Direct quantification of polo-like kinase 1 activity in cells and tissues using a highly sensitive and specific ELISA assay
Bibliographic record
Abstract
Polo-like kinase 1 (Plk1) plays a pivotal role in the regulation of cellular proliferation. Plk1 is overexpressed in approximately 80% of human tumors of diverse origins, and overexpression of Plk1 promotes neoplastic transformation of human cells. A growing body of evidence suggests that deregulation of Plk1 closely correlates with prognosis of various cancers in humans. Thus, accurate assessment of Plk1 deregulation would provide clear clinical advantages. However, because of the limited amount of cancer tissues available, quantification of the Plk1 activity has not been feasible. Here, we report the development of a rapid, highly sensitive, and specific ELISA-based Plk1 assay that can quantify the level of Plk1 activity with a small amount (2-20 microg) of total cellular proteins. Unlike the conventional immunocomplex kinase assay, this assay directly utilizes total cellular lysates and does not require a Plk1 enrichment step such as immunoprecipitation or affinity purification. Using this assay, we demonstrated that Plk1 activity is elevated in tumors but not in the surrounding normal tissues and that the level of Plk1 activity significantly diminishes after an antiproliferative chemotherapy. The method described here may provide an innovative tool for assessing the predisposition for cancer development, monitoring early tumor response after therapy, and estimating the prognosis of patients with cancers from multiple organ sites.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".