Novel detection method for chemiluminescence derived from the Kinase-Glo luminescent kinase assay platform: Advantages over traditional microplate luminometers
Bibliographic record
Abstract
The efficacy of cellular signal transduction is of paramount importance for the proper functioning of a cell and an organism as a whole. Protein kinases are responsible for much of this transmission and thus have been the focal point of extensive research. While there are numerous commercially available protein kinase assays, the Kinase-Glo luminescent kinase assay (Promega) provides an easy-to-use and high throughput platform for determining protein kinase activity. This assay is said to require the use of a microplate spectrophotometer capable of detecting a luminescent signal. This study shows that:•The ChemiGenius Bioimaging system (Syngene), typically used for visualizing chemiluminescence from Western blots, provides an alternative detection system for Kinase-Glo luminescence.•The novel detection system confers an advantage over traditional luminometers, in that it allows visualization of the luminescent wells, which allows for the real-time analysis and correction of experimental errors (i.e. bubble formation).•Determining kinase kinetics using this detection system produced comparable results to previous studies on the same enzyme (i.e. glycogen synthase kinase 3).
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".