Polymer nanofiber-based microchips for EGFR mutation analysis of circulating tumor cells in lung adenocarcinoma
Bibliographic record
Abstract
BACKGROUND: Circulating tumor cells (CTCs) detection, an approach considered to be "liquid biopsy", is a potential alternative method in clinical use for early diagnosis of solid tumor progression. METHODS: -glycolic acid) (PLGA) - nanofiber (PN)-NanoVelcro chip as an efficient device for simple and rapid capture of CTCs from peripheral blood. We evaluated the device performance by assessing the capture efficiency and purity. Single CTC was isolated via laser microdissection system for subsequent genetic analysis, with an aim to find the concordance of epidermal growth factor receptor (EGFR) mutations between tumor tissue and CTCs. RESULTS: PN-NanoVelcro chip exhibits great performance in capture efficiency and high purity. The genetic analysis results showed that most EGFR mutation in tumor tissue could also be detected in CTCs. CONCLUSION: Compared to computed tomography image results, CTC detection can be implemented throughout the course of diseases and provides an accurate and earlier diagnosis of tumor progression, which make it possible for patients to acquire suitable and timely treatment.
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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 | 0.000 |
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".