SCDT-45. ANTIBODY-BASED PET IMAGING OF BRAIN CANCER CELLS INFILTRATION
Bibliographic record
Abstract
Infiltrated glioblastoma (GBM) cells into the brain parenchyma cause recurrences after tumor resection and there is presently no efficient non-invasive method to detect these infiltrated cells. One obstacle is the blood-brain barrier (BBB), which limits the passage of specific radiolabeled antibodies targeting infiltrated cells to the central nervous system, preventing imaging by positron emission tomography (PET). We hereby aimed to conceive bi-specific radiolabeled antibodies acting in two chronological steps: 1- targeting a transporter to allow receptor-mediated transcytosis through the BBB and 2- targeting a specific biomarker of GBM cells for a specific retention and imaging. We will present the first part of the project consisting to demonstrate that the mono-specific radiolabeled antibody targeting BBB-transporter can actively cross the BBB of healthy rat following injection in the right external carotid artery. This method allows for high tracer concentration in the right hemisphere after first passage following the injection. Comparing the specific radiolabeled antibody to a non-specific antibody, we observe that only the BBB-transmigrating antibody is momentary retained at the BBB and then returns in the blood circulation. We will next assess whether this transitory uptake to the BBB is sufficient to let the bi-specific antibody reaches and link a specific antigen present on the migrating GBM cells. This study demonstrates that our radiolabeled antibody allows for a transitory and specific uptake to the BBB. These successful results are promising for the use of bi-specific radiolabeled antibody targeting infiltrated GBM cells thus should enabling for specific PET imaging.
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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".