Pulse sequence considerations for quantification of pyruvate‐to‐lactate conversion kPL in hyperpolarized 13C imaging
Hyperpolarized 13C MRI takes advantage of the unprecedented 50 000‐fold signal‐to‐noise ratio enhancement to interrogate cancer metabolism in patients and animals. It can measure the pyruvate‐to‐lactate conversion rate, kPL, a metabolic biomarker of cancer aggressiveness and progression. Therefore,...
Gespeichert in:
| Veröffentlicht in: | NMR in biomedicine 2019-03, Vol.32 (3), p.e4052-n/a |
|---|---|
| Hauptverfasser: | , , , , , , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | n/a |
|---|---|
| container_issue | 3 |
| container_start_page | e4052 |
| container_title | NMR in biomedicine |
| container_volume | 32 |
| creator | Chen, Hsin‐Yu Gordon, Jeremy W. Bok, Robert A. Cao, Peng Morze, Cornelius Criekinge, Mark Milshteyn, Eugene Carvajal, Lucas Hurd, Ralph E. Kurhanewicz, John Vigneron, Daniel B. Larson, Peder E.Z. |
| description | Hyperpolarized 13C MRI takes advantage of the unprecedented 50 000‐fold signal‐to‐noise ratio enhancement to interrogate cancer metabolism in patients and animals. It can measure the pyruvate‐to‐lactate conversion rate, kPL, a metabolic biomarker of cancer aggressiveness and progression. Therefore, it is crucial to evaluate kPL reliably. In this study, three sequence components and parameters that modulate kPL estimation were identified and investigated in model simulations and through in vivo animal studies using several specifically designed pulse sequences. These factors included a magnetization spoiling effect due to RF pulses, a crusher gradient‐induced flow suppression, and intrinsic image weightings due to relaxation. Simulation showed that the RF‐induced magnetization spoiling can be substantially improved using an inputless kPL fitting. In vivo studies found a significantly higher apparent kPL with an additional gradient that leads to flow suppression (kPL,FID‐Delay,Crush/kPL,FID‐Delay = 1.37 ± 0.33, P |
| doi_str_mv | 10.1002/nbm.4052 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_6380928</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2183025489</sourcerecordid><originalsourceid>FETCH-LOGICAL-p1212-7159aa344569ec542e4c8e01f99a7d7a9a90ebfe9f24737a38f111590b6700cc3</originalsourceid><addsrcrecordid>eNpVUclOHDEQtSJQmEwi8QmWODcpL734ggSjLEjDckjOlsdTPWPosRt39yTDiU_gG_kS3ICQuNT66lWpHiGHDI4ZAP_uF5tjCTn_RCYMlMqYVHyPTEDlPBOyggPypetuAKCSgn8mBwKKQgrIJ-T_9dB0SDu8G9BbpDb4zi0xmt6liNYh0rvB-N7Vzr7UaKhpu4vD1vT49PDYh2QaY_uUjsNbjN2Iur2eU-fpetdibENjorvHJWViRt3GrJxffSX7tUmrv735Kfn788ef2e9sfvXrfHY6z1rGGc9KlitjhJR5odDmkqO0FQKrlTLlsjTKKMBFjarmshSlEVXNWJqBRVECWCum5OSVtx0WG1xa9H00jW5juiPudDBOf-x4t9arsNWFqEDxKhEcvRHEkJ7U9fomDNGnmzVnlQCey0olVPaK-uca3L3TM9CjQDoJpEeB9OXZxejFM2UoiE8</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2183025489</pqid></control><display><type>article</type><title>Pulse sequence considerations for quantification of pyruvate‐to‐lactate conversion kPL in hyperpolarized 13C imaging</title><source>Wiley Online Library Journals Frontfile Complete</source><creator>Chen, Hsin‐Yu ; Gordon, Jeremy W. ; Bok, Robert A. ; Cao, Peng ; Morze, Cornelius ; Criekinge, Mark ; Milshteyn, Eugene ; Carvajal, Lucas ; Hurd, Ralph E. ; Kurhanewicz, John ; Vigneron, Daniel B. ; Larson, Peder E.Z.</creator><creatorcontrib>Chen, Hsin‐Yu ; Gordon, Jeremy W. ; Bok, Robert A. ; Cao, Peng ; Morze, Cornelius ; Criekinge, Mark ; Milshteyn, Eugene ; Carvajal, Lucas ; Hurd, Ralph E. ; Kurhanewicz, John ; Vigneron, Daniel B. ; Larson, Peder E.Z.</creatorcontrib><description>Hyperpolarized 13C MRI takes advantage of the unprecedented 50 000‐fold signal‐to‐noise ratio enhancement to interrogate cancer metabolism in patients and animals. It can measure the pyruvate‐to‐lactate conversion rate, kPL, a metabolic biomarker of cancer aggressiveness and progression. Therefore, it is crucial to evaluate kPL reliably. In this study, three sequence components and parameters that modulate kPL estimation were identified and investigated in model simulations and through in vivo animal studies using several specifically designed pulse sequences. These factors included a magnetization spoiling effect due to RF pulses, a crusher gradient‐induced flow suppression, and intrinsic image weightings due to relaxation. Simulation showed that the RF‐induced magnetization spoiling can be substantially improved using an inputless kPL fitting. In vivo studies found a significantly higher apparent kPL with an additional gradient that leads to flow suppression (kPL,FID‐Delay,Crush/kPL,FID‐Delay = 1.37 ± 0.33, P < 0.01, N = 6), which agrees with simulation outcomes (12.5% kPL error with Δv = 40 cm/s), indicating that the gradients predominantly suppressed flowing pyruvate spins. Significantly lower kPL was found using a delayed free induction decay (FID) acquisition versus a minimum‐TE version (kPL,FID‐Delay/kPL,FID = 0.67 ± 0.09, P < 0.01, N = 5), and the lactate peak had broader linewidth than pyruvate (Δωlactate/Δωpyruvate = 1.32 ± 0.07, P < 0.000 01, N = 13). This illustrated that lactate's T2*, shorter than that of pyruvate, can affect calculated kPL values. We also found that an FID sequence yielded significantly lower kPL versus a double spin‐echo sequence that includes spin‐echo spoiling, flow suppression from crusher gradients, and more T2 weighting (kPL,DSE/kPL,FID = 2.40 ± 0.98, P < 0.0001, N = 7). In summary, the pulse sequence, as well as its interaction with pharmacokinetics and the tissue microenvironment, can impact and be optimized for the measurement of kPL. The data acquisition and analysis pipelines can work synergistically to provide more robust and reproducible kPL measures for future preclinical and clinical studies.
This study investigated three pulse sequence components and parameters that can affect estimates of kPL in HP‐13C MRI. In vivo animal tumor studies showed significant impact on kPL with crusher‐gradient induced flow suppression, and intrinsic image weighting due to relaxation, corroborated by signal‐model simulations. RF‐induced magnetization spoiling can be substantially improved using an inputless kPL fitting. These outcomes suggested that the pulse sequence, as well as its interaction with pharmacokinetics and tissue microenvironment, can impact the measurement of kPL.</description><identifier>ISSN: 0952-3480</identifier><identifier>EISSN: 1099-1492</identifier><identifier>DOI: 10.1002/nbm.4052</identifier><identifier>PMID: 30664305</identifier><language>eng</language><publisher>Oxford: Wiley Subscription Services, Inc</publisher><subject>Biological products ; Biomarkers ; Cancer ; cancer imaging ; Carbon 13 ; Computer simulation ; Conversion ; Data acquisition ; Data processing ; Delay ; hyperpolarized C‐13 pyruvate ; In vivo methods and tests ; kinetic modeling ; Lactic acid ; Magnetic resonance imaging ; Magnetization ; Metabolism ; Parameter estimation ; Parameter identification ; Pharmacokinetics ; Pharmacology ; pulse sequence ; Pyruvic acid</subject><ispartof>NMR in biomedicine, 2019-03, Vol.32 (3), p.e4052-n/a</ispartof><rights>2019 John Wiley & Sons, Ltd.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><orcidid>0000-0003-4113-7671 ; 0000-0003-4183-3634 ; 0000-0002-2765-1685</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fnbm.4052$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fnbm.4052$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>230,314,776,780,881,1411,27901,27902,45550,45551</link.rule.ids></links><search><creatorcontrib>Chen, Hsin‐Yu</creatorcontrib><creatorcontrib>Gordon, Jeremy W.</creatorcontrib><creatorcontrib>Bok, Robert A.</creatorcontrib><creatorcontrib>Cao, Peng</creatorcontrib><creatorcontrib>Morze, Cornelius</creatorcontrib><creatorcontrib>Criekinge, Mark</creatorcontrib><creatorcontrib>Milshteyn, Eugene</creatorcontrib><creatorcontrib>Carvajal, Lucas</creatorcontrib><creatorcontrib>Hurd, Ralph E.</creatorcontrib><creatorcontrib>Kurhanewicz, John</creatorcontrib><creatorcontrib>Vigneron, Daniel B.</creatorcontrib><creatorcontrib>Larson, Peder E.Z.</creatorcontrib><title>Pulse sequence considerations for quantification of pyruvate‐to‐lactate conversion kPL in hyperpolarized 13C imaging</title><title>NMR in biomedicine</title><description>Hyperpolarized 13C MRI takes advantage of the unprecedented 50 000‐fold signal‐to‐noise ratio enhancement to interrogate cancer metabolism in patients and animals. It can measure the pyruvate‐to‐lactate conversion rate, kPL, a metabolic biomarker of cancer aggressiveness and progression. Therefore, it is crucial to evaluate kPL reliably. In this study, three sequence components and parameters that modulate kPL estimation were identified and investigated in model simulations and through in vivo animal studies using several specifically designed pulse sequences. These factors included a magnetization spoiling effect due to RF pulses, a crusher gradient‐induced flow suppression, and intrinsic image weightings due to relaxation. Simulation showed that the RF‐induced magnetization spoiling can be substantially improved using an inputless kPL fitting. In vivo studies found a significantly higher apparent kPL with an additional gradient that leads to flow suppression (kPL,FID‐Delay,Crush/kPL,FID‐Delay = 1.37 ± 0.33, P < 0.01, N = 6), which agrees with simulation outcomes (12.5% kPL error with Δv = 40 cm/s), indicating that the gradients predominantly suppressed flowing pyruvate spins. Significantly lower kPL was found using a delayed free induction decay (FID) acquisition versus a minimum‐TE version (kPL,FID‐Delay/kPL,FID = 0.67 ± 0.09, P < 0.01, N = 5), and the lactate peak had broader linewidth than pyruvate (Δωlactate/Δωpyruvate = 1.32 ± 0.07, P < 0.000 01, N = 13). This illustrated that lactate's T2*, shorter than that of pyruvate, can affect calculated kPL values. We also found that an FID sequence yielded significantly lower kPL versus a double spin‐echo sequence that includes spin‐echo spoiling, flow suppression from crusher gradients, and more T2 weighting (kPL,DSE/kPL,FID = 2.40 ± 0.98, P < 0.0001, N = 7). In summary, the pulse sequence, as well as its interaction with pharmacokinetics and the tissue microenvironment, can impact and be optimized for the measurement of kPL. The data acquisition and analysis pipelines can work synergistically to provide more robust and reproducible kPL measures for future preclinical and clinical studies.
This study investigated three pulse sequence components and parameters that can affect estimates of kPL in HP‐13C MRI. In vivo animal tumor studies showed significant impact on kPL with crusher‐gradient induced flow suppression, and intrinsic image weighting due to relaxation, corroborated by signal‐model simulations. RF‐induced magnetization spoiling can be substantially improved using an inputless kPL fitting. These outcomes suggested that the pulse sequence, as well as its interaction with pharmacokinetics and tissue microenvironment, can impact the measurement of kPL.</description><subject>Biological products</subject><subject>Biomarkers</subject><subject>Cancer</subject><subject>cancer imaging</subject><subject>Carbon 13</subject><subject>Computer simulation</subject><subject>Conversion</subject><subject>Data acquisition</subject><subject>Data processing</subject><subject>Delay</subject><subject>hyperpolarized C‐13 pyruvate</subject><subject>In vivo methods and tests</subject><subject>kinetic modeling</subject><subject>Lactic acid</subject><subject>Magnetic resonance imaging</subject><subject>Magnetization</subject><subject>Metabolism</subject><subject>Parameter estimation</subject><subject>Parameter identification</subject><subject>Pharmacokinetics</subject><subject>Pharmacology</subject><subject>pulse sequence</subject><subject>Pyruvic acid</subject><issn>0952-3480</issn><issn>1099-1492</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNpVUclOHDEQtSJQmEwi8QmWODcpL734ggSjLEjDckjOlsdTPWPosRt39yTDiU_gG_kS3ICQuNT66lWpHiGHDI4ZAP_uF5tjCTn_RCYMlMqYVHyPTEDlPBOyggPypetuAKCSgn8mBwKKQgrIJ-T_9dB0SDu8G9BbpDb4zi0xmt6liNYh0rvB-N7Vzr7UaKhpu4vD1vT49PDYh2QaY_uUjsNbjN2Iur2eU-fpetdibENjorvHJWViRt3GrJxffSX7tUmrv735Kfn788ef2e9sfvXrfHY6z1rGGc9KlitjhJR5odDmkqO0FQKrlTLlsjTKKMBFjarmshSlEVXNWJqBRVECWCum5OSVtx0WG1xa9H00jW5juiPudDBOf-x4t9arsNWFqEDxKhEcvRHEkJ7U9fomDNGnmzVnlQCey0olVPaK-uca3L3TM9CjQDoJpEeB9OXZxejFM2UoiE8</recordid><startdate>201903</startdate><enddate>201903</enddate><creator>Chen, Hsin‐Yu</creator><creator>Gordon, Jeremy W.</creator><creator>Bok, Robert A.</creator><creator>Cao, Peng</creator><creator>Morze, Cornelius</creator><creator>Criekinge, Mark</creator><creator>Milshteyn, Eugene</creator><creator>Carvajal, Lucas</creator><creator>Hurd, Ralph E.</creator><creator>Kurhanewicz, John</creator><creator>Vigneron, Daniel B.</creator><creator>Larson, Peder E.Z.</creator><general>Wiley Subscription Services, Inc</general><scope>7QO</scope><scope>8FD</scope><scope>FR3</scope><scope>P64</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0003-4113-7671</orcidid><orcidid>https://orcid.org/0000-0003-4183-3634</orcidid><orcidid>https://orcid.org/0000-0002-2765-1685</orcidid></search><sort><creationdate>201903</creationdate><title>Pulse sequence considerations for quantification of pyruvate‐to‐lactate conversion kPL in hyperpolarized 13C imaging</title><author>Chen, Hsin‐Yu ; Gordon, Jeremy W. ; Bok, Robert A. ; Cao, Peng ; Morze, Cornelius ; Criekinge, Mark ; Milshteyn, Eugene ; Carvajal, Lucas ; Hurd, Ralph E. ; Kurhanewicz, John ; Vigneron, Daniel B. ; Larson, Peder E.Z.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p1212-7159aa344569ec542e4c8e01f99a7d7a9a90ebfe9f24737a38f111590b6700cc3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Biological products</topic><topic>Biomarkers</topic><topic>Cancer</topic><topic>cancer imaging</topic><topic>Carbon 13</topic><topic>Computer simulation</topic><topic>Conversion</topic><topic>Data acquisition</topic><topic>Data processing</topic><topic>Delay</topic><topic>hyperpolarized C‐13 pyruvate</topic><topic>In vivo methods and tests</topic><topic>kinetic modeling</topic><topic>Lactic acid</topic><topic>Magnetic resonance imaging</topic><topic>Magnetization</topic><topic>Metabolism</topic><topic>Parameter estimation</topic><topic>Parameter identification</topic><topic>Pharmacokinetics</topic><topic>Pharmacology</topic><topic>pulse sequence</topic><topic>Pyruvic acid</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chen, Hsin‐Yu</creatorcontrib><creatorcontrib>Gordon, Jeremy W.</creatorcontrib><creatorcontrib>Bok, Robert A.</creatorcontrib><creatorcontrib>Cao, Peng</creatorcontrib><creatorcontrib>Morze, Cornelius</creatorcontrib><creatorcontrib>Criekinge, Mark</creatorcontrib><creatorcontrib>Milshteyn, Eugene</creatorcontrib><creatorcontrib>Carvajal, Lucas</creatorcontrib><creatorcontrib>Hurd, Ralph E.</creatorcontrib><creatorcontrib>Kurhanewicz, John</creatorcontrib><creatorcontrib>Vigneron, Daniel B.</creatorcontrib><creatorcontrib>Larson, Peder E.Z.</creatorcontrib><collection>Biotechnology Research Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>NMR in biomedicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chen, Hsin‐Yu</au><au>Gordon, Jeremy W.</au><au>Bok, Robert A.</au><au>Cao, Peng</au><au>Morze, Cornelius</au><au>Criekinge, Mark</au><au>Milshteyn, Eugene</au><au>Carvajal, Lucas</au><au>Hurd, Ralph E.</au><au>Kurhanewicz, John</au><au>Vigneron, Daniel B.</au><au>Larson, Peder E.Z.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Pulse sequence considerations for quantification of pyruvate‐to‐lactate conversion kPL in hyperpolarized 13C imaging</atitle><jtitle>NMR in biomedicine</jtitle><date>2019-03</date><risdate>2019</risdate><volume>32</volume><issue>3</issue><spage>e4052</spage><epage>n/a</epage><pages>e4052-n/a</pages><issn>0952-3480</issn><eissn>1099-1492</eissn><abstract>Hyperpolarized 13C MRI takes advantage of the unprecedented 50 000‐fold signal‐to‐noise ratio enhancement to interrogate cancer metabolism in patients and animals. It can measure the pyruvate‐to‐lactate conversion rate, kPL, a metabolic biomarker of cancer aggressiveness and progression. Therefore, it is crucial to evaluate kPL reliably. In this study, three sequence components and parameters that modulate kPL estimation were identified and investigated in model simulations and through in vivo animal studies using several specifically designed pulse sequences. These factors included a magnetization spoiling effect due to RF pulses, a crusher gradient‐induced flow suppression, and intrinsic image weightings due to relaxation. Simulation showed that the RF‐induced magnetization spoiling can be substantially improved using an inputless kPL fitting. In vivo studies found a significantly higher apparent kPL with an additional gradient that leads to flow suppression (kPL,FID‐Delay,Crush/kPL,FID‐Delay = 1.37 ± 0.33, P < 0.01, N = 6), which agrees with simulation outcomes (12.5% kPL error with Δv = 40 cm/s), indicating that the gradients predominantly suppressed flowing pyruvate spins. Significantly lower kPL was found using a delayed free induction decay (FID) acquisition versus a minimum‐TE version (kPL,FID‐Delay/kPL,FID = 0.67 ± 0.09, P < 0.01, N = 5), and the lactate peak had broader linewidth than pyruvate (Δωlactate/Δωpyruvate = 1.32 ± 0.07, P < 0.000 01, N = 13). This illustrated that lactate's T2*, shorter than that of pyruvate, can affect calculated kPL values. We also found that an FID sequence yielded significantly lower kPL versus a double spin‐echo sequence that includes spin‐echo spoiling, flow suppression from crusher gradients, and more T2 weighting (kPL,DSE/kPL,FID = 2.40 ± 0.98, P < 0.0001, N = 7). In summary, the pulse sequence, as well as its interaction with pharmacokinetics and the tissue microenvironment, can impact and be optimized for the measurement of kPL. The data acquisition and analysis pipelines can work synergistically to provide more robust and reproducible kPL measures for future preclinical and clinical studies.
This study investigated three pulse sequence components and parameters that can affect estimates of kPL in HP‐13C MRI. In vivo animal tumor studies showed significant impact on kPL with crusher‐gradient induced flow suppression, and intrinsic image weighting due to relaxation, corroborated by signal‐model simulations. RF‐induced magnetization spoiling can be substantially improved using an inputless kPL fitting. These outcomes suggested that the pulse sequence, as well as its interaction with pharmacokinetics and tissue microenvironment, can impact the measurement of kPL.</abstract><cop>Oxford</cop><pub>Wiley Subscription Services, Inc</pub><pmid>30664305</pmid><doi>10.1002/nbm.4052</doi><tpages>14</tpages><orcidid>https://orcid.org/0000-0003-4113-7671</orcidid><orcidid>https://orcid.org/0000-0003-4183-3634</orcidid><orcidid>https://orcid.org/0000-0002-2765-1685</orcidid></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0952-3480 |
| ispartof | NMR in biomedicine, 2019-03, Vol.32 (3), p.e4052-n/a |
| issn | 0952-3480 1099-1492 |
| language | eng |
| recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_6380928 |
| source | Wiley Online Library Journals Frontfile Complete |
| subjects | Biological products Biomarkers Cancer cancer imaging Carbon 13 Computer simulation Conversion Data acquisition Data processing Delay hyperpolarized C‐13 pyruvate In vivo methods and tests kinetic modeling Lactic acid Magnetic resonance imaging Magnetization Metabolism Parameter estimation Parameter identification Pharmacokinetics Pharmacology pulse sequence Pyruvic acid |
| title | Pulse sequence considerations for quantification of pyruvate‐to‐lactate conversion kPL in hyperpolarized 13C imaging |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-07T10%3A20%3A54IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Pulse%20sequence%20considerations%20for%20quantification%20of%20pyruvate%E2%80%90to%E2%80%90lactate%20conversion%20kPL%20in%20hyperpolarized%2013C%20imaging&rft.jtitle=NMR%20in%20biomedicine&rft.au=Chen,%20Hsin%E2%80%90Yu&rft.date=2019-03&rft.volume=32&rft.issue=3&rft.spage=e4052&rft.epage=n/a&rft.pages=e4052-n/a&rft.issn=0952-3480&rft.eissn=1099-1492&rft_id=info:doi/10.1002/nbm.4052&rft_dat=%3Cproquest_pubme%3E2183025489%3C/proquest_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2183025489&rft_id=info:pmid/30664305&rfr_iscdi=true |