Ontology type: schema:ScholarlyArticle
2015-10
AUTHORSSatarupa Banerjee, Mousumi Pal, Jitamanyu Chakrabarty, Cyril Petibois, Ranjan Rashmi Paul, Amita Giri, Jyotirmoy Chatterjee
ABSTRACTIn search of specific label-free biomarkers for differentiation of two oral lesions, namely oral leukoplakia (OLK) and oral squamous-cell carcinoma (OSCC), Fourier-transform infrared (FTIR) spectroscopy was performed on paraffin-embedded tissue sections from 47 human subjects (eight normal (NOM), 16 OLK, and 23 OSCC). Difference between mean spectra (DBMS), Mann-Whitney's U test, and forward feature selection (FFS) techniques were used for optimising spectral-marker selection. Classification of diseases was performed with linear and quadratic support vector machine (SVM) at 10-fold cross-validation, using different combinations of spectral features. It was observed that six features obtained through FFS enabled differentiation of NOM and OSCC tissue (1782, 1713, 1665, 1545, 1409, and 1161 cm(-1)) and were most significant, able to classify OLK and OSCC with 81.3 % sensitivity, 95.7 % specificity, and 89.7 % overall accuracy. The 43 spectral markers extracted through Mann-Whitney's U Test were the least significant when quadratic SVM was used. Considering the high sensitivity and specificity of the FFS technique, extracting only six spectral biomarkers was thus most useful for diagnosis of OLK and OSCC, and to overcome inter and intra-observer variability experienced in diagnostic best-practice histopathological procedure. By considering the biochemical assignment of these six spectral signatures, this work also revealed altered glycogen and keratin content in histological sections which could able to discriminate OLK and OSCC. The method was validated through spectral selection by the DBMS technique. Thus this method has potential for diagnostic cost minimisation for oral lesions by label-free biomarker identification. More... »
PAGES7935-7943
http://scigraph.springernature.com/pub.10.1007/s00216-015-8960-3
DOIhttp://dx.doi.org/10.1007/s00216-015-8960-3
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1040447617
PUBMEDhttps://www.ncbi.nlm.nih.gov/pubmed/26342309
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/2008/1004",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Medical Biotechnology",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/10",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Technology",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Biomarkers, Tumor",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Carcinoma, Squamous Cell",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Humans",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Leukoplakia, Oral",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Mouth",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Mouth Neoplasms",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Sensitivity and Specificity",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Spectroscopy, Fourier Transform Infrared",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Support Vector Machine",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "Indian Institute of Technology Kharagpur",
"id": "https://www.grid.ac/institutes/grid.429017.9",
"name": [
"School of Medical Science and Technology, Indian Institute of Technology, 721302, Kharagpur, India"
],
"type": "Organization"
},
"familyName": "Banerjee",
"givenName": "Satarupa",
"id": "sg:person.0735321325.93",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0735321325.93"
],
"type": "Person"
},
{
"affiliation": {
"name": [
"Department of Oral and Maxillofacial Pathology, Guru Nanak Institute of Dental Science and Research, 157/F Nilganj Road, Panihati, 700 114, Kolkata, India"
],
"type": "Organization"
},
"familyName": "Pal",
"givenName": "Mousumi",
"id": "sg:person.0610042437.00",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0610042437.00"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "National Institute of Technology Durgapur",
"id": "https://www.grid.ac/institutes/grid.444419.8",
"name": [
"Department of Chemistry, National Institute of Technology, 713209, Durgapur, India"
],
"type": "Organization"
},
"familyName": "Chakrabarty",
"givenName": "Jitamanyu",
"id": "sg:person.0763236303.26",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0763236303.26"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "University of Bordeaux",
"id": "https://www.grid.ac/institutes/grid.412041.2",
"name": [
"University of Bordeaux \u2013 Inserm U1029 LAMC \u2013 Biophysics of Vascular Plasticity, 33608, Pessac, France"
],
"type": "Organization"
},
"familyName": "Petibois",
"givenName": "Cyril",
"id": "sg:person.0613723320.30",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0613723320.30"
],
"type": "Person"
},
{
"affiliation": {
"name": [
"Department of Oral and Maxillofacial Pathology, Guru Nanak Institute of Dental Science and Research, 157/F Nilganj Road, Panihati, 700 114, Kolkata, India"
],
"type": "Organization"
},
"familyName": "Paul",
"givenName": "Ranjan Rashmi",
"id": "sg:person.01040517437.95",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01040517437.95"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "North Bengal Medical College and Hospital",
"id": "https://www.grid.ac/institutes/grid.416411.7",
"name": [
"Department of Pathology, North Bengal Medical College and Hospital, 734012, Darjeeling, India"
],
"type": "Organization"
},
"familyName": "Giri",
"givenName": "Amita",
"id": "sg:person.010275311202.18",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010275311202.18"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Indian Institute of Technology Kharagpur",
"id": "https://www.grid.ac/institutes/grid.429017.9",
"name": [
"School of Medical Science and Technology, Indian Institute of Technology, 721302, Kharagpur, India"
],
"type": "Organization"
},
"familyName": "Chatterjee",
"givenName": "Jyotirmoy",
"id": "sg:person.01246277203.66",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01246277203.66"
],
"type": "Person"
}
],
"citation": [
{
"id": "https://doi.org/10.1016/j.addr.2015.03.009",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1000480879"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.4137/bic.s12951",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1001303079"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1002/(sici)1520-6343(1999)5:2<117::aid-bspy5>3.0.co;2-k",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1005752091"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1039/c3an00256j",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1008044405"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.oraloncology.2008.05.016",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1012323208"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/0030-4220(68)90437-4",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1013527782"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/0030-4220(68)90437-4",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1013527782"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/s0304-3835(96)04450-3",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1015421036"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1111/j.1745-7270.2007.00320.x",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1017684681"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1039/c2an35483g",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1020634264"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1177/154411130301400105",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1020876508"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1177/154411130301400105",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1020876508"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1002/hed.23962",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1024019656"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/1472-6947-10-16",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1027114800",
"https://doi.org/10.1186/1472-6947-10-16"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1039/c2an16300d",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1029863020"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1002/jbio.201300190",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1031134214"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/jid.1955.82",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1031633985",
"https://doi.org/10.1038/jid.1955.82"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1006/excr.1993.1185",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1033990461"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nprot.2014.110",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1034819774",
"https://doi.org/10.1038/nprot.2014.110"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1039/c2ay25544h",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1035641215"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1371/journal.pone.0116491",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1036450390"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.cdp.2003.11.004",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1038523471"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1080/05704920701829043",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1040216494"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1093/bioinformatics/btt084",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1041296281"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1096/fj.02-0752rev",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1041814143"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/jid.1958.130",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1047246942",
"https://doi.org/10.1038/jid.1958.130"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00216-006-0827-1",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1048936592",
"https://doi.org/10.1007/s00216-006-0827-1"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00216-006-0827-1",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1048936592",
"https://doi.org/10.1007/s00216-006-0827-1"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1385/bter:87:1-3:045",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1048972841",
"https://doi.org/10.1385/bter:87:1-3:045"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1097/00024382-199912000-00012",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1050444766"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1097/00024382-199912000-00012",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1050444766"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1111/j.1749-6632.1960.tb49965.x",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1050527222"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/0304-4165(91)90172-d",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1052688489"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/0304-4165(91)90172-d",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1052688489"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1117/1.jbo.17.10.105002",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1052929323"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.4028/www.scientific.net/amr.550-553.1304",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1072027226"
],
"type": "CreativeWork"
},
{
"id": "https://app.dimensions.ai/details/publication/pub.1078495556",
"type": "CreativeWork"
}
],
"datePublished": "2015-10",
"datePublishedReg": "2015-10-01",
"description": "In search of specific label-free biomarkers for differentiation of two oral lesions, namely oral leukoplakia (OLK) and oral squamous-cell carcinoma (OSCC), Fourier-transform infrared (FTIR) spectroscopy was performed on paraffin-embedded tissue sections from 47 human subjects (eight normal (NOM), 16 OLK, and 23 OSCC). Difference between mean spectra (DBMS), Mann-Whitney's U test, and forward feature selection (FFS) techniques were used for optimising spectral-marker selection. Classification of diseases was performed with linear and quadratic support vector machine (SVM) at 10-fold cross-validation, using different combinations of spectral features. It was observed that six features obtained through FFS enabled differentiation of NOM and OSCC tissue (1782, 1713, 1665, 1545, 1409, and 1161\u00a0cm(-1)) and were most significant, able to classify OLK and OSCC with 81.3\u00a0% sensitivity, 95.7\u00a0% specificity, and 89.7\u00a0% overall accuracy. The 43 spectral markers extracted through Mann-Whitney's U Test were the least significant when quadratic SVM was used. Considering the high sensitivity and specificity of the FFS technique, extracting only six spectral biomarkers was thus most useful for diagnosis of OLK and OSCC, and to overcome inter and intra-observer variability experienced in diagnostic best-practice histopathological procedure. By considering the biochemical assignment of these six spectral signatures, this work also revealed altered glycogen and keratin content in histological sections which could able to discriminate OLK and OSCC. The method was validated through spectral selection by the DBMS technique. Thus this method has potential for diagnostic cost minimisation for oral lesions by label-free biomarker identification. ",
"genre": "research_article",
"id": "sg:pub.10.1007/s00216-015-8960-3",
"inLanguage": [
"en"
],
"isAccessibleForFree": false,
"isPartOf": [
{
"id": "sg:journal.1357342",
"issn": [
"1618-2642",
"1618-2650"
],
"name": "Analytical and Bioanalytical Chemistry",
"type": "Periodical"
},
{
"issueNumber": "26",
"type": "PublicationIssue"
},
{
"type": "PublicationVolume",
"volumeNumber": "407"
}
],
"name": "Fourier-transform-infrared-spectroscopy based spectral-biomarker selection towards optimum diagnostic differentiation of oral leukoplakia and cancer",
"pagination": "7935-7943",
"productId": [
{
"name": "readcube_id",
"type": "PropertyValue",
"value": [
"c0acde10e1727a67ab61381a40608b58f168b25a413602daa6e1833900188448"
]
},
{
"name": "pubmed_id",
"type": "PropertyValue",
"value": [
"26342309"
]
},
{
"name": "nlm_unique_id",
"type": "PropertyValue",
"value": [
"101134327"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/s00216-015-8960-3"
]
},
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1040447617"
]
}
],
"sameAs": [
"https://doi.org/10.1007/s00216-015-8960-3",
"https://app.dimensions.ai/details/publication/pub.1040447617"
],
"sdDataset": "articles",
"sdDatePublished": "2019-04-10T15:13",
"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_8663_00000592.jsonl",
"type": "ScholarlyArticle",
"url": "http://link.springer.com/10.1007%2Fs00216-015-8960-3"
}
]
Download the RDF metadata as: json-ld nt turtle xml License info
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/s00216-015-8960-3'
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/s00216-015-8960-3'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00216-015-8960-3'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00216-015-8960-3'
This table displays all metadata directly associated to this object as RDF triples.
261 TRIPLES
21 PREDICATES
70 URIs
30 LITERALS
18 BLANK NODES