<i>IN VIVO</i> NEAR-INFRARED FLUORESCENCE IMAGING OF HUMAN COLON ADENOCARCINOMA BY SPECIFIC IMMUNOTARGETING OF A TUMOR-ASSOCIATED MUCIN
Bibliographic record
Abstract
Background and Aims: Accurate endoscopic detection of premalignant lesions and early cancers in the colon is essential for cure, since prognosis is closely related to lesion size and stage. Although it has great clinical potential, autofluorescence endoscopy has limited tumor-to-normal tissue image contrast for detecting small preneoplastic lesions. We have developed a molecularly specific, near-infrared fluorescent monoclonal antibody (CC49) bioconjugate which targets tumor-associated glycoprotein 72 (TAG72), as a contrast agent to improve fluorescence-based endoscopy of colon cancer. Methods: The fluorescent anti-TAG72 conjugate was evaluated in vitro and in vivo in athymic nude mice bearing human colon adenocarcinoma (LS174T) subcutaneous tumors. Autofluorescence, a fluorescent but irrelevant antibody and the free fluorescent dye served as controls. Fluorescent agents were injected intravenously, and in vivo whole body fluorescence imaging was performed at various time points to determine pharmacokinetics, followed by ex vivo tissue analysis by confocal fluorescence microscopy and histology. Results: Fluorescence microscopy and histology confirmed specific LS174T cell membrane targeting of labeled CC49 in vitro and ex vivo. In vivo fluorescence imaging demonstrated significant tumor-to-normal tissue contrast enhancement with labeled-CC49 at three hours post injection, with maximum contrast after 48 h. Accumulation of tumor fluorescence demonstrated that modification of CC49 antibodies did not alter their specific tumor-localizing properties, and was antibody-dependent since controls did not produce detectable tumor fluorescence. Conclusions: These results show proof-of-principle that our near-infrared fluorescent-antibody probe targeting a tumor-associated mucin detects colonic tumors at the molecular level in real time, and offer a basis for future improvement of image contrast during clinical fluorescence endoscopy.
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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".