Detecting EGFR Gene Mutations on a Nanobioarray Chip
Bibliographic record
Abstract
In this study, three point mutations of EGFR relevant to lung cancer therapy are detected. Mutated EGFR is the target of a therapy for non-small cell lung cancer (NSCLC) using tyrosine kinase inhibitors (TKIs) as treatment drugs. Background/Objectives: Point mutations in exon 21 (L858R and L861Q) of the EGFR gene are TKI-sensitive; however, mutations in exon 20 (T790M) are TKI-resistant. Therefore, a fast detection method that classifies a NSCLC patient to be drug sensitive or drug resistant is highly clinically relevant. Methods: Probes were designed to detect three point mutations in genomic samples based on DNA hybridization on a solid surface. A method has been developed to detect single nucleotide polymorphism (SNP) for these mutation detections in the 16-channel nanobioarray chip. The wash by gold-nanoparticles (AuNP) was used to assist the differentiation detection Results: The gold nanoparticle-assisted wash method has enhanced differentiation between WT and mutated sequences relevant to the EGFR sensitivity to tyrosine kinase inhibitors. Conclusions: The WT and mutated sequences (T790M, L858R and L861Q) in genomic samples were successfully differentiated from each other.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".