Thrombo-tag, an <i>in vivo</i> formed nanotracer for the detection of thrombi in mice by fast pre-targeted molecular imaging
Bibliographic record
Abstract
Radioisotope-labelled nanoparticles permit novel applications in molecular imaging, while recent developments in imaging have enabled direct visualization of biological processes. While this holds true for pathological processes that are stable in time, such as cancer, imaging approaches are limited for phenomena that take place in the range of minutes, such as thrombotic events. Here, we take advantage of bioorthogonal chemistry to demonstrate the concept of nanoparticle-based fast pre-targeted imaging. Using a newly designed nanoparticle that targets platelets we show the applicability of this approach developing thrombo-tag, an in vivo produced nanoparticle that labels thrombi. We show that thrombo-tag allows specific labelling of platelets that accumulate in the injured pulmonary vasculature, or that aggregate in brains of mice suffering thrombotic processes. The fast kinetics and high specificity features of thrombo-tag may critically expand the application of molecular imaging to the most prevalent and debilitating diseases in the clinics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".