Antitumor Efficacy of Human Monocyte-Derived Dendritic Cells: Comparing Effects of two Monocyte Isolation Methods View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-12

AUTHORS

Graça S Marques, Zélia Silva, Paula A. Videira

ABSTRACT

Background: Dendritic cells (DCs), which can be used as anti-cancer vaccines, are generally obtained in vitro from isolated CD14+ monocytes (MoDCs). This generates high cell numbers and allows instructing DCs to guarantee effective antitumor responses. However, the impact of the monocyte isolation step in the antitumor effectiveness of the generated MoDCs is still unknown. Here, we compared the most used immunomagnetic technologies for monocyte isolation: magnetic activated cell sorting (MACS) from Miltenyi Biotec and EasySep from STEM CELL. Results: MACS technology allowed a higher monocyte yield and purity and, by flow cytometry, monocytes displayed higher size and lower granularity. In the resting state, EasySep_MoDCs showed a higher basal expression of HLA-DR, and no significant response to stimulation by LPS and TNF-α. When stimulated with whole tumor cells lysates, both MoDCs expressed similar levels of maturation and co-stimulatory markers. However, when cultured with autologous T cells, MACS_MoDCs induced significantly higher IFN-γ secretion than EasySep_MoDCs, indicating a stronger induction of Th1 cell response profile. Concordantly, T cells induced by MACS_MoDCs also showed a higher release of cytotoxic granules when in contact with tumor cells. Conclusions: Overall, both the MACS and the EasySep isolation immunomagnetic technologies provide monocytes that differentiate into viable and functional MoDCs. In our experimental settings, resting EasySep_MoDCs showed a higher basal level of maturation but show less responsivity to stimuli. On the other hand, MACS_MoDCs, when stimulated with tumor antigens, showed better ability to stimulate Th1 responses and to induce T cell cytotoxicity against tumor cells. Thus, monocyte isolation techniques crucially affect MoDCs' function and, therefore, should be carefully selected to obtain the desired functionality. More... »

PAGES

4

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s12575-018-0069-6

DOI

http://dx.doi.org/10.1186/s12575-018-0069-6

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1100730155

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/29434528


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1107", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Immunology", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/11", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Medical and Health Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Universidade Nova de Lisboa", 
          "id": "https://www.grid.ac/institutes/grid.10772.33", 
          "name": [
            "CEDOC, NOVA Medical School/Faculdade de Ci\u00eancias M\u00e9dicas, Universidade NOVA de Lisboa, Lisbon, Portugal"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Marques", 
        "givenName": "Gra\u00e7a S", 
        "id": "sg:person.013124022431.76", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013124022431.76"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Universidade Nova de Lisboa", 
          "id": "https://www.grid.ac/institutes/grid.10772.33", 
          "name": [
            "CEDOC, NOVA Medical School/Faculdade de Ci\u00eancias M\u00e9dicas, Universidade NOVA de Lisboa, Lisbon, Portugal", 
            "UCIBIO, Departamento Ci\u00eancias da Vida, Faculdade de Ci\u00eancias e Tecnologia, Universidade NOVA de Lisboa, Lisbon, Portugal"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Silva", 
        "givenName": "Z\u00e9lia", 
        "id": "sg:person.0676315405.40", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0676315405.40"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Universidade Nova de Lisboa", 
          "id": "https://www.grid.ac/institutes/grid.10772.33", 
          "name": [
            "CEDOC, NOVA Medical School/Faculdade de Ci\u00eancias M\u00e9dicas, Universidade NOVA de Lisboa, Lisbon, Portugal", 
            "UCIBIO, Departamento Ci\u00eancias da Vida, Faculdade de Ci\u00eancias e Tecnologia, Universidade NOVA de Lisboa, Lisbon, Portugal", 
            "CDG & Allies \u2013 Professionals and Patient Associations International Network (CDG & Allies \u2013 PPAIN), Faculdade de Ci\u00eancias e Tecnologia, Universidade NOVA de Lisboa, Lisbon, Portugal"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Videira", 
        "givenName": "Paula A.", 
        "id": "sg:person.01024130171.68", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01024130171.68"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1016/s0163-7258(01)00164-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000233179"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2217/imt.12.40", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001201737"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1146/annurev.iy.09.040191.001415", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005122639"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/2041731412472690", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006462914"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/2041731412472690", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006462914"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nri1592", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010707228", 
          "https://doi.org/10.1038/nri1592"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nri1592", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010707228", 
          "https://doi.org/10.1038/nri1592"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.18632/oncotarget.9419", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012926925"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/cyto.990110203", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018545922"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/cyto.990110203", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018545922"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.biologicals.2009.05.004", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019555379"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0022-1759(03)00265-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020414324"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0022-1759(03)00265-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020414324"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1155/2014/946209", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022465610"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3389/fimmu.2013.00491", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022536946"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.molonc.2014.02.008", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024409181"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.imbio.2013.07.005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030689231"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.clim.2010.04.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031035359"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10719-007-9092-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037673431", 
          "https://doi.org/10.1007/s10719-007-9092-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nm1039", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039401759", 
          "https://doi.org/10.1038/nm1039"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nm1039", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039401759", 
          "https://doi.org/10.1038/nm1039"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.tmrv.2009.11.004", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039481531"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pone.0047176", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044696651"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1365-2567.2004.02076.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045588435"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/32588", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048341608", 
          "https://doi.org/10.1038/32588"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/32588", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048341608", 
          "https://doi.org/10.1038/32588"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10549-005-0988-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053527463", 
          "https://doi.org/10.1007/s10549-005-0988-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10549-005-0988-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053527463", 
          "https://doi.org/10.1007/s10549-005-0988-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1089/152581603322023025", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059213417"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1517/14712598.2015.1000298", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1067589024"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3791/54296", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1071425996"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00262-002-0338-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1075240184", 
          "https://doi.org/10.1007/s00262-002-0338-7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1076976792", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1083134705", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.dci.2017.03.010", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084069224"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2018-12", 
    "datePublishedReg": "2018-12-01", 
    "description": "Background: Dendritic cells (DCs), which can be used as anti-cancer vaccines, are generally obtained in vitro from isolated CD14+ monocytes (MoDCs). This generates high cell numbers and allows instructing DCs to guarantee effective antitumor responses. However, the impact of the monocyte isolation step in the antitumor effectiveness of the generated MoDCs is still unknown. Here, we compared the most used immunomagnetic technologies for monocyte isolation: magnetic activated cell sorting (MACS) from Miltenyi Biotec and EasySep from STEM CELL.\nResults: MACS technology allowed a higher monocyte yield and purity and, by flow cytometry, monocytes displayed higher size and lower granularity. In the resting state, EasySep_MoDCs showed a higher basal expression of HLA-DR, and no significant response to stimulation by LPS and TNF-\u03b1. When stimulated with whole tumor cells lysates, both MoDCs expressed similar levels of maturation and co-stimulatory markers. However, when cultured with autologous T cells, MACS_MoDCs induced significantly higher IFN-\u03b3 secretion than EasySep_MoDCs, indicating a stronger induction of Th1 cell response profile. Concordantly, T cells induced by MACS_MoDCs also showed a higher release of cytotoxic granules when in contact with tumor cells.\nConclusions: Overall, both the MACS and the EasySep isolation immunomagnetic technologies provide monocytes that differentiate into viable and functional MoDCs. In our experimental settings, resting EasySep_MoDCs showed a higher basal level of maturation but show less responsivity to stimuli. On the other hand, MACS_MoDCs, when stimulated with tumor antigens, showed better ability to stimulate Th1 responses and to induce T cell cytotoxicity against tumor cells. Thus, monocyte isolation techniques crucially affect MoDCs' function and, therefore, should be carefully selected to obtain the desired functionality.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1186/s12575-018-0069-6", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1023701", 
        "issn": [
          "1480-9222"
        ], 
        "name": "Biological Procedures Online", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "20"
      }
    ], 
    "name": "Antitumor Efficacy of Human Monocyte-Derived Dendritic Cells: Comparing Effects of two Monocyte Isolation Methods", 
    "pagination": "4", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "4733b254cd6d9531b6b4404544f1f075389e13774b9d7d7a4f7132abc1e8050d"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "29434528"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "100963717"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/s12575-018-0069-6"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1100730155"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/s12575-018-0069-6", 
      "https://app.dimensions.ai/details/publication/pub.1100730155"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T17:27", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8672_00000493.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1186/s12575-018-0069-6"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1186/s12575-018-0069-6'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/s12575-018-0069-6'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s12575-018-0069-6'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s12575-018-0069-6'


 

This table displays all metadata directly associated to this object as RDF triples.

172 TRIPLES      21 PREDICATES      57 URIs      21 LITERALS      9 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1186/s12575-018-0069-6 schema:about anzsrc-for:11
2 anzsrc-for:1107
3 schema:author Naee417cb799d424abfbc0b8315b63f38
4 schema:citation sg:pub.10.1007/s00262-002-0338-7
5 sg:pub.10.1007/s10549-005-0988-1
6 sg:pub.10.1007/s10719-007-9092-6
7 sg:pub.10.1038/32588
8 sg:pub.10.1038/nm1039
9 sg:pub.10.1038/nri1592
10 https://app.dimensions.ai/details/publication/pub.1076976792
11 https://app.dimensions.ai/details/publication/pub.1083134705
12 https://doi.org/10.1002/cyto.990110203
13 https://doi.org/10.1016/j.biologicals.2009.05.004
14 https://doi.org/10.1016/j.clim.2010.04.003
15 https://doi.org/10.1016/j.dci.2017.03.010
16 https://doi.org/10.1016/j.imbio.2013.07.005
17 https://doi.org/10.1016/j.molonc.2014.02.008
18 https://doi.org/10.1016/j.tmrv.2009.11.004
19 https://doi.org/10.1016/s0022-1759(03)00265-5
20 https://doi.org/10.1016/s0163-7258(01)00164-4
21 https://doi.org/10.1089/152581603322023025
22 https://doi.org/10.1111/j.1365-2567.2004.02076.x
23 https://doi.org/10.1146/annurev.iy.09.040191.001415
24 https://doi.org/10.1155/2014/946209
25 https://doi.org/10.1177/2041731412472690
26 https://doi.org/10.1371/journal.pone.0047176
27 https://doi.org/10.1517/14712598.2015.1000298
28 https://doi.org/10.18632/oncotarget.9419
29 https://doi.org/10.2217/imt.12.40
30 https://doi.org/10.3389/fimmu.2013.00491
31 https://doi.org/10.3791/54296
32 schema:datePublished 2018-12
33 schema:datePublishedReg 2018-12-01
34 schema:description Background: Dendritic cells (DCs), which can be used as anti-cancer vaccines, are generally obtained in vitro from isolated CD14+ monocytes (MoDCs). This generates high cell numbers and allows instructing DCs to guarantee effective antitumor responses. However, the impact of the monocyte isolation step in the antitumor effectiveness of the generated MoDCs is still unknown. Here, we compared the most used immunomagnetic technologies for monocyte isolation: magnetic activated cell sorting (MACS) from Miltenyi Biotec and EasySep from STEM CELL. Results: MACS technology allowed a higher monocyte yield and purity and, by flow cytometry, monocytes displayed higher size and lower granularity. In the resting state, EasySep_MoDCs showed a higher basal expression of HLA-DR, and no significant response to stimulation by LPS and TNF-α. When stimulated with whole tumor cells lysates, both MoDCs expressed similar levels of maturation and co-stimulatory markers. However, when cultured with autologous T cells, MACS_MoDCs induced significantly higher IFN-γ secretion than EasySep_MoDCs, indicating a stronger induction of Th1 cell response profile. Concordantly, T cells induced by MACS_MoDCs also showed a higher release of cytotoxic granules when in contact with tumor cells. Conclusions: Overall, both the MACS and the EasySep isolation immunomagnetic technologies provide monocytes that differentiate into viable and functional MoDCs. In our experimental settings, resting EasySep_MoDCs showed a higher basal level of maturation but show less responsivity to stimuli. On the other hand, MACS_MoDCs, when stimulated with tumor antigens, showed better ability to stimulate Th1 responses and to induce T cell cytotoxicity against tumor cells. Thus, monocyte isolation techniques crucially affect MoDCs' function and, therefore, should be carefully selected to obtain the desired functionality.
35 schema:genre research_article
36 schema:inLanguage en
37 schema:isAccessibleForFree true
38 schema:isPartOf N468fbed189664024905bfc67d6556f8a
39 N83c51918bb504c57acd669c81e6fcd8d
40 sg:journal.1023701
41 schema:name Antitumor Efficacy of Human Monocyte-Derived Dendritic Cells: Comparing Effects of two Monocyte Isolation Methods
42 schema:pagination 4
43 schema:productId N1bd620e2610a490bb43ef0b7bf22740b
44 N47b44929fa22473d82c9ed4829afb145
45 N757763696af54da7bcc05fbd44d9be06
46 N8bca103ca64c4acba7ac3affb4ec2c7c
47 Nf7a71ad12d4a40b88d3c553522cfd702
48 schema:sameAs https://app.dimensions.ai/details/publication/pub.1100730155
49 https://doi.org/10.1186/s12575-018-0069-6
50 schema:sdDatePublished 2019-04-10T17:27
51 schema:sdLicense https://scigraph.springernature.com/explorer/license/
52 schema:sdPublisher N7b744b4d8a21453b9107275ddd8ec1cb
53 schema:url http://link.springer.com/10.1186/s12575-018-0069-6
54 sgo:license sg:explorer/license/
55 sgo:sdDataset articles
56 rdf:type schema:ScholarlyArticle
57 N05ef0ae1a64142168cdf13215fdaf8cc rdf:first sg:person.01024130171.68
58 rdf:rest rdf:nil
59 N1bd620e2610a490bb43ef0b7bf22740b schema:name doi
60 schema:value 10.1186/s12575-018-0069-6
61 rdf:type schema:PropertyValue
62 N468fbed189664024905bfc67d6556f8a schema:volumeNumber 20
63 rdf:type schema:PublicationVolume
64 N47b44929fa22473d82c9ed4829afb145 schema:name pubmed_id
65 schema:value 29434528
66 rdf:type schema:PropertyValue
67 N757763696af54da7bcc05fbd44d9be06 schema:name dimensions_id
68 schema:value pub.1100730155
69 rdf:type schema:PropertyValue
70 N7b744b4d8a21453b9107275ddd8ec1cb schema:name Springer Nature - SN SciGraph project
71 rdf:type schema:Organization
72 N83c51918bb504c57acd669c81e6fcd8d schema:issueNumber 1
73 rdf:type schema:PublicationIssue
74 N8bca103ca64c4acba7ac3affb4ec2c7c schema:name readcube_id
75 schema:value 4733b254cd6d9531b6b4404544f1f075389e13774b9d7d7a4f7132abc1e8050d
76 rdf:type schema:PropertyValue
77 Naee417cb799d424abfbc0b8315b63f38 rdf:first sg:person.013124022431.76
78 rdf:rest Ne3846fcb72da4acfbf810ff3b76a2a30
79 Ne3846fcb72da4acfbf810ff3b76a2a30 rdf:first sg:person.0676315405.40
80 rdf:rest N05ef0ae1a64142168cdf13215fdaf8cc
81 Nf7a71ad12d4a40b88d3c553522cfd702 schema:name nlm_unique_id
82 schema:value 100963717
83 rdf:type schema:PropertyValue
84 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
85 schema:name Medical and Health Sciences
86 rdf:type schema:DefinedTerm
87 anzsrc-for:1107 schema:inDefinedTermSet anzsrc-for:
88 schema:name Immunology
89 rdf:type schema:DefinedTerm
90 sg:journal.1023701 schema:issn 1480-9222
91 schema:name Biological Procedures Online
92 rdf:type schema:Periodical
93 sg:person.01024130171.68 schema:affiliation https://www.grid.ac/institutes/grid.10772.33
94 schema:familyName Videira
95 schema:givenName Paula A.
96 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01024130171.68
97 rdf:type schema:Person
98 sg:person.013124022431.76 schema:affiliation https://www.grid.ac/institutes/grid.10772.33
99 schema:familyName Marques
100 schema:givenName Graça S
101 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013124022431.76
102 rdf:type schema:Person
103 sg:person.0676315405.40 schema:affiliation https://www.grid.ac/institutes/grid.10772.33
104 schema:familyName Silva
105 schema:givenName Zélia
106 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0676315405.40
107 rdf:type schema:Person
108 sg:pub.10.1007/s00262-002-0338-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1075240184
109 https://doi.org/10.1007/s00262-002-0338-7
110 rdf:type schema:CreativeWork
111 sg:pub.10.1007/s10549-005-0988-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053527463
112 https://doi.org/10.1007/s10549-005-0988-1
113 rdf:type schema:CreativeWork
114 sg:pub.10.1007/s10719-007-9092-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037673431
115 https://doi.org/10.1007/s10719-007-9092-6
116 rdf:type schema:CreativeWork
117 sg:pub.10.1038/32588 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048341608
118 https://doi.org/10.1038/32588
119 rdf:type schema:CreativeWork
120 sg:pub.10.1038/nm1039 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039401759
121 https://doi.org/10.1038/nm1039
122 rdf:type schema:CreativeWork
123 sg:pub.10.1038/nri1592 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010707228
124 https://doi.org/10.1038/nri1592
125 rdf:type schema:CreativeWork
126 https://app.dimensions.ai/details/publication/pub.1076976792 schema:CreativeWork
127 https://app.dimensions.ai/details/publication/pub.1083134705 schema:CreativeWork
128 https://doi.org/10.1002/cyto.990110203 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018545922
129 rdf:type schema:CreativeWork
130 https://doi.org/10.1016/j.biologicals.2009.05.004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019555379
131 rdf:type schema:CreativeWork
132 https://doi.org/10.1016/j.clim.2010.04.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031035359
133 rdf:type schema:CreativeWork
134 https://doi.org/10.1016/j.dci.2017.03.010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084069224
135 rdf:type schema:CreativeWork
136 https://doi.org/10.1016/j.imbio.2013.07.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030689231
137 rdf:type schema:CreativeWork
138 https://doi.org/10.1016/j.molonc.2014.02.008 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024409181
139 rdf:type schema:CreativeWork
140 https://doi.org/10.1016/j.tmrv.2009.11.004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039481531
141 rdf:type schema:CreativeWork
142 https://doi.org/10.1016/s0022-1759(03)00265-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020414324
143 rdf:type schema:CreativeWork
144 https://doi.org/10.1016/s0163-7258(01)00164-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000233179
145 rdf:type schema:CreativeWork
146 https://doi.org/10.1089/152581603322023025 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059213417
147 rdf:type schema:CreativeWork
148 https://doi.org/10.1111/j.1365-2567.2004.02076.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1045588435
149 rdf:type schema:CreativeWork
150 https://doi.org/10.1146/annurev.iy.09.040191.001415 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005122639
151 rdf:type schema:CreativeWork
152 https://doi.org/10.1155/2014/946209 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022465610
153 rdf:type schema:CreativeWork
154 https://doi.org/10.1177/2041731412472690 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006462914
155 rdf:type schema:CreativeWork
156 https://doi.org/10.1371/journal.pone.0047176 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044696651
157 rdf:type schema:CreativeWork
158 https://doi.org/10.1517/14712598.2015.1000298 schema:sameAs https://app.dimensions.ai/details/publication/pub.1067589024
159 rdf:type schema:CreativeWork
160 https://doi.org/10.18632/oncotarget.9419 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012926925
161 rdf:type schema:CreativeWork
162 https://doi.org/10.2217/imt.12.40 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001201737
163 rdf:type schema:CreativeWork
164 https://doi.org/10.3389/fimmu.2013.00491 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022536946
165 rdf:type schema:CreativeWork
166 https://doi.org/10.3791/54296 schema:sameAs https://app.dimensions.ai/details/publication/pub.1071425996
167 rdf:type schema:CreativeWork
168 https://www.grid.ac/institutes/grid.10772.33 schema:alternateName Universidade Nova de Lisboa
169 schema:name CDG & Allies – Professionals and Patient Associations International Network (CDG & Allies – PPAIN), Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, Lisbon, Portugal
170 CEDOC, NOVA Medical School/Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, Lisbon, Portugal
171 UCIBIO, Departamento Ciências da Vida, Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, Lisbon, Portugal
172 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...