Visualization of Brain Tumors with Infrared-Labeled Aptamers for Fluorescence-Guided Surgery
Bibliographic record
Abstract
Gliomas remain challenging brain tumors to treat due to their infiltrative nature. Accurately identifying tumor boundaries during surgery is crucial for successful resection. This study introduces an innovative intraoperative visualization method utilizing surgical fluorescence microscopy to precisely locate tumor cell dissemination. Here, the focus is on the development of a novel contrasting agent (IR-Glint) for intraoperative visualization of human glial tumors comprising infrared-labeled Glint aptamers. The specificity of IR-Glint is assessed using flow cytometry and microscopy on primary cell cultures. In vivo effectiveness is studied on mouse and rabbit models, employing orthotopic xenotransplantation of human brain gliomas with various imaging techniques, including PET/CT, in vivo fluorescence visualization, confocal laser scanning, and surgical microscopy. The experiments validate the potential of IR-Glint for the intraoperative visualization of gliomas using infrared imaging. IR-Glint penetrates the blood-brain barrier and can be used for both intravenous and surface applications, allowing clear visualization of the tumor. The surface application directly to the brain reduces the dosage required and mitigates potential toxic effects on the patient. The research shows the potential of infrared dye-labeled aptamers for accurately visualizing glial tumors during brain surgery. This novel aptamer-assisted fluorescence-guided surgery (AptaFGS) may pave the way for future advancements in the field of neurosurgery.
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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".