The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Recent and potential developments of biofluid analyses in metabolomics
Abstract
Metabolomics, one of the 'omic' sciences in systems biology, is the global assessment and validation of endogenous small-molecule metabolites within a biologic system. Analysis of these key metabolites in body fluids has become an important role to monitor the state of biological organisms and is a widely used diagnostic tool for disease. A majority of these metabolites are being applied to metabolic profiling of the biological samples, for example, plasma and whole blood, serum, urine, saliva, cerebrospinal fluid, synovial fluid, semen, and tissue homogenates. However, the recognition of the need for a holistic approach to metabolism led to the application of metabolomics to biological fluids for disease diagnostics. A recent surge in metabolomic applications which are probably more accurate than routine clinical practice, dedicated to characterizing the biological fluids. While developments in the analysis of biofluid samples encompassing an important impediment, it must be emphasized that these biofluids are complementary. Metabolomics provides potential advantages that classical diagnostic approaches do not, based on following discovery of a suite of clinically relevant biomarkers that are simultaneously affected by the disease. Emerging as a promising biofocus, metabolomics will drive biofluid analyses and offer great benefits for public health in the long-term.
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
Human metabolic phenotype diversity and its association with diet and blood pressure
Holmes, E., Loo, R. L., Stamler, J., Bictash, M., Yap, I. K. S., Chan, Q., Ebbels, T., De Iorio, M., Brown, I. J., Veselkov, K. A., et al. (2008) · Nature · doi:10.1038/nature06882 · Cited by 1,054
Human metabolic individuality in biomedical and pharmaceutical research
CARDIoGRAM, Suhre, K., Shin, S.-Y., Petersen, A.-K., Mohney, R. P., Meredith, D., Wägele, B., Altmaier, E., Deloukas, P., Erdmann, J., et al. (2011) · Nature · doi:10.1038/nature10354 · Cited by 1,017
Pharmaco-metabonomic phenotyping and personalized drug treatment
Andrew Clayton, T., Lindon, J. C., Cloarec, O., Antti, H., Charuel, C., Hanton, G., Provost, J.-P., Le Net, J.-L., Baker, D., Walley, R. J., et al. (2006) · Nature · doi:10.1038/nature04648 · Cited by 824
Llorach, R., Urpi-Sarda, M., Jauregui, O., Monagas, M., & Andres-Lacueva, C. (2009) · Journal of Proteome Research · doi:10.1021/pr900470a · Cited by 144
Xu, J., Zhang, J., Cai, S., Dong, J., Yang, J. Y., & Chen, Z. (2009) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-009-2623-1 · Cited by 43
Cited by
Dutta, M., Joshi, M., Srivastava, S., Lodh, I., Chakravarty, B., & Chaudhury, K. (2012) · Molecular BioSystems · doi:10.1039/c2mb25353d · Cited by 121
Metadata
- Authors
- Zhang, A., Sun, H., Wang, P., Han, Y., & Wang, X.
- Venue
- Journal of Proteomics
- Publisher
- Elsevier BV
- Published
- 2012-02
- Volume
- 75
- Issue
- 4
- Pages
- 1079-1088
- ISSN
- 1874-3919 (ISSN-L)
- OpenAlex
- W2053585277
- Cited by
- 276
- References
- 5
- FWCI
- 9.29
- Type
- journal article
- Language
- en
- Open access
- closed
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.