Noninvasive localization of tumors by immunofluorescence imaging using a single chain Fv fragment of a human monoclonal antibody with broad cancer specificity
Bibliographic record
Abstract
BACKGROUND: A single chain antibody fragment, NovoMAb-G2-scFv, derived from a human anti-tumor monoclonal antibody recognizes tumor antigen molecules expressed on a wide variety of human cancers including melanoma, breast carcinoma, colon adenocarcinoma, squamous cell carcinoma, lung carcinoma, and prostate carcinoma. This study was designed to evaluate the use of a NovoMab-G2-scFv/cyanine fluorochrome (Cy5.5.18) conjugate as diagnostic tool for in vivo imaging of tumors. METHODS: The NovoMab-G2-scFv-Cy5 complex was administered to athymic mice injected subcutaneously with human melanoma tumor cells, and the distribution of fluorescence was imaged noninvasively using a charge-coupled device camera. Images were acquired 2, 6, 12, 24, 48, and 72 hours after injection. RESULTS: Fluorescence was detected at the tumor site after injection of NovoMab-G2-scFv-Cy5 but not after injection of a labeled irrelevant control antibody fragment. Fluorescence from the tumor site peaked 2 hours after injection and gradually declined, reaching a minimum 72 hours after injection. Fluorescence was also apparent in the kidneys, indicating clearance of the complex through the kidneys. Results suggest that 16% and 73% of the antibody is located in the tumor and kidneys, respectively. Imaging of isolated organs confirmed the presence of the NovoMab-G2-scFv-Cy5 complex in tumors, kidneys, and liver. No fluorescence was observed in other organs. CONCLUSIONS: Specific binding of the antibody-dye complex to the tumor was observed, and the kinetics of binding to tumors and kidneys were determined. These results suggest that the NovoMab-G2-scFv-Cy5.5 complex may be used for noninvasive tumor localization.
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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 | 0.000 | 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) | 0.001 | 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".