Aptamers based photodiagnostic
Bibliographic record
Abstract
Nucleic acid aptamers are molecules that are being used in a large number of biomedical applications. Aptamers have the properties to bind to a wide range of molecules with high specificity and affinity for their target. These properties together with their small size and their ease of synthesis make them very attractive and promising for targeting diseases. Aptamers can serve as cancer diagnostic tools by detecting specific biomarkers, circulating cancer cells or imaging diseased tissue. On the other hand, aptamers can be used as therapeutic agents due to their potential antagonist activity, or as targeting agents. The objective of this work is to use a fluorescent labelled aptamer to detect cancer cells. A nuclease resistant 2'Fluoro-Pyrimidine RNA aptamer has been selected using the cell-SELEX strategy and binds a cell surface biomarker that seems to be overexpressed in several cancers. Identification of its target is ongoing. A fluorescent moiety (Alexa Fluor probes) is added on the aptamer for labelling. We have characterised the affinity of this aptamer for several cancer cell lines using flow cytometry and binding experiments. An efficient binding has been obtained with the breast adenocarcinoma MCF-7 and the pharynx squamous cell carcinoma FaDu cell lines (92% and 82% labelling respectively). Localisation and internalisation studies of the fluorescent aptamer are also realised on the selected cell lines using fluorescent microscope. Finally, our ongoing studies aim the coupling of this aptamer to a chlorin loaded nanoparticle for aptamer-targeted photodynamic 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".