Protein microarrays identify antibodies to protein kinase Cζ that are associated with a greater risk of allograft loss in pediatric renal transplant recipients is a research paper published in Kidney International (2009). On theSindex it has a DataRank of 2.1. It has been cited 60 times, with 39 citing works in its 1-hop citation network.
Antibodies to human leukocyte antigens (HLAs) are a risk factor for acute renal allograft rejection and loss. The role of non-HLAs and their significance to allograft rejection have gained recent attention. Here, we applied protein microarray technology, with the capacity to simultaneously identify 5056 potential antigen targets, to assess non-HLA antibody formation in 15 pediatric renal transplant recipients during allograft rejection. Comparison of the pre- and post-transplant serum identified de novo antibodies to 229 non-HLA targets, 36 of which were present in multiple patients at allograft rejection. On the basis of its reactivity, protein kinase Czeta (PKCzeta) was selected for confirmatory testing and clinical study. Immunohistochemical analysis found PKCzeta both within the renal tissue and infiltrating lymphocytes at rejection. Patients who had an elevated anti-PKCzeta titer developed rejection, which was significantly more likely to result in graft loss. The absence of C4d deposition in patients with high anti-PKCzeta titers suggests that it is a marker of severe allograft injury rather than itself being pathogenic. Presumably, critical renal injury and inflammation associated with this rejection subtype lead to the immunological exposure of PKCzeta with resultant antibody formation. Prospective assessment of serum anti-PKCzeta levels at allograft rejection will be needed to confirm these results.
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Base Score Contribution
0.617
From this paper's citation signal
Citation Network Contribution
1.5
From 32 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 30% comes from its base citations and 70% from the citation network (32 citing papers contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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