, Carlos, Garmendia, Juncal, Diputación Foral de Navarra, Ministerio de Ciencia e Innovación (España), Ministerio de Ciencia, Innovación y Universidades (España), Agencia Estatal de Investigación (España), European Commission, Instituto de Salud Carlos III, Garmendia, Juncal [0000-0002-7440-2737], Rapún-Araiz, Beatriz, Sorzabal-Bellido, Ioritz, Asensio-López, Javier, Lázaro-Díez, María, Ariz, Mikel, Sobejano de la Merced, Carlos, Euba, Begoña, Fernández-Calvet, Ariadna, Cortés-Domínguez, Ivan, Burgui, Saioa, Toledo-Arana, Alejandro, Ortiz-de-Solórzano, Carlos, and Garmendia, Juncal

Abstract

Genomic diversity of nontypeable H. influenzae strains confers phenotypic heterogeneity. Multiple strains of H. influenzae can be simultaneously isolated from clinical specimens, but we lack detailed information about polyclonal infection dynamics by this pathogen. A long-term barrier to our understanding of this host-pathogen interplay is the lack of genetic tools for strain engineering and differential labeling. Here, we present a novel plasmid toolkit named pTBH (toolbox for Haemophilus), with standardized modules for fluorescent or bioluminescent labeling, adapted to H. influenzae requirements but designed to be versatile so it can be utilized in other bacterial species. We present detailed experimental and quantitative image analysis methods, together with proof-of-principle examples, and show the ample possibilities of 3D microscopy, combined with quantitative image analysis, to model H. influenzae polyclonal infection lifestyles and unravel the co-habitation and co-infection dynamics of this respiratory pathogen.

Published
2023

17. 1298 A weakly supervised deep learning framework to predict KRAS-STK11 and KRAS-TP53 co-mutations in lung adenocarcinomas using H&E tissue sections

Author

Jiménez-Sánchez, Daniel, primary, Andrea, Carlos De, additional, López-Janeiro, Álvaro, additional, Lozano, Maria D, additional, Ortiz-de-Solórzano, Carlos, additional, Romano-Martínez, Aida, additional, Sainz, Cristina, additional, Montuenga, Luis, additional, Melero, Ignacio, additional, and Pio, Ruben, additional

Published
2022
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18. GLUT1 ablation in astrocytes paradoxically improves central and peripheral glucose metabolism via enhanced insulin-stimulated ATP release

Author

Ardanaz, Carlos G., primary, de la Cruz, Aida, additional, Elizalde-Horcada, Marcos, additional, Puerta, Elena, additional, Ramírez, María J., additional, Ortega, Jorge E., additional, Urbiola, Ainhoa, additional, Ederra, Cristina, additional, Ariz, Mikel, additional, Ortiz-de-Solórzano, Carlos, additional, Irigoyen, Joaquín Fernández-, additional, Santamaría, Enrique, additional, Karsenty, Gerard, additional, Brüning, Jens C., additional, and Solas, Maite, additional

Published
2022
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19. Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study

Author

Rudyanto, Rina D., Kerkstra, Sjoerd, van Rikxoort, Eva M., Fetita, Catalin, Brillet, Pierre-Yves, Lefevre, Christophe, Xue, Wenzhe, Zhu, Xiangjun, Liang, Jianming, Öksüz, İlkay, Ünay, Devrim, Kadipaşaogˇlu, Kamuran, Estépar, Raúl San José, Ross, James C., Washko, George R., Prieto, Juan-Carlos, Hoyos, Marcela Hernández, Orkisz, Maciej, Meine, Hans, Hüllebrand, Markus, Stöcker, Christina, Mir, Fernando Lopez, Naranjo, Valery, Villanueva, Eliseo, Staring, Marius, Xiao, Changyan, Stoel, Berend C., Fabijanska, Anna, Smistad, Erik, Elster, Anne C., Lindseth, Frank, Foruzan, Amir Hossein, Kiros, Ryan, Popuri, Karteek, Cobzas, Dana, Jimenez-Carretero, Daniel, Santos, Andres, Ledesma-Carbayo, Maria J., Helmberger, Michael, Urschler, Martin, Pienn, Michael, Bosboom, Dennis G.H., Campo, Arantza, Prokop, Mathias, de Jong, Pim A., Ortiz-de-Solorzano, Carlos, Muñoz-Barrutia, Arrate, and van Ginneken, Bram

Published
2014
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20. Segmentation of Touching Cell Nuclei Using a Two-Stage Graph Cut Model

Author

Daněk, Ondřej, Matula, Pavel, Ortiz-de-Solórzano, Carlos, Muñoz-Barrutia, Arrate, Maška, Martin, Kozubek, Michal, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Salberg, Arnt-Børre, editor, Hardeberg, Jon Yngve, editor, and Jenssen, Robert, editor

Published
2009
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21. A Two-Phase Segmentation of Cell Nuclei Using Fast Level Set-Like Algorithms

Author

Maška, Martin, Daněk, Ondřej, Ortiz-de-Solórzano, Carlos, Muñoz-Barrutia, Arrate, Kozubek, Michal, García, Ignacio Fernández, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Salberg, Arnt-Børre, editor, Hardeberg, Jon Yngve, editor, and Jenssen, Robert, editor

Published
2009
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22. Consistent and Elastic Registration of Histological Sections Using Vector-Spline Regularization

Author

Arganda-Carreras, Ignacio, Sorzano, Carlos O. S., Marabini, Roberto, Carazo, José María, Ortiz-de-Solorzano, Carlos, Kybic, Jan, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Beichel, Reinhard R., editor, and Sonka, Milan, editor

Published
2006
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27. Design and validation of a tunable inertial microfluidic system for the efficient enrichment of circulating tumor cells in blood

Author

Rodríguez‐Pena, Alejandro, primary, Armendariz, Estibaliz, additional, Oyarbide, Alvaro, additional, Morales, Xabier, additional, Ortiz‐Espinosa, Sergio, additional, Ruiz‐Fernández de Córdoba, Borja, additional, Cochonneau, Denis, additional, Cornago, Iñaki, additional, Heymann, Dominique, additional, Argemi, Josepmaría, additional, D'Avola, Delia, additional, Sangro, Bruno, additional, Lecanda, Fernando, additional, Pio, Ruben, additional, Cortés‐Domínguez, Iván, additional, and Ortiz‐de‐Solórzano, Carlos, additional

Published
2022
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28. Tumor ENPP1 (CD203a)/Haptoglobin Axis Exploits Myeloid-Derived Suppressor Cells to Promote Post-Radiotherapy Local Recurrence in Breast Cancer

Author

Ruiz-Fernández de Córdoba, Borja, primary, Moreno, Haritz, additional, Valencia, Karmele, additional, Perurena, Naiara, additional, Ruedas, Pablo, additional, Walle, Thomas, additional, Pezonaga-Torres, Alberto, additional, Hinojosa, Juan, additional, Guruceaga, Elisabet, additional, Pineda-Lucena, Antonio, additional, Abengózar-Muela, Marta, additional, Cochonneau, Denis, additional, Zandueta, Carolina, additional, Martínez-Canarias, Susana, additional, Teijeira, Álvaro, additional, Ajona, Daniel, additional, Ortiz-Espinosa, Sergio, additional, Morales, Xabier, additional, Ortiz de Solórzano, Carlos, additional, Santisteban, Marta, additional, Ramos-García, Luis I., additional, Guembe, Laura, additional, Strnad, Vratislav, additional, Heymann, Dominique, additional, Hervás-Stubbs, Sandra, additional, Pío, Rubén, additional, Rodríguez-Ruiz, María E., additional, de Andrea, Carlos E., additional, Vicent, Silvestre, additional, Melero, Ignacio, additional, Lecanda, Fernando, additional, and Martínez-Monge, Rafael, additional

Published
2022
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29. NaroNet: Discovery of tumor microenvironment elements from highly multiplexed images

Author

Jiménez-Sánchez, Daniel, Ariz, Mikel, Chang, Hang, Matias-Guiu, Xavier, de Andrea, Carlos E., and Ortiz-de-Solórzano, Carlos

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Cancer cells, Pronòstic mèdic, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, Breast Neoplasms, Health Informatics, Multiplex imaging, Quantitative Biology - Quantitative Methods, Machine Learning (cs.LG), Imaging mass cytometry, Tumor Microenvironment, Humans, Radiology, Nuclear Medicine and imaging, Spatial biology, Quantitative Methods (q-bio.QM), Tumors, Interpretable machine learning, Radiological and Ultrasound Technology, Weakly supervised learning, Self supervised learning, Deep learning, Prognosis, Computer Graphics and Computer-Aided Design, Diagnòstic per la imatge, Tumor microenvironment, FOS: Biological sciences, Diagnostic imaging, Female, Cèl·lules canceroses, I.4.9, Computer Vision and Pattern Recognition, Cellular neighborhoods
Abstract

Many efforts have been made to discover tumor-specific microenvironment elements (TMEs) from immunostained tissue sections. However, the identification of yet unknown but relevant TMEs from multiplex immunostained tissues remains a challenge, due to the number of markers involved (tens) and the complexity of their spatial interactions. We present NaroNet, which uses machine learning to identify and annotate known as well as novel TMEs from self-supervised embeddings of cells, organized at different levels (local cell phenotypes and cellular neighborhoods). Then it uses the abundance of TMEs to classify patients based on biological or clinical features. We validate NaroNet using synthetic patient cohorts with adjustable incidence of different TMEs and two cancer patient datasets. In both synthetic and real datasets, NaroNet unsupervisedly identifies novel TMEs, relevant for the user-defined classification task. As NaroNet requires only patient-level information, it renders state-of-the-art computational methods accessible to a broad audience, accelerating the discovery of biomarker signatures., Comment: 37 pages, 4 figures

Published
2022

33. Muscular and Tendon Degeneration after Achilles Rupture: New Insights into Future Repair Strategies

Author

Gil-Melgosa, Lara, primary, Grasa, Jorge, additional, Urbiola, Ainhoa, additional, Llombart, Rafael, additional, Susaeta Ruiz, Miguel, additional, Montiel, Verónica, additional, Ederra, Cristina, additional, Calvo, Begoña, additional, Ariz, Mikel, additional, Ripalda-Cemborain, Purificación, additional, Prosper, Felipe, additional, Ortiz-de-Solórzano, Carlos, additional, Pons-Villanueva, Juan, additional, and Pérez Ruiz, Ana, additional

Published
2021
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39. Preclinical Evaluation of the Antimicrobial-Immunomodulatory Dual Action of Xenohormetic Molecules against Haemophilus influenzae Respiratory Infection

Author

Fernández-Calvet, Ariadna, primary, Euba, Begoña, additional, Caballero, Lucía, additional, Díez-Martínez, Roberto, additional, Menéndez, Margarita, additional, Ortiz de Solórzano, Carlos, additional, Leiva, José, additional, Micol, Vicente, additional, Barrajón-Catalán, Enrique, additional, and Garmendia, Junkal, additional

Published
2019
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41. Targeting aberrant DNA methylation in mesenchymal stromal cells as a treatment for myeloma bone disease

Author

Garcia-Gomez, Antonio, primary, Li, Tianlu, additional, Rodríguez-Ubreva, Javier, additional, Ciudad, Laura, additional, Català-Moll, Francesc, additional, Martín-Sánchez, Montserrat, additional, San-Segundo, Laura, additional, Morales, Xabier, additional, Ortiz de Solórzano, Carlos, additional, Oyarzabal, Julen, additional, San José-Enériz, Edurne, additional, Agirre, Xabier, additional, Prosper, Felipe, additional, Garayoa, Mercedes, additional, and Ballestar, Esteban, additional

Published
2019
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42. NMF-RI: blind spectral unmixing of highly mixed multispectral flow and image cytometry data.

Author

Jiménez-Sánchez, Daniel, Ariz, Mikel, Morgado, José Mário, Cortés-Domínguez, Iván, and Ortiz-de-Solórzano, Carlos

Subjects
MULTISPECTRAL imaging, FLOW cytometry, SIGNAL-to-noise ratio, NONNEGATIVE matrices, MATRIX decomposition
Abstract

Motivation Recent advances in multiplex immunostaining and multispectral cytometry have opened the door to simultaneously visualizing an unprecedented number of biomarkers both in liquid and solid samples. Properly unmixing fluorescent emissions is a challenging task, which normally requires the characterization of the individual fluorochromes from control samples. As the number of fluorochromes increases, the cost in time and use of reagents becomes prohibitively high. Here, we present a fully unsupervised blind spectral unmixing method for the separation of fluorescent emissions in highly mixed spectral data, without the need for control samples. To this end, we extend an existing method based on non-negative Matrix Factorization, and introduce several critical improvements: initialization based on the theoretical spectra, automated selection of 'sparse' data and use of a re-initialized multilayer optimizer. Results Our algorithm is exhaustively tested using synthetic data to study its robustness against different levels of colocalization, signal to noise ratio, spectral resolution and the effect of errors in the initialization of the algorithm. Then, we compare the performance of our method to that of traditional spectral unmixing algorithms using novel multispectral flow and image cytometry systems. In all cases, we show that our blind unmixing algorithm performs robust unmixing of highly spatially and spectrally mixed data with an unprecedently low computational cost. In summary, we present the first use of a blind unmixing method in multispectral flow and image cytometry, opening the door to the widespread use of our method to efficiently pre-process multiplex immunostaining samples without the need of experimental controls. Availability and implementation https://github.com/djimenezsanchez/Blind%5fUnmixing%5fNMF%5fRI/ contains the source code and all datasets used in this manuscript. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Published
2020
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45. Automated Neuromelanin Imaging as a Diagnostic Biomarker for Parkinson's Disease

Author

Castellanos, Gabriel, Fernández-Seara, María A., Lorenzo-Betancor, Oswaldo, Ortega-Cubero, Sara, Puigvert, Marc, Uranga, Javier, Vidorreta, Marta, Irigoyen, Jaime, Lorenzo, Elena, Muñoz Barrutia, María Arrate, Ortiz de Solórzano, Carlos, Pastor, Pau, and Pastor, María A.

Subjects
Magnetic resonance imaging, Parkinson's, Medicina, Diagnosis, Substantia nigra, Neuromelanin, Funding, Biología y Biomedicina
Abstract

Published online We aimed to analyze the diagnostic accuracy of an automated segmentation and quantification method of the SNc and locus coeruleus (LC) volumes based on neuromelanin (NM)-sensitive MRI (NM-MRI) in patients with idiopathic (iPD) and monogenic (iPD) Parkinson's disease (PD). Thirty-six patients (23 idiopathic and 13 monogenic PARKIN or LRRK2 mutations) and 37 age-matched healthy controls underwent 3T-NM-MRI. SNc and LC volumetry were performed using fully automated multi-image atlas segmentation. The diagnostic performance to differentiate PD from controls was measured using the area under the curve (AUC) and likelihood ratios based on receiver operating characteristic (ROC) analyses. We found a significant reduction of SNc and LC volumes in patients, when compared to controls. ROC analysis showed better diagnostic accuracy when using SNc volume than LC volume. Significant differences between ipsilateral and contralateral SNc volumes, in relation to the more clinically affected side, were found in patients with iPD (P=0.007). Contralateral atrophy in the SNc showed the highest power to discriminate PD subjects from controls (AUC, 0.93-0.94; sensitivity, 91%-92%; specificity, 89%; positive likelihood ratio: 8.4-8.5; negative likelihood ratio: 0.09-0.1 at a single cut-off point). Interval likelihood ratios for contralateral SNc volume improved the diagnostic accuracy of volumetric measurements. SNc and LC volumetry based on NM-MRI resulting from the automated segmentation and quantification technique can yield high diagnostic accuracy for differentiating PD from health and might be an unbiased disease biomarker. Supported by the Fund for Health Research Foundation (FIS-ISCIII), Spanish Ministry of Economy and Competitiveness (reference: PI 13/02211); Spanish Ministry of Science and Innovation SAF2006-10126 (2006-2009), SAF2010-22329-C02-01 (2011-2013), and SAF2013-47939-R (to P.P.); project 061131 from the “Fundació La Marató de TV3” and by the UTE project FIMA, Spain (to P.P.). C.O.d.S. and A.M.B. were supported by Spanish Ministry of Economy and Competitiveness DPI2012-38090-C03-02 and TEC2013-48552-C2-1-R, respectively. Publicado

Published
2015

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