The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Magnetic resonance imaging of tumor glycolysis using hyperpolarized 13C-labeled glucose
Open accessAbstract
In this study, we monitored glycolysis in mouse lymphoma and lung tumors by measuring the conversion of hyperpolarized [U-2H, U-13C]glucose to lactate using 13C magnetic resonance spectroscopy and spectroscopic imaging. We observed labeled lactate only in tumors and not in surrounding normal tissue or other tissues in the body and found that it was markedly decreased at 24 h after treatment with a chemotherapeutic drug. We also detected an increase in a resonance assigned to 6-phosphogluconate in the pentose phosphate pathway. This technique could provide a new way of detecting early evidence of tumor treatment response in the clinic and of monitoring tumor pentose phosphate pathway activity.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Understanding the Warburg Effect: The Metabolic Requirements of Cell Proliferation
Vander Heiden, M. G., Cantley, L. C., & Thompson, C. B. (2009) · Science · doi:10.1126/science.1160809 · Cited by 16,197
Regulation of cancer cell metabolism
Cairns, R. A., Harris, I. S., & Mak, T. W. (2011) · Nature Reviews Cancer · doi:10.1038/nrc2981 · Cited by 4,744
Cancer cell metabolism: warburg and beyond
Hsu, P. P., & Sabatini, D. M. (2008) · Cell · doi:10.1016/j.cell.2008.08.021 · Cited by 2,452
Kurhanewicz, J., Vigneron, D. B., Brindle, K., Chekmenev, E. Y., Comment, A., Cunningham, C. H., DeBerardinis, R. J., Green, G. G., Leach, M. O., Rajan, S. S., et al. (2011) · Neoplasia · doi:10.1593/neo.101102 · Cited by 681
Self-citationMadsen, P. L., Cruz, N. F., Sokoloff, L., & Dienel, G. A. (1999) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1097/00004647-199904000-00005 · Cited by 174
Cited by
The return of metabolism: biochemistry and physiology of the pentose phosphate pathway
Stincone, A., Prigione, A., Cramer, T., Wamelink, M. M. C., Campbell, K., Cheung, E., Olin‐Sandoval, V., Grüning, N., Krüger, A., Tauqeer Alam, M., et al. (2015) · Biological Reviews · doi:10.1111/brv.12140 · Cited by 1,576
Self-citationEnvironment impacts the metabolic dependencies of Ras-driven non-small cell lung cancer
Davidson, S. M., Papagiannakopoulos, T., Olenchock, B. A., Heyman, J. E., Keibler, M. A., Luengo, A., Bauer, M. R., Jha, A. K., O’Brien, J. P., Pierce, K. A., et al. (2016) · Cell Metabolism · doi:10.1016/j.cmet.2016.01.007 · Cited by 737
Toward understanding and exploiting tumor heterogeneity
Alizadeh, A. A., Aranda, V., Bardelli, A., Blanpain, C., Bock, C., Borowski, C., Caldas, C., Califano, A., Doherty, M., Elsner, M., et al. (2015) · Nature Medicine · doi:10.1038/nm.3915 · Cited by 746
Exhaustion and senescence: two crucial dysfunctional states of T cells in the tumor microenvironment
Zhao, Y., Shao, Q., & Peng, G. (2020) · Cellular & Molecular Immunology · doi:10.1038/s41423-019-0344-8 · Cited by 382
Targeting Cancer Metabolism: Dietary and Pharmacologic Interventions
Vernieri, C., Casola, S., Foiani, M., Pietrantonio, F., de Braud, F., & Longo, V. (2016) · Cancer Discovery · doi:10.1158/2159-8290.cd-16-0615 · Cited by 202
Imaging of glucose metabolism by 13C-MRI distinguishes pancreatic cancer subtypes in mice
Kishimoto, S., Brender, J. R., Crooks, D. R., Matsumoto, S., Seki, T., Oshima, N., Merkle, H., Lin, P., Reed, G., Chen, A. P., et al. (2019) · eLife · doi:10.7554/elife.46312 · Cited by 41
Imaging cancer metabolism using magnetic resonance
Brindle, K. M. (2024) · npj Imaging · doi:10.1038/s44303-023-00004-0 · Cited by 22
Self-citation
Metadata
- Authors
- Rodrigues, T. B., Serrao, E. M., Kennedy, B. W. C., Hu, D.-E., Kettunen, M. I., & Brindle, K. M.
- Venue
- Nature Medicine
- Published
- 2013-12-08
- Volume
- 20
- Issue
- 1
- Pages
- 93-97
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- OpenAlex
- W2093970530
- Cited by
- 274
- References
- 5
- FWCI
- 11.28
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
