Pulmonary measles disease: old and new imaging tools View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-12

AUTHORS

Fabrizio Albarello, Massimo Cristofaro, Elisa Busi Rizzi, Maria Letizia Giancola, Emanuele Nicastri, Vincenzo SchininĂ 

ABSTRACT

BACKGROUND: Measles virus can cause lower respiratory tract infection, so that chest radiography is necessary to investigate lung involvement in patients with respiratory distress. PURPOSE: To assess measles pneumonia imaging during the measles outbreak occurred in 2016-2017 in Italy. MATERIAL AND METHODS: We retrospectively observed adult patients with a serological diagnosis of measles, who underwent chest-X rays for suspected pneumonia. If a normal radiography resulted, the patient underwent unenhanced CT. A CT post processing software package was used for an additional quantitative lung and airway involvement analysis . RESULTS: Among 290 patients affected by measles, 150 underwent chest-X ray. Traditional imaging allowed the pneumonia diagnosis in 114 patients (76%). The most frequent abnormality at chest X-rays was bronchial wall thickening, observed in 88.5% of the cases; radiological findings are faint in the 25% of the cases (29/114 patients). In nine subjects with a normal chest X-ray, unenhanced CT with a quantitative analysis was performed, and depicted features consistent with constrictive bronchiolitis. CONCLUSION: Measles may produce bronchiolitis and pneumonia. In the cases in which involvement of pulmonary parenchyma is not sufficient to result in radiological abnormalities, CT used with a dedicated postprocessing software package, provides an accurate lungs and airways analysis, also determining the percentage of lung involvement. More... »

PAGES

1-9

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11547-018-0919-y

DOI

http://dx.doi.org/10.1007/s11547-018-0919-y

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1105903112

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30062499


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/2008/1102", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Cardiorespiratory Medicine and Haematology", 
        "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": "Istituto Nazionale per le Malattie Infettive Lazzaro Spallanzani", 
          "id": "https://www.grid.ac/institutes/grid.419423.9", 
          "name": [
            "Diagnostic Imaging Unit, National Institute for Infectious Diseases, L.Spallanzani, Rome, Italy"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Albarello", 
        "givenName": "Fabrizio", 
        "id": "sg:person.013406124343.88", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013406124343.88"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Istituto Nazionale per le Malattie Infettive Lazzaro Spallanzani", 
          "id": "https://www.grid.ac/institutes/grid.419423.9", 
          "name": [
            "Diagnostic Imaging Unit, National Institute for Infectious Diseases, L.Spallanzani, Rome, Italy"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Cristofaro", 
        "givenName": "Massimo", 
        "id": "sg:person.0646344524.57", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0646344524.57"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Istituto Nazionale per le Malattie Infettive Lazzaro Spallanzani", 
          "id": "https://www.grid.ac/institutes/grid.419423.9", 
          "name": [
            "Diagnostic Imaging Unit, National Institute for Infectious Diseases, L.Spallanzani, Rome, Italy"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Busi Rizzi", 
        "givenName": "Elisa", 
        "id": "sg:person.01063260774.30", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01063260774.30"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Istituto Nazionale per le Malattie Infettive Lazzaro Spallanzani", 
          "id": "https://www.grid.ac/institutes/grid.419423.9", 
          "name": [
            "Clinical Department, National Institute for Infectious Diseases, L.Spallanzani, Rome, Italy"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Giancola", 
        "givenName": "Maria Letizia", 
        "id": "sg:person.01145150564.34", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01145150564.34"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Istituto Nazionale per le Malattie Infettive Lazzaro Spallanzani", 
          "id": "https://www.grid.ac/institutes/grid.419423.9", 
          "name": [
            "Clinical Department, National Institute for Infectious Diseases, L.Spallanzani, Rome, Italy"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Nicastri", 
        "givenName": "Emanuele", 
        "id": "sg:person.01160152235.27", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01160152235.27"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Istituto Nazionale per le Malattie Infettive Lazzaro Spallanzani", 
          "id": "https://www.grid.ac/institutes/grid.419423.9", 
          "name": [
            "Diagnostic Imaging Unit, National Institute for Infectious Diseases, L.Spallanzani, Rome, Italy"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Schinin\u00e0", 
        "givenName": "Vincenzo", 
        "id": "sg:person.01260364730.58", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01260364730.58"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1097/00005382-199701000-00004", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003210433"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/00005382-199701000-00004", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003210433"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2169/internalmedicine.49.3843", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004032925"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2169/internalmedicine.49.3843", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004032925"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1600-065x.2010.00925.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010032018"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1600-065x.2010.00925.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010032018"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.ppat.1002885", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014380274"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1001/jama.1992.03480090085032", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017102389"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/radiol.11092149", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027701571"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.coviro.2012.03.005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028639549"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1086/379652", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031781859"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1378/chest.97.2.315", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035307179"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.humpath.2007.01.015", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035658199"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0002-9343(81)90203-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035774289"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1136/bcr-2015-211054", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037652684"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0196-0644(95)70072-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044729307"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.eimc.2009.05.006", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049189022"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0022-3476(69)80456-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052574865"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1469-0691.2012.03982.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052681865"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1055/s-2006-957336", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1057470373"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1086/377712", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1058670782"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2214/ajr.05.1826", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069297952"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2214/ajr.130.2.223", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069309285"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2214/ajr.134.2.257", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069310299"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2214/ajr.162.3.8109498", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069318427"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/radiographics.22.suppl_1.g02oc15s137", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1075174000"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1077217979", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1078378430", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1078709691", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1078738013", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1078980876", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1079419571", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/radiology.170.3.2916013", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1079452758"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/radiology.161.3.3786710", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1079951195"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/131.3.653", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1080329985"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1081725843", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/radiology.196.1.7784583", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1082505971"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1152/jappl.1994.76.1.271", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1082686858"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/radiology.186.3.8430169", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1082803638"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/radiology.187.1.8451419", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1082812000"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/radiology.205.2.9356630", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1083168112"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1136/bmj.j426", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1083845252"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1136/bmj.j426", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1083845252"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0140-6736(17)31463-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1090319935"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2018-12", 
    "datePublishedReg": "2018-12-01", 
    "description": "BACKGROUND: Measles virus can cause lower respiratory tract infection, so that chest radiography is necessary to investigate lung involvement in patients with respiratory distress.\nPURPOSE: To assess measles pneumonia imaging during the measles outbreak occurred in 2016-2017 in Italy.\nMATERIAL AND METHODS: We retrospectively observed adult patients with a serological diagnosis of measles, who underwent chest-X rays for suspected pneumonia. If a normal radiography resulted, the patient underwent unenhanced CT. A CT post processing software package was used for an additional quantitative lung and airway involvement analysis .\nRESULTS: Among 290 patients affected by measles, 150 underwent chest-X ray. Traditional imaging allowed the pneumonia diagnosis in 114 patients (76%). The most frequent abnormality at chest X-rays was bronchial wall thickening, observed in 88.5% of the cases; radiological findings are faint in the 25% of the cases (29/114 patients). In nine subjects with a normal chest X-ray, unenhanced CT with a quantitative analysis was performed, and depicted features consistent with constrictive bronchiolitis.\nCONCLUSION: Measles may produce bronchiolitis and pneumonia. In the cases in which involvement of pulmonary parenchyma is not sufficient to result in radiological abnormalities, CT used with a dedicated postprocessing software package, provides an accurate lungs and airways analysis, also determining the percentage of lung involvement.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s11547-018-0919-y", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1357346", 
        "issn": [
          "0026-4962", 
          "1826-6983"
        ], 
        "name": "La radiologia medica", 
        "type": "Periodical"
      }
    ], 
    "name": "Pulmonary measles disease: old and new imaging tools", 
    "pagination": "1-9", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "d3cdef16f603413ce043b3e29dde6411c2012af91759b329318422d45c3121a3"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "30062499"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "0177625"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s11547-018-0919-y"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1105903112"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s11547-018-0919-y", 
      "https://app.dimensions.ai/details/publication/pub.1105903112"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T21:46", 
    "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_8687_00000564.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1007%2Fs11547-018-0919-y"
  }
]
 

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.10.1007/s11547-018-0919-y'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s11547-018-0919-y'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11547-018-0919-y'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11547-018-0919-y'


 

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

212 TRIPLES      21 PREDICATES      67 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s11547-018-0919-y schema:about anzsrc-for:11
2 anzsrc-for:1102
3 schema:author Nc89f0a3c21944a958ed59916a45652eb
4 schema:citation https://app.dimensions.ai/details/publication/pub.1077217979
5 https://app.dimensions.ai/details/publication/pub.1078378430
6 https://app.dimensions.ai/details/publication/pub.1078709691
7 https://app.dimensions.ai/details/publication/pub.1078738013
8 https://app.dimensions.ai/details/publication/pub.1078980876
9 https://app.dimensions.ai/details/publication/pub.1079419571
10 https://app.dimensions.ai/details/publication/pub.1081725843
11 https://doi.org/10.1001/jama.1992.03480090085032
12 https://doi.org/10.1016/0002-9343(81)90203-5
13 https://doi.org/10.1016/j.coviro.2012.03.005
14 https://doi.org/10.1016/j.eimc.2009.05.006
15 https://doi.org/10.1016/j.humpath.2007.01.015
16 https://doi.org/10.1016/s0022-3476(69)80456-7
17 https://doi.org/10.1016/s0140-6736(17)31463-0
18 https://doi.org/10.1016/s0196-0644(95)70072-2
19 https://doi.org/10.1055/s-2006-957336
20 https://doi.org/10.1086/377712
21 https://doi.org/10.1086/379652
22 https://doi.org/10.1097/00005382-199701000-00004
23 https://doi.org/10.1111/j.1469-0691.2012.03982.x
24 https://doi.org/10.1111/j.1600-065x.2010.00925.x
25 https://doi.org/10.1136/bcr-2015-211054
26 https://doi.org/10.1136/bmj.j426
27 https://doi.org/10.1148/131.3.653
28 https://doi.org/10.1148/radiographics.22.suppl_1.g02oc15s137
29 https://doi.org/10.1148/radiol.11092149
30 https://doi.org/10.1148/radiology.161.3.3786710
31 https://doi.org/10.1148/radiology.170.3.2916013
32 https://doi.org/10.1148/radiology.186.3.8430169
33 https://doi.org/10.1148/radiology.187.1.8451419
34 https://doi.org/10.1148/radiology.196.1.7784583
35 https://doi.org/10.1148/radiology.205.2.9356630
36 https://doi.org/10.1152/jappl.1994.76.1.271
37 https://doi.org/10.1371/journal.ppat.1002885
38 https://doi.org/10.1378/chest.97.2.315
39 https://doi.org/10.2169/internalmedicine.49.3843
40 https://doi.org/10.2214/ajr.05.1826
41 https://doi.org/10.2214/ajr.130.2.223
42 https://doi.org/10.2214/ajr.134.2.257
43 https://doi.org/10.2214/ajr.162.3.8109498
44 schema:datePublished 2018-12
45 schema:datePublishedReg 2018-12-01
46 schema:description BACKGROUND: Measles virus can cause lower respiratory tract infection, so that chest radiography is necessary to investigate lung involvement in patients with respiratory distress. PURPOSE: To assess measles pneumonia imaging during the measles outbreak occurred in 2016-2017 in Italy. MATERIAL AND METHODS: We retrospectively observed adult patients with a serological diagnosis of measles, who underwent chest-X rays for suspected pneumonia. If a normal radiography resulted, the patient underwent unenhanced CT. A CT post processing software package was used for an additional quantitative lung and airway involvement analysis . RESULTS: Among 290 patients affected by measles, 150 underwent chest-X ray. Traditional imaging allowed the pneumonia diagnosis in 114 patients (76%). The most frequent abnormality at chest X-rays was bronchial wall thickening, observed in 88.5% of the cases; radiological findings are faint in the 25% of the cases (29/114 patients). In nine subjects with a normal chest X-ray, unenhanced CT with a quantitative analysis was performed, and depicted features consistent with constrictive bronchiolitis. CONCLUSION: Measles may produce bronchiolitis and pneumonia. In the cases in which involvement of pulmonary parenchyma is not sufficient to result in radiological abnormalities, CT used with a dedicated postprocessing software package, provides an accurate lungs and airways analysis, also determining the percentage of lung involvement.
47 schema:genre research_article
48 schema:inLanguage en
49 schema:isAccessibleForFree false
50 schema:isPartOf sg:journal.1357346
51 schema:name Pulmonary measles disease: old and new imaging tools
52 schema:pagination 1-9
53 schema:productId N4c5d38151a544b308a147c8c21bf8463
54 N73557ef676ff4131ad2abdd73609f1b4
55 Nb2464816dafe47cd8f064583a31994ea
56 Ndd7b41a51c714b9bb1d5f2f3fa52b3c7
57 Nfcb67130a231467fafad78729ae41425
58 schema:sameAs https://app.dimensions.ai/details/publication/pub.1105903112
59 https://doi.org/10.1007/s11547-018-0919-y
60 schema:sdDatePublished 2019-04-10T21:46
61 schema:sdLicense https://scigraph.springernature.com/explorer/license/
62 schema:sdPublisher Nb25a97411aca49deb01059d3057bb711
63 schema:url https://link.springer.com/10.1007%2Fs11547-018-0919-y
64 sgo:license sg:explorer/license/
65 sgo:sdDataset articles
66 rdf:type schema:ScholarlyArticle
67 N0817fbac3a07435bb3a8a6f40f6ecbc1 rdf:first sg:person.0646344524.57
68 rdf:rest Nb8fa929840784e62a2c305ab59712f3a
69 N35296004f8c44b29acd394b59f62270f rdf:first sg:person.01260364730.58
70 rdf:rest rdf:nil
71 N4c5d38151a544b308a147c8c21bf8463 schema:name pubmed_id
72 schema:value 30062499
73 rdf:type schema:PropertyValue
74 N73557ef676ff4131ad2abdd73609f1b4 schema:name readcube_id
75 schema:value d3cdef16f603413ce043b3e29dde6411c2012af91759b329318422d45c3121a3
76 rdf:type schema:PropertyValue
77 Na7011a4e00ec49f38d616b462de173a4 rdf:first sg:person.01160152235.27
78 rdf:rest N35296004f8c44b29acd394b59f62270f
79 Nb2464816dafe47cd8f064583a31994ea schema:name dimensions_id
80 schema:value pub.1105903112
81 rdf:type schema:PropertyValue
82 Nb25a97411aca49deb01059d3057bb711 schema:name Springer Nature - SN SciGraph project
83 rdf:type schema:Organization
84 Nb8fa929840784e62a2c305ab59712f3a rdf:first sg:person.01063260774.30
85 rdf:rest Nf107d30bd56c48feaf2a97923786a404
86 Nc89f0a3c21944a958ed59916a45652eb rdf:first sg:person.013406124343.88
87 rdf:rest N0817fbac3a07435bb3a8a6f40f6ecbc1
88 Ndd7b41a51c714b9bb1d5f2f3fa52b3c7 schema:name nlm_unique_id
89 schema:value 0177625
90 rdf:type schema:PropertyValue
91 Nf107d30bd56c48feaf2a97923786a404 rdf:first sg:person.01145150564.34
92 rdf:rest Na7011a4e00ec49f38d616b462de173a4
93 Nfcb67130a231467fafad78729ae41425 schema:name doi
94 schema:value 10.1007/s11547-018-0919-y
95 rdf:type schema:PropertyValue
96 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
97 schema:name Medical and Health Sciences
98 rdf:type schema:DefinedTerm
99 anzsrc-for:1102 schema:inDefinedTermSet anzsrc-for:
100 schema:name Cardiorespiratory Medicine and Haematology
101 rdf:type schema:DefinedTerm
102 sg:journal.1357346 schema:issn 0026-4962
103 1826-6983
104 schema:name La radiologia medica
105 rdf:type schema:Periodical
106 sg:person.01063260774.30 schema:affiliation https://www.grid.ac/institutes/grid.419423.9
107 schema:familyName Busi Rizzi
108 schema:givenName Elisa
109 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01063260774.30
110 rdf:type schema:Person
111 sg:person.01145150564.34 schema:affiliation https://www.grid.ac/institutes/grid.419423.9
112 schema:familyName Giancola
113 schema:givenName Maria Letizia
114 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01145150564.34
115 rdf:type schema:Person
116 sg:person.01160152235.27 schema:affiliation https://www.grid.ac/institutes/grid.419423.9
117 schema:familyName Nicastri
118 schema:givenName Emanuele
119 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01160152235.27
120 rdf:type schema:Person
121 sg:person.01260364730.58 schema:affiliation https://www.grid.ac/institutes/grid.419423.9
122 schema:familyName SchininĂ 
123 schema:givenName Vincenzo
124 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01260364730.58
125 rdf:type schema:Person
126 sg:person.013406124343.88 schema:affiliation https://www.grid.ac/institutes/grid.419423.9
127 schema:familyName Albarello
128 schema:givenName Fabrizio
129 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013406124343.88
130 rdf:type schema:Person
131 sg:person.0646344524.57 schema:affiliation https://www.grid.ac/institutes/grid.419423.9
132 schema:familyName Cristofaro
133 schema:givenName Massimo
134 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0646344524.57
135 rdf:type schema:Person
136 https://app.dimensions.ai/details/publication/pub.1077217979 schema:CreativeWork
137 https://app.dimensions.ai/details/publication/pub.1078378430 schema:CreativeWork
138 https://app.dimensions.ai/details/publication/pub.1078709691 schema:CreativeWork
139 https://app.dimensions.ai/details/publication/pub.1078738013 schema:CreativeWork
140 https://app.dimensions.ai/details/publication/pub.1078980876 schema:CreativeWork
141 https://app.dimensions.ai/details/publication/pub.1079419571 schema:CreativeWork
142 https://app.dimensions.ai/details/publication/pub.1081725843 schema:CreativeWork
143 https://doi.org/10.1001/jama.1992.03480090085032 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017102389
144 rdf:type schema:CreativeWork
145 https://doi.org/10.1016/0002-9343(81)90203-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035774289
146 rdf:type schema:CreativeWork
147 https://doi.org/10.1016/j.coviro.2012.03.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028639549
148 rdf:type schema:CreativeWork
149 https://doi.org/10.1016/j.eimc.2009.05.006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049189022
150 rdf:type schema:CreativeWork
151 https://doi.org/10.1016/j.humpath.2007.01.015 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035658199
152 rdf:type schema:CreativeWork
153 https://doi.org/10.1016/s0022-3476(69)80456-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052574865
154 rdf:type schema:CreativeWork
155 https://doi.org/10.1016/s0140-6736(17)31463-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1090319935
156 rdf:type schema:CreativeWork
157 https://doi.org/10.1016/s0196-0644(95)70072-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044729307
158 rdf:type schema:CreativeWork
159 https://doi.org/10.1055/s-2006-957336 schema:sameAs https://app.dimensions.ai/details/publication/pub.1057470373
160 rdf:type schema:CreativeWork
161 https://doi.org/10.1086/377712 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058670782
162 rdf:type schema:CreativeWork
163 https://doi.org/10.1086/379652 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031781859
164 rdf:type schema:CreativeWork
165 https://doi.org/10.1097/00005382-199701000-00004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003210433
166 rdf:type schema:CreativeWork
167 https://doi.org/10.1111/j.1469-0691.2012.03982.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1052681865
168 rdf:type schema:CreativeWork
169 https://doi.org/10.1111/j.1600-065x.2010.00925.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1010032018
170 rdf:type schema:CreativeWork
171 https://doi.org/10.1136/bcr-2015-211054 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037652684
172 rdf:type schema:CreativeWork
173 https://doi.org/10.1136/bmj.j426 schema:sameAs https://app.dimensions.ai/details/publication/pub.1083845252
174 rdf:type schema:CreativeWork
175 https://doi.org/10.1148/131.3.653 schema:sameAs https://app.dimensions.ai/details/publication/pub.1080329985
176 rdf:type schema:CreativeWork
177 https://doi.org/10.1148/radiographics.22.suppl_1.g02oc15s137 schema:sameAs https://app.dimensions.ai/details/publication/pub.1075174000
178 rdf:type schema:CreativeWork
179 https://doi.org/10.1148/radiol.11092149 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027701571
180 rdf:type schema:CreativeWork
181 https://doi.org/10.1148/radiology.161.3.3786710 schema:sameAs https://app.dimensions.ai/details/publication/pub.1079951195
182 rdf:type schema:CreativeWork
183 https://doi.org/10.1148/radiology.170.3.2916013 schema:sameAs https://app.dimensions.ai/details/publication/pub.1079452758
184 rdf:type schema:CreativeWork
185 https://doi.org/10.1148/radiology.186.3.8430169 schema:sameAs https://app.dimensions.ai/details/publication/pub.1082803638
186 rdf:type schema:CreativeWork
187 https://doi.org/10.1148/radiology.187.1.8451419 schema:sameAs https://app.dimensions.ai/details/publication/pub.1082812000
188 rdf:type schema:CreativeWork
189 https://doi.org/10.1148/radiology.196.1.7784583 schema:sameAs https://app.dimensions.ai/details/publication/pub.1082505971
190 rdf:type schema:CreativeWork
191 https://doi.org/10.1148/radiology.205.2.9356630 schema:sameAs https://app.dimensions.ai/details/publication/pub.1083168112
192 rdf:type schema:CreativeWork
193 https://doi.org/10.1152/jappl.1994.76.1.271 schema:sameAs https://app.dimensions.ai/details/publication/pub.1082686858
194 rdf:type schema:CreativeWork
195 https://doi.org/10.1371/journal.ppat.1002885 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014380274
196 rdf:type schema:CreativeWork
197 https://doi.org/10.1378/chest.97.2.315 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035307179
198 rdf:type schema:CreativeWork
199 https://doi.org/10.2169/internalmedicine.49.3843 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004032925
200 rdf:type schema:CreativeWork
201 https://doi.org/10.2214/ajr.05.1826 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069297952
202 rdf:type schema:CreativeWork
203 https://doi.org/10.2214/ajr.130.2.223 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069309285
204 rdf:type schema:CreativeWork
205 https://doi.org/10.2214/ajr.134.2.257 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069310299
206 rdf:type schema:CreativeWork
207 https://doi.org/10.2214/ajr.162.3.8109498 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069318427
208 rdf:type schema:CreativeWork
209 https://www.grid.ac/institutes/grid.419423.9 schema:alternateName Istituto Nazionale per le Malattie Infettive Lazzaro Spallanzani
210 schema:name Clinical Department, National Institute for Infectious Diseases, L.Spallanzani, Rome, Italy
211 Diagnostic Imaging Unit, National Institute for Infectious Diseases, L.Spallanzani, Rome, Italy
212 rdf:type schema:Organization
 




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


...