DNA Aptamers for the Characterization of Histological Structure of Lung Adenocarcinoma
Bibliographic record
Abstract
Nucleic acid aptamers are becoming popular as molecular probes for identification and imaging pathology and, at the same time, as a convenient platform for targeted therapy. Recent studies have shown that aptamers may be effectively used for tumor characterization and as commercially available monoclonal antibodies. Here we present three DNA aptamers binding to whole transformed lung cancer tissues, including tumor cells, connective tissues, and blood vessels. Protein targets have been revealed using affinity purification followed by mass spectrometry analyses, and they have been validated using a panel of correspondent antibodies and 3D imaging of tumor tissues. Each of the proteins targeted by the aptamers is involved in cancer progression and most of them are crucial for lung adenocarcinoma. We propose the use of these aptamers in aptahistochemistry for the characterization of the histological structure of lung adenocarcinoma. The value of the presented aptamers is their application together or separately for indicating the spread of neoplastic transformation, for complex differential diagnostics, and for targeted therapy of the tumor itself as well as all transformed structures of the adjacent tissues. Moreover, it has been demonstrated that these aptamers could be used for intraoperative tumor visualization and margin assessment.
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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".