Prediction and validation of total and regional skeletal muscle mass by ultrasound in Japanese adults View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2005-10-19

AUTHORS

Kiyoshi Sanada, Charles F. Kearns, Taishi Midorikawa, Takashi Abe

ABSTRACT

The present study was performed to develop regression-based prediction equations for skeletal muscle (SM) mass by ultrasound and to investigate the validity of these equations in Japanese adults. Seventy-two Japanese men (n=38) and women (n=34) aged 18–61 years participated in this study and were randomly separated into two groups: the model development group (n=48) and the validation group (n=24). The total and regional SM mass were measured using magnetic resonance imaging (MRI) 1.5 T-scanners with spin-echo sequence. Contiguous transverse images (about 150 slices) with a slice thickness of 1 cm were obtained from the first cervical vertebra to the ankle joints. The volume of SM was calculated from the summation of digitized cross-sectional area. The SM volume was converted into mass units (kg) by an assumed SM density of 1.04 kg l−1. The muscle thickness (MTH) was measured by B-mode ultrasound (5 MHz scanning head) at nine sites on the anatomical SM belly. Strong correlations were observed between the site-matched SM mass (total, arm, trunk body, thigh, and lower leg) by MRI measurement and the MTH × height (in m) in the model development group (r=0.83–0.96 in men, r=0.53–0.91 in women, P<0.05). When the SM mass prediction equations were applied to the validation group, significant correlations were also observed between the MRI-measured and predicted SM mass (P<0.05). The predicted total SM mass for the validation group was 19.6 (6.5) kg and was not significantly different from the MRI-measured SM mass of 20.2 (6.5) kg. Bland–Altman analysis did not indicate a bias in prediction of the total SM mass for the validation group (r=0.00, NS). These results suggested that ultrasound-derived prediction equations are a valid method to predict SM mass and an alternative to MRI measurement in healthy Japanese adults. More... »

PAGES

24-31

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00421-005-0061-0

DOI

http://dx.doi.org/10.1007/s00421-005-0061-0

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1002240691

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/16235068


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/2008/11", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Medical and Health Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1103", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Clinical Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Adolescent", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Adult", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Body Weights and Measures", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Female", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Japan", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Magnetic Resonance Angiography", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Male", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Middle Aged", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Muscle, Skeletal", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Reproducibility of Results", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Ultrasonography", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Tokyo Metropolitan University, 1-1 Minami-Ohsawa Hachioji, 192-0397, Tokyo, Japan", 
          "id": "http://www.grid.ac/institutes/grid.265074.2", 
          "name": [
            "Tokyo Metropolitan University, 1-1 Minami-Ohsawa Hachioji, 192-0397, Tokyo, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Sanada", 
        "givenName": "Kiyoshi", 
        "id": "sg:person.01100642414.49", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01100642414.49"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Tokyo Metropolitan University, 1-1 Minami-Ohsawa Hachioji, 192-0397, Tokyo, Japan", 
          "id": "http://www.grid.ac/institutes/grid.265074.2", 
          "name": [
            "Tokyo Metropolitan University, 1-1 Minami-Ohsawa Hachioji, 192-0397, Tokyo, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kearns", 
        "givenName": "Charles F.", 
        "id": "sg:person.01051074015.77", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01051074015.77"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Tokyo Metropolitan University, 1-1 Minami-Ohsawa Hachioji, 192-0397, Tokyo, Japan", 
          "id": "http://www.grid.ac/institutes/grid.265074.2", 
          "name": [
            "Tokyo Metropolitan University, 1-1 Minami-Ohsawa Hachioji, 192-0397, Tokyo, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Midorikawa", 
        "givenName": "Taishi", 
        "id": "sg:person.0762567575.13", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0762567575.13"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Tokyo Metropolitan University, 1-1 Minami-Ohsawa Hachioji, 192-0397, Tokyo, Japan", 
          "id": "http://www.grid.ac/institutes/grid.265074.2", 
          "name": [
            "Tokyo Metropolitan University, 1-1 Minami-Ohsawa Hachioji, 192-0397, Tokyo, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Abe", 
        "givenName": "Takashi", 
        "id": "sg:person.013620221043.16", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013620221043.16"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/s00421-003-1034-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051740902", 
          "https://doi.org/10.1007/s00421-003-1034-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s004210100468", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040400150", 
          "https://doi.org/10.1007/s004210100468"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.2165/00007256-198805020-00002", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040449838", 
          "https://doi.org/10.2165/00007256-198805020-00002"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s004210050394", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007367687", 
          "https://doi.org/10.1007/s004210050394"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.2165/00007256-198805010-00003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048158144", 
          "https://doi.org/10.2165/00007256-198805010-00003"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00421-003-0961-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046275115", 
          "https://doi.org/10.1007/s00421-003-0961-9"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2005-10-19", 
    "datePublishedReg": "2005-10-19", 
    "description": "The present study was performed to develop regression-based prediction equations for skeletal muscle (SM) mass by ultrasound and to investigate the validity of these equations in Japanese adults. Seventy-two Japanese men (n=38) and women (n=34) aged 18\u201361\u00a0years participated in this study and were randomly separated into two groups: the model development group (n=48) and the validation group (n=24). The total and regional SM mass were measured using magnetic resonance imaging (MRI) 1.5 T-scanners with spin-echo sequence. Contiguous transverse images (about 150 slices) with a slice thickness of 1\u00a0cm were obtained from the first cervical vertebra to the ankle joints. The volume of SM was calculated from the summation of digitized cross-sectional area. The SM volume was converted into mass units (kg) by an assumed SM density of 1.04\u00a0kg\u00a0l\u22121. The muscle thickness (MTH) was measured by B-mode ultrasound (5\u00a0MHz scanning head) at nine sites on the anatomical SM belly. Strong correlations were observed between the site-matched SM mass (total, arm, trunk body, thigh, and lower leg) by MRI measurement and the MTH\u00a0\u00d7\u00a0height (in m) in the model development group (r=0.83\u20130.96 in men, r=0.53\u20130.91 in women, P<0.05). When the SM mass prediction equations were applied to the validation group, significant correlations were also observed between the MRI-measured and predicted SM mass (P<0.05). The predicted total SM mass for the validation group was 19.6 (6.5)\u00a0kg and was not significantly different from the MRI-measured SM mass of 20.2 (6.5)\u00a0kg. Bland\u2013Altman analysis did not indicate a bias in prediction of the total SM mass for the validation group (r=0.00, NS). These results suggested that ultrasound-derived prediction equations are a valid method to predict SM mass and an alternative to MRI measurement in healthy Japanese adults.", 
    "genre": "article", 
    "id": "sg:pub.10.1007/s00421-005-0061-0", 
    "inLanguage": "en", 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1319730", 
        "issn": [
          "1439-6319", 
          "1432-1025"
        ], 
        "name": "European Journal of Applied Physiology", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "96"
      }
    ], 
    "keywords": [
      "skeletal muscle mass", 
      "total SM mass", 
      "validation group", 
      "muscle thickness", 
      "Japanese adults", 
      "SM mass", 
      "muscle mass", 
      "model development group", 
      "contiguous transverse images", 
      "MRI measurements", 
      "healthy Japanese adults", 
      "regional skeletal muscle mass", 
      "regional SM mass", 
      "B-mode ultrasound", 
      "first cervical vertebra", 
      "Bland-Altman analysis", 
      "Japanese men", 
      "ankle joint", 
      "SM volume", 
      "cervical vertebrae", 
      "development group", 
      "cross-sectional area", 
      "SM density", 
      "T scanner", 
      "spin-echo sequence", 
      "significant correlation", 
      "ultrasound", 
      "transverse images", 
      "adults", 
      "MRI", 
      "present study", 
      "group", 
      "slice thickness", 
      "valid method", 
      "strong correlation", 
      "women", 
      "prediction equations", 
      "men", 
      "vertebrae", 
      "study", 
      "regression-based prediction equations", 
      "mass", 
      "belly", 
      "correlation", 
      "volume", 
      "years", 
      "joints", 
      "sites", 
      "units", 
      "bias", 
      "alternative", 
      "validity", 
      "analysis", 
      "validation", 
      "area", 
      "measurements", 
      "results", 
      "thickness", 
      "summation", 
      "height", 
      "Sm", 
      "method", 
      "sequence", 
      "images", 
      "prediction", 
      "mass units", 
      "density", 
      "equations"
    ], 
    "name": "Prediction and validation of total and regional skeletal muscle mass by ultrasound in Japanese adults", 
    "pagination": "24-31", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1002240691"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s00421-005-0061-0"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "16235068"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s00421-005-0061-0", 
      "https://app.dimensions.ai/details/publication/pub.1002240691"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-06-01T22:04", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220601/entities/gbq_results/article/article_408.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1007/s00421-005-0061-0"
  }
]
 

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/s00421-005-0061-0'

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/s00421-005-0061-0'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00421-005-0061-0'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00421-005-0061-0'


 

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

223 TRIPLES      22 PREDICATES      112 URIs      98 LITERALS      19 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s00421-005-0061-0 schema:about N05112f7d4be147e796a60762a445bd0c
2 N0721844f1d224a3384b99d4b2ec3cc5d
3 N141b3c7c71da4d1ea77b1c773ec6aea8
4 N45c415a4c6a448c8a8f3bf0b9c40f082
5 N6290c8a540254ac58d5056ea444d48fb
6 N7dc58477d36f407fa50d8bbbb9bdb1a1
7 N8c57ac94be8f46fc982e36c39c0881a4
8 Nb065a824746e4933a840e9eeb3eedab2
9 Nb65fcfdc7f0a4bb9a195e5029aaad45d
10 Nd260c8d45ebb4bc584f5cd9a6bcc8fd7
11 Nde3bf99d30194e1e8aceb150fc070b4a
12 Nf5170c1bc4c541149beb80c13ccbaede
13 anzsrc-for:11
14 anzsrc-for:1103
15 schema:author N8c375d8c1cf949fb9644b672a6a1bd0e
16 schema:citation sg:pub.10.1007/s00421-003-0961-9
17 sg:pub.10.1007/s00421-003-1034-9
18 sg:pub.10.1007/s004210050394
19 sg:pub.10.1007/s004210100468
20 sg:pub.10.2165/00007256-198805010-00003
21 sg:pub.10.2165/00007256-198805020-00002
22 schema:datePublished 2005-10-19
23 schema:datePublishedReg 2005-10-19
24 schema:description The present study was performed to develop regression-based prediction equations for skeletal muscle (SM) mass by ultrasound and to investigate the validity of these equations in Japanese adults. Seventy-two Japanese men (n=38) and women (n=34) aged 18–61 years participated in this study and were randomly separated into two groups: the model development group (n=48) and the validation group (n=24). The total and regional SM mass were measured using magnetic resonance imaging (MRI) 1.5 T-scanners with spin-echo sequence. Contiguous transverse images (about 150 slices) with a slice thickness of 1 cm were obtained from the first cervical vertebra to the ankle joints. The volume of SM was calculated from the summation of digitized cross-sectional area. The SM volume was converted into mass units (kg) by an assumed SM density of 1.04 kg l−1. The muscle thickness (MTH) was measured by B-mode ultrasound (5 MHz scanning head) at nine sites on the anatomical SM belly. Strong correlations were observed between the site-matched SM mass (total, arm, trunk body, thigh, and lower leg) by MRI measurement and the MTH × height (in m) in the model development group (r=0.83–0.96 in men, r=0.53–0.91 in women, P<0.05). When the SM mass prediction equations were applied to the validation group, significant correlations were also observed between the MRI-measured and predicted SM mass (P<0.05). The predicted total SM mass for the validation group was 19.6 (6.5) kg and was not significantly different from the MRI-measured SM mass of 20.2 (6.5) kg. Bland–Altman analysis did not indicate a bias in prediction of the total SM mass for the validation group (r=0.00, NS). These results suggested that ultrasound-derived prediction equations are a valid method to predict SM mass and an alternative to MRI measurement in healthy Japanese adults.
25 schema:genre article
26 schema:inLanguage en
27 schema:isAccessibleForFree false
28 schema:isPartOf N383a92c04b444e248839e25d486a1b76
29 N3cbc44cf85af4b6998644fb5e4dfa98a
30 sg:journal.1319730
31 schema:keywords B-mode ultrasound
32 Bland-Altman analysis
33 Japanese adults
34 Japanese men
35 MRI
36 MRI measurements
37 SM density
38 SM mass
39 SM volume
40 Sm
41 T scanner
42 adults
43 alternative
44 analysis
45 ankle joint
46 area
47 belly
48 bias
49 cervical vertebrae
50 contiguous transverse images
51 correlation
52 cross-sectional area
53 density
54 development group
55 equations
56 first cervical vertebra
57 group
58 healthy Japanese adults
59 height
60 images
61 joints
62 mass
63 mass units
64 measurements
65 men
66 method
67 model development group
68 muscle mass
69 muscle thickness
70 prediction
71 prediction equations
72 present study
73 regional SM mass
74 regional skeletal muscle mass
75 regression-based prediction equations
76 results
77 sequence
78 significant correlation
79 sites
80 skeletal muscle mass
81 slice thickness
82 spin-echo sequence
83 strong correlation
84 study
85 summation
86 thickness
87 total SM mass
88 transverse images
89 ultrasound
90 units
91 valid method
92 validation
93 validation group
94 validity
95 vertebrae
96 volume
97 women
98 years
99 schema:name Prediction and validation of total and regional skeletal muscle mass by ultrasound in Japanese adults
100 schema:pagination 24-31
101 schema:productId N03a38699859840e7b52be82ce713d8ed
102 N0f916c36b2474a7398a89b81ce2bb5db
103 N44c18e453a0d418784180859b06e87fb
104 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002240691
105 https://doi.org/10.1007/s00421-005-0061-0
106 schema:sdDatePublished 2022-06-01T22:04
107 schema:sdLicense https://scigraph.springernature.com/explorer/license/
108 schema:sdPublisher Nb41463b308744730963e0a978726c298
109 schema:url https://doi.org/10.1007/s00421-005-0061-0
110 sgo:license sg:explorer/license/
111 sgo:sdDataset articles
112 rdf:type schema:ScholarlyArticle
113 N03a38699859840e7b52be82ce713d8ed schema:name pubmed_id
114 schema:value 16235068
115 rdf:type schema:PropertyValue
116 N05112f7d4be147e796a60762a445bd0c schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
117 schema:name Reproducibility of Results
118 rdf:type schema:DefinedTerm
119 N0721844f1d224a3384b99d4b2ec3cc5d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
120 schema:name Male
121 rdf:type schema:DefinedTerm
122 N0f916c36b2474a7398a89b81ce2bb5db schema:name dimensions_id
123 schema:value pub.1002240691
124 rdf:type schema:PropertyValue
125 N141b3c7c71da4d1ea77b1c773ec6aea8 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
126 schema:name Body Weights and Measures
127 rdf:type schema:DefinedTerm
128 N383a92c04b444e248839e25d486a1b76 schema:volumeNumber 96
129 rdf:type schema:PublicationVolume
130 N3cbc44cf85af4b6998644fb5e4dfa98a schema:issueNumber 1
131 rdf:type schema:PublicationIssue
132 N44c18e453a0d418784180859b06e87fb schema:name doi
133 schema:value 10.1007/s00421-005-0061-0
134 rdf:type schema:PropertyValue
135 N45c415a4c6a448c8a8f3bf0b9c40f082 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
136 schema:name Middle Aged
137 rdf:type schema:DefinedTerm
138 N6290c8a540254ac58d5056ea444d48fb schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
139 schema:name Japan
140 rdf:type schema:DefinedTerm
141 N7dc58477d36f407fa50d8bbbb9bdb1a1 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
142 schema:name Adult
143 rdf:type schema:DefinedTerm
144 N8c375d8c1cf949fb9644b672a6a1bd0e rdf:first sg:person.01100642414.49
145 rdf:rest Ne5deed79b5474d1aa548c3cba7a350b5
146 N8c57ac94be8f46fc982e36c39c0881a4 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
147 schema:name Ultrasonography
148 rdf:type schema:DefinedTerm
149 Nb065a824746e4933a840e9eeb3eedab2 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
150 schema:name Adolescent
151 rdf:type schema:DefinedTerm
152 Nb41463b308744730963e0a978726c298 schema:name Springer Nature - SN SciGraph project
153 rdf:type schema:Organization
154 Nb65fcfdc7f0a4bb9a195e5029aaad45d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
155 schema:name Magnetic Resonance Angiography
156 rdf:type schema:DefinedTerm
157 Nb8558b59188247868751dd8bfeb0d3eb rdf:first sg:person.013620221043.16
158 rdf:rest rdf:nil
159 Nc02006a13e354b6ea755cd8627362048 rdf:first sg:person.0762567575.13
160 rdf:rest Nb8558b59188247868751dd8bfeb0d3eb
161 Nd260c8d45ebb4bc584f5cd9a6bcc8fd7 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
162 schema:name Humans
163 rdf:type schema:DefinedTerm
164 Nde3bf99d30194e1e8aceb150fc070b4a schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
165 schema:name Female
166 rdf:type schema:DefinedTerm
167 Ne5deed79b5474d1aa548c3cba7a350b5 rdf:first sg:person.01051074015.77
168 rdf:rest Nc02006a13e354b6ea755cd8627362048
169 Nf5170c1bc4c541149beb80c13ccbaede schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
170 schema:name Muscle, Skeletal
171 rdf:type schema:DefinedTerm
172 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
173 schema:name Medical and Health Sciences
174 rdf:type schema:DefinedTerm
175 anzsrc-for:1103 schema:inDefinedTermSet anzsrc-for:
176 schema:name Clinical Sciences
177 rdf:type schema:DefinedTerm
178 sg:journal.1319730 schema:issn 1432-1025
179 1439-6319
180 schema:name European Journal of Applied Physiology
181 schema:publisher Springer Nature
182 rdf:type schema:Periodical
183 sg:person.01051074015.77 schema:affiliation grid-institutes:grid.265074.2
184 schema:familyName Kearns
185 schema:givenName Charles F.
186 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01051074015.77
187 rdf:type schema:Person
188 sg:person.01100642414.49 schema:affiliation grid-institutes:grid.265074.2
189 schema:familyName Sanada
190 schema:givenName Kiyoshi
191 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01100642414.49
192 rdf:type schema:Person
193 sg:person.013620221043.16 schema:affiliation grid-institutes:grid.265074.2
194 schema:familyName Abe
195 schema:givenName Takashi
196 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013620221043.16
197 rdf:type schema:Person
198 sg:person.0762567575.13 schema:affiliation grid-institutes:grid.265074.2
199 schema:familyName Midorikawa
200 schema:givenName Taishi
201 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0762567575.13
202 rdf:type schema:Person
203 sg:pub.10.1007/s00421-003-0961-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046275115
204 https://doi.org/10.1007/s00421-003-0961-9
205 rdf:type schema:CreativeWork
206 sg:pub.10.1007/s00421-003-1034-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051740902
207 https://doi.org/10.1007/s00421-003-1034-9
208 rdf:type schema:CreativeWork
209 sg:pub.10.1007/s004210050394 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007367687
210 https://doi.org/10.1007/s004210050394
211 rdf:type schema:CreativeWork
212 sg:pub.10.1007/s004210100468 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040400150
213 https://doi.org/10.1007/s004210100468
214 rdf:type schema:CreativeWork
215 sg:pub.10.2165/00007256-198805010-00003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048158144
216 https://doi.org/10.2165/00007256-198805010-00003
217 rdf:type schema:CreativeWork
218 sg:pub.10.2165/00007256-198805020-00002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040449838
219 https://doi.org/10.2165/00007256-198805020-00002
220 rdf:type schema:CreativeWork
221 grid-institutes:grid.265074.2 schema:alternateName Tokyo Metropolitan University, 1-1 Minami-Ohsawa Hachioji, 192-0397, Tokyo, Japan
222 schema:name Tokyo Metropolitan University, 1-1 Minami-Ohsawa Hachioji, 192-0397, Tokyo, Japan
223 rdf:type schema:Organization
 




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


...