Dual-tracer receptor concentration imaging using tracers with different tissue delivery kinetics
Bibliographic record
Abstract
Simultaneous dynamic fluorescent imaging of a suitable untargeted tracer in conjunction with any molecular targeted fluorescent agent has been shown to be a powerful approach for quantifying cancer-specific cell surface receptors in vivo in the presence of non-specific uptake and tracer delivery variability. The identification of a “suitable” untargeted tracer (i.e., one having equivalent plasma and tissue delivery pharmacokinetics to the targeted tracer) for every targeted tracer, however, may not always be feasible or could require extensive testing. This work presents a “deconvolution” approach capable of correcting for plasma and tissue-delivery pharmacokinetic differences between tracers by quantifying dynamic differences in targeted and untargeted tracer uptake in a receptor-free tissue (one devoid of targeted molecular species) and correcting uptake in all other tissues accordingly. This deconvolution correction approach is evaluated in theoretical models and explored in an in vivo mouse xenograft model of human glioma. In the animal experiments, epidermal growth factor receptor (EGFR: a receptor known to be overexpressed in the investigated glioma cell line) was targeted using a fluorescent tracer with very different plasma pharmacokinetics than a second untargeted fluorescent tracer. Without correcting for these differences, the dual-tracer approach yielded substantially higher estimations of EGFR concentration in all tissues than expected; however, deconvolution correction was able to produce estimates that matched ex vivo validation.
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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".