Faculty Opinions recommendation of In vivo internal tumor illumination by telomerase-dependent adenoviral GFP for precise surgical navigation.
Bibliographic record
Abstract
Cancer surgery requires the complete and precise identification of malignant tissue margins including the smallest disseminated lesions.Internal green fluorescent protein (GFP) fluorescence can intensely illuminate even single cells but requires GFP sequence transcription within the cell.Introducing and selectively activating the GFP gene in malignant tissue in vivo is made possible by the development of OBP-401, a telomerase-dependent, replicationcompetent adenovirus expressing GFP.This potentially powerful adjunct to surgical navigation was demonstrated in 2 nude mouse models that represent difficult surgical challenges-the resection of widely disseminated cancer.HCT-116, a model of intraperitoneal disseminated human colon cancer, was labeled by virus injection into the peritoneal cavity.A549, a model of pleural dissemination of human lung cancer, was labeled by virus administered into the pleural cavity.Only the malignant tissue fluoresced brightly in both models.In the intraperitoneal model of disseminated cancer, fluorescence-guided surgery enabled resection of all tumor nodules labeled with GFP by OBP-401.The data in this report suggest that adenoviral-GFP labeling tumors in patients can enable fluorescence-guided surgical navigation.Adenovirus ͉ green fluorescent protein ͉ metastasis
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.080 | 0.045 |
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".