Development and characterization of aptamer-conjugated imaging tools for diagnostic applications
Bibliographic record
Abstract
Magnetic resonance imaging (MRI) and computed tomography (CT) are imaging modalities commonly used to assess blood flow; however, neither can label a blood clot that may either disrupt flow, causing an ischemic stroke, or have the ability to demonstrate where a cerebral hemorrhage has occurred (hemorrhagic stroke).To improve the diagnosis and treatment of blood vessel diseases, such as stroke and aneurysms, we developed fibrinogen aptamer (FA)-functionalized contrast agents, enabling the identification and labelling of blood clots.Fibrin was chosen as the target of interest as it is involved in blood clot formation and is, therefore, a major constituent of aforementioned conditions.Since FA was originally selected to bind fibrinogen, fibrin-binding validation was required.It was hypothesized that FA would retain some binding affinity towards the polymerized form, fibrin, given that most of the structural elements of fibrinogen remain unmodified in the final form.To assess the affinity and selectivity of FA towards nonsolution-based fibrin, FA was tagged with a green emitting fluorophore and fluorescence co-localization was monitored.FA was selective, and binding was immediate upon direct interaction, accumulating to a significant amount within minutes.Solubilized fibrin was also used in a number of binding validation studies, including microscale thermophoresis, isothermal titration calorimetry, and circular dichroism.These techniques were used to calculate the apparent Kd, which was found to be within the acceptable range when compared to the known Kd of FA to fibrinogen.Four different FA-targeted contrast materials were produced, including gadolinium conjugates (Gd(III)-DOTA/NOTA-FA) for MRI, iodinated-FA and FA-functionalized gold nanoparticles (FA-AuNPs) for CT, and FA-functionalized gold-coated iron-oxide
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".