Improved tumor contrast achieved by single time point dual-reporter fluorescence imaging
Bibliographic record
Abstract
In this study, we demonstrate a method to quantify biomarker expression that uses an exogenous dual-reporter imaging approach to improve tumor signal detection. The uptake of two fluorophores, one nonspecific and one targeted to the epidermal growth factor receptor (EGFR), were imaged at 1 h in three types of xenograft tumors spanning a range of EGFR expression levels (n=6 in each group). Using this dual-reporter imaging methodology, tumor contrast-to-noise ratio was amplified by >6 times at 1 h postinjection and >2 times at 24 h. Furthermore, by as early as 20 min postinjection, the dual-reporter imaging signal in the tumor correlated significantly with a validated marker of receptor density (P<0.05, r=0.93). Dual-reporter imaging can improve sensitivity and specificity over conventional fluorescence imaging in applications such as fluorescence-guided surgery and directly approximates the receptor status of the tumor, a measure that could be used to inform choices of biological therapies.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".