Detection of the BCR-ABL Leukemia Gene Fusion using Chip-based Electrochemical Assay
Bibliographic record
Abstract
Ability to diagnose cancer before it progresses into advanced stages is highly desirable for the best treatment outcome. A sensitive test to analyze complex samples for specific cancer biomarkers would provide with important prognostic information and help to select the best treatment regimen. A highly robust, ultra sensitive and cost-effective electronic chip platform was used to detect nucleic acid biomarkers in heterogeneous biological samples without any amplification or purification. Chronic myelogenous leukemia (CML) was chosen as a model disease due to its hallmark genetic abnormality. This disease state therefore has an ideal market to test the detection of the fusion transcripts in complex samples, such as blood. It was shown that the CML-related fusion can be detected from unpurified cell lysates and as low as 10 cells were needed for detection. Finally, patient samples were analyzed using the assay and the fusion transcripts were accurately identified in all of them.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".