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Resultados totales (Incluyendo duplicados): 42553
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Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329689
Set de datos (Dataset). 2022

SUPPORTING INFORMATION 2D/2D HETEROJUNCTION OF TIO2 NANOSHEETS / ULTRATHIN G-C3N4 FOR EFFICIENT PHOTOCATALYTIC HYDROGEN EVOLUTION

  • Du, Ruifeng
  • Li, Baoying
  • Han, Xu
  • Xiao, Ke
  • Wang, Xiang
  • Zhang, Chaoqi
  • Arbiol, Jordi
  • Cabot, Andreu
10 pages. -- Figures and tables. -- Figure S1: SEM image of (a) bulk g-C3N4 and (b) ultrathin g-C3N4, (c) N2 adsorption-desorption isotherms of bCN and uCN. -- Figure S2: FTIR spectra of OAC, OLMA and TiO2 before and after ligands remove. -- Figure S3: Zeta potential distribution spectrum of TiO2 after ligands removal (a) and uCN (b). -- Figure S4: SEM image and EDS compositional maps of a T1/uCN1 composite. -- Figure S5: SEM image of T1/uCN2 and corresponding EDS spectrum. -- Figure S6: SEM image of T1/uCN2 and corresponding EDS spectrum. -- Figure S7: SEM image of T1/uCN2 and corresponding EDS spectrum; Figure S8: Chromatogram plots for 0.5 ml of standard hydrogen injected every half hour. -- Table S1: Gas Chromatography Peak Processing Data based on figure S8. -- Figure S9: Standard hydrogen curve for gas chromatography. -- Table S2: Exponential decay-fitted parameters of fluorescence lifetime of uCN, TiO2 and T1/uCN1. -- Figure S10: Photocatalytic hydrogen generation amount on bCN, TiO2 and T1/bCN1 during 4 h under simulated solar light irradiation; Table S3: Photocatalytic hydrogen production about TiO2/g-C3N4 based catalysts. -- Table S4: The AQE values with different incident light wavelengths for T1/uCN1. -- Figure S11: (a) Stability cycles of the T1/uCN1 for H2 evolution under simulated solar light irradiation; (b) TEM image of T1/uCN1 after 20 h photocatalytic H2 evolution reaction and (c) XRD pattern of T1/uCN1 before and after 20 h photocatalytic H2O2 evolution reaction., CN2 is supported by the Severo Ochoa program from Spanish MINECO (Grant No. SEV-2017-0706) and is funded by the CERCAProgramme / Generalitat de Catalunya., Peer reviewed

DOI: http://hdl.handle.net/10261/329689
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329689
HANDLE: http://hdl.handle.net/10261/329689
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329689
PMID: http://hdl.handle.net/10261/329689
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329689
Ver en: http://hdl.handle.net/10261/329689
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329689

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329691
Set de datos (Dataset). 2022

S4 FIG - EVALUATION OF SARS-COV-2 ENTRY, INFLAMMATION AND NEW THERAPEUTICS IN HUMAN LUNG TISSUE CELLS

  • Grau-Expósito, Judith
  • Perea, David
  • Suppi, Marina
  • Massana, Nuria
  • Vergara, Ander
  • Soler, María José
  • Trinité, Benjamin
  • Blanco, Julià
  • García-Pérez, Javier
  • Alcamí, José
  • Serrano-Mollar, Anna
  • Rosado, Joel
  • Falcó, Vicenç
  • Genesca, Meritxell
  • Buzón, María José
A) Percentage of enriched AT-II cells co-expressing the entry factors ACE2 and CD147 (in blue), and ACE2 and TMPRSS2 (in purple). (B) t-distributed Stochastic Neighbor Embedding (tSNE) representation for AXL expression in CD45+ and CD45-EpCAM+ fractions from a representative lung tissue. Right graphs show the percentage of expression of the AXL entry factor in the different cell populations, which were identified as in Fig 1A. (C) Bar plots showing the percentage of viral entry inhibition on HLT cells in the presence of anti-ACE2 antibody (15μg/ml) or recombinant human AXL (50μg/ml) after cell challenge with VSV*ΔG(Luc)-Spike. Data were analyzed by one sample t-test; *p<0.05, **p<0.01. (D) Frequency of each subset relative to live cells at 0h and 24h with and without the presence of virus. All cell subsets were identified as shown in S2A Fig. (E) EC50 values in the HLT model obtained from 3 different lung donors and performed in replicates. (F) Cells from 1 donor were cultured with 20 μM of selected drugs for 48h, and cell toxicity was measured using the CellTiter-Glo Luminescent kit (Promega), following the manufacturer’s instructions. Data was normalized to the untreated control. Mean±SEM is shown for all graphs, Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/329691
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329691
HANDLE: http://hdl.handle.net/10261/329691
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329691
PMID: http://hdl.handle.net/10261/329691
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329691
Ver en: http://hdl.handle.net/10261/329691
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329691

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329692
Set de datos (Dataset). 2022

SUPPLEMENTARY FILES OF THE ARTICLE KSR INDUCES RAS-INDEPENDENT MAPK PATHWAY ACTIVATION AND MODULATES THE EFFICACY OF KRAS INHIBITORS [DATASET]

  • Paniagua, Guillem
  • Jacob, Harrys K.C.
  • Brehey, Oksana
  • García-Alonso, Sara
  • Lechuga, Carmen G.
  • Pons, Tirso
  • Musteanu, Mónica
  • Guerra, Carmen
  • Drosten, Matthias
  • Barbacid, Mariano
Fig. S1. KSR1ΔCA1 localizes to the plasma membrane and binds BRAF. Fig. S2. 3D model of the mKSR1 kinase domain. Fig. S3. Purification of recombinant KSR1 protein. Fig. S4. RAS-independent proliferation in the absence of p53 does not involve KSR. Fig. S5. Western blot analysis of KSR1 expression levels in parental and resistant MIA PaCa-2 (A) as well as PDX-dc1 (B) cell lines. Fig. S6. Model of KSR-driven proliferation in RASless cells., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/329692, https://doi.org/10.20350/digitalCSIC/15373
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329692
HANDLE: http://hdl.handle.net/10261/329692, https://doi.org/10.20350/digitalCSIC/15373
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329692
PMID: http://hdl.handle.net/10261/329692, https://doi.org/10.20350/digitalCSIC/15373
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329692
Ver en: http://hdl.handle.net/10261/329692, https://doi.org/10.20350/digitalCSIC/15373
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329692

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329695
Set de datos (Dataset). 2022

SUPPORTING INFORMATION FOR FIGURES. INEQUALITIES IN COVID-19 INEQUALITIES RESEARCH: WHO HAD THE CAPACITY TO RESPOND?

  • Benach, Joan
  • Cash-Gibson, Lucinda
  • Rojas-Gualdrón, Diego F.
  • Padilla-Pozo, Álvaro
  • Fernández-Gracia, Juan
  • Eguíluz, Víctor M.
Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/329695
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329695
HANDLE: http://hdl.handle.net/10261/329695
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329695
PMID: http://hdl.handle.net/10261/329695
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329695
Ver en: http://hdl.handle.net/10261/329695
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329695

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329699
Set de datos (Dataset). 2022

SUPPLEMENTARY FILES OF THE ARTICLE MUTATIONAL SCREENING OF THE TPO AND DUOX2 GENES IN ARGENTINIAN CHILDREN WITH CONGENITAL HYPOTHYROIDISM DUE TO THYROID DYSHORMONOGENESIS [DATASET]

  • Molina, Maricel F.
  • Papendieck, Patricia
  • Sobrero, Gabriela
  • Balbi, Viviana A.
  • Belforte, Fiorella S.
  • Bueno, Elena
  • Adrover, Ezequiela
  • Olcese, María C.
  • Chiesa, Ana
  • Miras, Mirta B.
  • González, Verónica G.
  • Gomes Pio, Mauricio
  • González-Sarmiento, Rogelio
  • Targovnik, Héctor M.
  • Rivolta, Carina M.
Supplementary Notes 1: Case Reports. Supplementary Notes 2: 3D modeling analysis of the identified Thyroid Peroxidase (TPO), Dual Oxidase 2 (DUOX2) and Iodothyrosine Deiodinase I IYD variants. Supplementary Table 1. Human thyroid peroxidase, dual oxidase 2 and iodothyrosine deiodinase missense variants identified and their analysis with amino acid substitution prediction tools. Supplementary Table 2. Identification of human Thyroid Peroxidase (TPO) variants by Next-Generation Sequencing (NGS). Supplementary Table 3. Identification of human Dual Oxidase 2 (DUOX2) variants by Next-Generation Sequencing (NGS). Supplementary Figure 1. Differential diagnosis in patients with thyroid dyshormonogenesis. Supplementary Figure 2. Partial protein alignment of the Homo sapiens, Camelus dromedarius, Rattus norvegicus, Mus musculus, Culex quinquefasciatus, Sus scrofa, Lynx Canadensis, Xenopus tropicalis and Canis lupus familiaris thyroid peroxidase (TPO) species. Supplementary Figure 3. Partial protein alignment of the Homo sapiens, Mustela erminea, Sus scrofa, Mus musculus, Lynx canadensis, Bos taurus, Gallus gallus, Pan troglodytes dual oxidase 2 (DUOX2) species., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/329699
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329699
HANDLE: http://hdl.handle.net/10261/329699
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329699
PMID: http://hdl.handle.net/10261/329699
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329699
Ver en: http://hdl.handle.net/10261/329699
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329699

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329705
Set de datos (Dataset). 2022

GATING STRATEGY FOR THE IDENTIFICATION OF CELL SUBPOPULATIONS IN THE HUMAN LUNG TISSUE MODEL

  • Grau-Expósito, Judith
  • Perea, David
  • Suppi, Marina
  • Massana, Nuria
  • Vergara, Ander
  • Soler, María José
  • Trinité, Benjamin
  • Blanco, Julià
  • García-Pérez, Javier
  • Alcamí, José
  • Serrano-Mollar, Anna
  • Rosado, Joel
  • Falcó, Vicenç
  • Genesca, Meritxell
  • Buzón, María José
(A) General gating strategy used to identify different cell subsets in lung samples. A gate based on FSC vs. SSC was followed by doublet and dead cells exclusion. From live CD45- cells, endothelial cells (CD31+, purple) and epithelial cells (EpCAM+, grey) were gated, and within epithelial cells, AT-II cells (EpCAM+ and HLA-DR+, pink) were identified. Out of live CD45+ cells and based on FSC vs. SSC, we identified a lymphocyte population in which we distinguished between non-T lymphocytes (turquoise) and T cells (dark green) based on CD3 expression; and big cells, where we identified three major subsets based on their expression of CD11b and CD11c and, subsequently, CD14 and HLA-DR markers. We identified alveolar macrophages (blue), monocytes (violet), myeloid dendritic cells (mDCs, fuchsia) and neutrophils (orange)., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/329705
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329705
HANDLE: http://hdl.handle.net/10261/329705
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329705
PMID: http://hdl.handle.net/10261/329705
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329705
Ver en: http://hdl.handle.net/10261/329705
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329705

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329707
Set de datos (Dataset). 2022

OPTIMIZATION OF LUNG TISSUE ENZYMATIC DIGESTION VISUALIZED BY T-DISTRIBUTED STOCHASTIC NEIGHBOR EMBEDDING (TSNE), AND REPRESENTATIVE SPC AND ACE2 EXPRESSION

  • Grau-Expósito, Judith
  • Perea, David
  • Suppi, Marina
  • Massana, Nuria
  • Vergara, Ander
  • Soler, María José
  • Trinité, Benjamin
  • Blanco, Julià
  • García-Pérez, Javier
  • Alcamí, José
  • Serrano-Mollar, Anna
  • Rosado, Joel
  • Falcó, Vicenç
  • Genesca, Meritxell
  • Buzón, María José
(A) Representative tSNE maps showing concatenated flow cytometry standard files for three different protocols based on different digestion enzymes (collagenase, liberase or trypsin) from total live cells (upper), CD45+ cells (middle) and CD45- cells (lower). (B) Bar plots showing cell-type composition (count) analyzed by flow cytometry for each tissue protocol. (C) Representative flow cytometry plots showing Surfactant Protein C (SPC) staining and its respective fluorescence minus one (FMO) control. (D) Representative flow cytometry plots showing ACE2 staining and its respective fluorescence minus one (FMO) control., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/329707
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329707
HANDLE: http://hdl.handle.net/10261/329707
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329707
PMID: http://hdl.handle.net/10261/329707
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329707
Ver en: http://hdl.handle.net/10261/329707
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329707

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329710
Set de datos (Dataset). 2022

GATING STRATEGY FOR THE IDENTIFICATION OF ANTI-INFLAMMATORY EFFECTS OF SELECTED COMPOUNDS

  • Grau-Expósito, Judith
  • Perea, David
  • Suppi, Marina
  • Massana, Nuria
  • Vergara, Ander
  • Soler, María José
  • Trinité, Benjamin
  • Blanco, Julià
  • García-Pérez, Javier
  • Alcamí, José
  • Serrano-Mollar, Anna
  • Rosado, Joel
  • Falcó, Vicenç
  • Genesca, Meritxell
  • Buzón, María José
General gating strategy used to evaluate the expression of inflammatory molecules in lung samples. A gate based on FSC vs. SSC was followed by doublet and dead cells exclusion. From live CD45+ cells and based on FSC vs. SSC, we identified lymphocyte population and big cells, in which we identified two subsets based on their expression of CD11b and CD14: myeloid CD11b+CD14+ cells (blue-green) and myeloid CD11b+CD14- cells (orange) are shown, Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/329710
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329710
HANDLE: http://hdl.handle.net/10261/329710
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329710
PMID: http://hdl.handle.net/10261/329710
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329710
Ver en: http://hdl.handle.net/10261/329710
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329710

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329714
Set de datos (Dataset). 2022

ANTIVIRAL DRUG CANDIDATES FOR ENTRY INHIBITION OF SARS-COV-2

  • Grau-Expósito, Judith
  • Perea, David
  • Suppi, Marina
  • Massana, Nuria
  • Vergara, Ander
  • Soler, María José
  • Trinité, Benjamin
  • Blanco, Julià
  • García-Pérez, Javier
  • Alcamí, José
  • Serrano-Mollar, Anna
  • Rosado, Joel
  • Falcó, Vicenç
  • Genesca, Meritxell
  • Buzón, María José
Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/329714
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329714
HANDLE: http://hdl.handle.net/10261/329714
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329714
PMID: http://hdl.handle.net/10261/329714
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329714
Ver en: http://hdl.handle.net/10261/329714
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329714

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329718
Set de datos (Dataset). 2022

EC50 AND CC50 OF 39 ANTIVIRAL DRUG CANDIDATES

  • Grau-Expósito, Judith
  • Perea, David
  • Suppi, Marina
  • Massana, Nuria
  • Vergara, Ander
  • Soler, María José
  • Trinité, Benjamin
  • Blanco, Julià
  • García-Pérez, Javier
  • Alcamí, José
  • Serrano-Mollar, Anna
  • Rosado, Joel
  • Falcó, Vicenç
  • Genesca, Meritxell
  • Buzón, María José
Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/329718
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329718
HANDLE: http://hdl.handle.net/10261/329718
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329718
PMID: http://hdl.handle.net/10261/329718
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329718
Ver en: http://hdl.handle.net/10261/329718
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329718

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