The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Kidney Transplant Rejection and Tissue Injury by Gene Profiling of Biopsies and Peripheral Blood Lymphocytes
Open accessAbstract
A major challenge for kidney transplantation is balancing the need for immunosuppression to prevent rejection, while minimizing drug-induced toxicities. We used DNA microarrays (HG-U95Av2 GeneChips, Affymetrix) to determine gene expression profiles for kidney biopsies and peripheral blood lymphocytes (PBLs) in transplant patients including normal donor kidneys, well-functioning transplants without rejection, kidneys undergoing acute rejection, and transplants with renal dysfunction without rejection. We developed a data analysis schema based on expression signal determination, class comparison and prediction, hierarchical clustering, statistical power analysis and real-time quantitative PCR validation. We identified distinct gene expression signatures for both biopsies and PBLs that correlated significantly with each of the different classes of transplant patients. This is the most complete report to date using commercial arrays to identify unique expression signatures in transplant biopsies distinguishing acute rejection, acute dysfunction without rejection and well-functioning transplants with no rejection history. We demonstrate for the first time the successful application of high density DNA chip analysis of PBL as a diagnostic tool for transplantation. The significance of these results, if validated in a multicenter prospective trial, would be the establishment of a metric based on gene expression signatures for monitoring the immune status and immunosuppression of transplanted patients.
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
In silico prediction of protein-protein interactions in human macrophages
Souiai, O., Guerfali, F., Ben Miled, S., Brun, C., & Benkahla, A. (2014) · BMC Research Notes · doi:10.1186/1756-0500-7-157 · Cited by 10,803
Significance analysis of microarrays applied to the ionizing radiation response
Tusher, V. G., Tibshirani, R., & Chu, G. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.091062498 · Cited by 10,568
Model-based analysis of oligonucleotide arrays: Expression index computation and outlier detection.
Li, C., & Wong, W. H. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.98.1.31 · Cited by 3,133
Expression monitoring by hybridization to high-density oligonucleotide arrays
Lockhart, D. J., Dong, H., Byrne, M. C., Follettie, M. T., Gallo, M. V., Chee, M. S., Mittmann, M., Wang, C., Kobayashi, M., Norton, H., et al. (1996) · Nature Biotechnology · doi:10.1038/nbt1296-1675 · Cited by 2,840
Lock, C., Hermans, G., Pedotti, R., Brendolan, A., Schadt, E., Garren, H., Langer-Gould, A., Strober, S., Cannella, B., Allard, J., et al. (2002) · Nature Medicine · doi:10.1038/nm0502-500 · Cited by 1,669
Pitfalls in the Use of DNA Microarray Data for Diagnostic and Prognostic Classification
Simon, R., Radmacher, M. D., Dobbin, K., & McShane, L. M. (2003) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/95.1.14 · Cited by 1,006
Li, C., & Hung Wong, W. (2001) · Genome Biology · doi:10.1186/gb-2001-2-8-research0032 · Cited by 976
Gordon, G. J., Jensen, R. V., Hsiao, L.-L., Gullans, S. R., Blumenstock, J. E., Ramaswamy, S., Richards, W. G., Sugarbaker, D. J., & Bueno, R. (2002) · PubMed · Cited by 859
Molecular Heterogeneity in Acute Renal Allograft Rejection Identified by DNA Microarray Profiling
Sarwal, M., Chua, M.-S., Kambham, N., Hsieh, S.-C., Satterwhite, T., Masek, M., & Salvatierra, O. (2003) · New England Journal of Medicine · doi:10.1056/nejmoa035588 · Cited by 701
Quantitative analysis of mRNA amplification by in vitro transcription
Baugh, L. R. (2001) · Nucleic Acids Research · doi:10.1093/nar/29.5.e29 · Cited by 498
Cited by
Solez, K., Colvin, R., Racusen, L., Sis, B., Halloran, P., Birk, P., Campbell, P., Cascalho, M., Collins, A., Demetris, A., et al. (2007) · American Journal of Transplantation · doi:10.1111/j.1600-6143.2006.01688.x · Cited by 1,082
Self-citationBrouard, S., Mansfield, E., Braud, C., Li, L., Giral, M., Hsieh, S.-C., Baeten, D., Zhang, M., Ashton-Chess, J., Braudeau, C., et al. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0705834104 · Cited by 373
Predictability of human differential gene expression
Crow, M., Lim, N., Ballouz, S., Pavlidis, P., & Gillis, J. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1802973116 · Cited by 117
MXRA 5 is a TGF‐β1‐regulated human protein with anti‐inflammatory and anti‐fibrotic properties
Poveda, J., Sanz, A. B., Fernandez‐Fernandez, B., Carrasco, S., Ruiz‐Ortega, M., Cannata‐Ortiz, P., Ortiz, A., & Sanchez‐Niño, M. D. (2017) · Journal of Cellular and Molecular Medicine · doi:10.1111/jcmm.12953 · Cited by 77
De Jonge, J., Kurian, S., Shaked, A., Reddy, K. R., Hancock, W., Salomon, D. R., & Olthoff, K. M. (2009) · American Journal of Transplantation · doi:10.1111/j.1600-6143.2009.02557.x · Cited by 54
Self-citation
Metadata
- Authors
- Flechner, S. M., Kurian, S. M., Head, S. R., Sharp, S. M., Whisenant, T. C., Zhang, J., Chismar, J. D., Horvath, S., Mondala, T., Gilmartin, T., Cook, D. J., Kay, S. A., Walker, J. R., & Salomon, D. R.
- Venue
- American Journal of Transplantation
- Publisher
- Elsevier BV
- Published
- 2004-09
- Volume
- 4
- Issue
- 9
- Pages
- 1475-1489
- ISSN
- 1600-6135 (ISSN-L)
- OpenAlex
- W2044737323
- Cited by
- 289
- References
- 18
- FWCI
- 10.99
- Type
- journal article
- Language
- en
- Open access
- hybrid
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Kidney Transplant Rejection and Tissue Injury by Gene Profiling of Biopsies and Peripheral Blood Lymphocytes. https://app.cochat.ai/papers/10.1111/j.1600-6143.2004.00526.x
- The paper
- Flechner, S. M., Kurian, S. M., Head, S. R., Sharp, S. M., Whisenant, T. C., Zhang, J., Chismar, J. D., Horvath, S., Mondala, T., Gilmartin, T., Cook, D. J., Kay, S. A., Walker, J. R., & Salomon, D. R. (2004). Kidney Transplant Rejection and Tissue Injury by Gene Profiling of Biopsies and Peripheral Blood Lymphocytes. American Journal of Transplantation. https://doi.org/10.1111/j.1600-6143.2004.00526.x
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.
