Evaluation of nimotuzumab Fab2 as an optical imaging agent in EGFR positive cancers
Bibliographic record
Abstract
Abstract Molecular-targeted imaging probes can be used with a variety of imaging modalities to detect diseased tissues and guide their removal. EGFR is a useful biomarker for a variety of cancers, because it is expressed at high levels relative to normal tissues. Previously, we showed the anti-EGFR antibody nimotuzumab can be used as a positron emission tomography and fluorescent imaging probe for EGFR positive cancers in mice. These imaging probes are currently in clinical trials for PET imaging and image-guided surgery, respectively. One issue with using antibody probes for imaging is their long circulation time and slow tissue penetration, which requires patients to wait a few days after injection before imaging or surgery, multiple visits and longer radiation exposure. Here, we generated a Fab2 fragment of nimotuzumab, by pepsin digestion and labeled it with IRDye800CW to evaluate its optical imaging properties. The Fab2 had faster tumor accumulation and clearance in mice relative to the nimotuzumab IgG. The fluorescent signal peaked at 2 h post injection and remained high until 6 h post injection. The properties of the Fab2 allow a higher signal to background to be obtained in a shorter time frame, reducing the wait time for imaging after probe infusion.
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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 | 0.000 |
| 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) | 0.001 | 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".