Aptamers Selected to Postoperative Lung Adenocarcinoma Detect Circulating Tumor Cells in Human Blood
Bibliographic record
Abstract
Circulating tumor cells (CTCs) are rare cells and valuable clinical markers of prognosis of metastasis formation and prediction of patient survival. Most CTC analyses are based on the antibody-based detection of a few epithelial markers; therefore miss an important portion of mesenchymal cancer cells circulating in blood. In this work, we selected and identified DNA aptamers as specific affinity probes that bind to lung adenocarcinoma cells derived from postoperative tissues. The unique feature of our selection strategy is that aptamers are produced for lung cancer cell biomarkers in their native state and conformation without previous knowledge of the biomarkers. The aptamers did not bind to normal lung cells and lymphocytes, and had very low affinity to A549 lung adenocarcinoma culture. We applied these aptamers to detect CTCs, apoptotic bodies, and microemboli in clinical samples of peripheral blood of lung cancer and metastatic lung cancer patients. We identified aptamer-associated protein biomarkers for lung cancer such as vimentin, annexin A2, annexin A5, histone 2B, neutrophil defensin, and clusterin. Tumor-specific aptamers can be produced for individual patients and synthesized many times during anticancer therapy, thereby opening up the possibility of personalized diagnostics.
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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".