A High-Throughput Screening Assay for NKCC1 Cotransporter Using Nonradioactive Rubidium Flux Technology
Bibliographic record
Abstract
A high-throughput screening (HTS) assay was developed for cotransporter, NKCC1, which is a potential target for the treatment of diverse disorders. This nonradioactive rubidium flux assay coupled with ion channel reader series provides a working screen for this target expressed in human embryonic kidney (HEK) cell line. An eightfold window of detection was achieved with the optimized assay. This new functional assay offered a robust working model for NKCC1 in determining reliable and concordant rank orders of the test compounds supporting its sensitivity and specificity. The robustness of manual assay indicated by Z' of 0.9 qualified its amenability to automation. The Z' of 0.7 was displayed by automated assay employed in high-throughput screening of compound libraries against this target. Being electrically neutral, the NKCC1 screening is difficult to achieve by both manual and automated electrophysiological techniques. These techniques, although considered gold standard, suffer from their inherent problems of being too slow to be in high-throughput format and with high running costs. In addition to being a functional assay for NKCC1, it is nontoxic as compared with thallium flux assay, which is prone to generate high number of false-positive/false-negative rates because of its innate fluorescence issues.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".