Development of Galectin-7-Specific Nanobodies: Implications for Immunotherapy and Molecular Imaging in Cancer
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Galectins play significant roles in regulating immune responses, posing challenges for cancer immunotherapy. The development of galectin inhibitors has been limited by their high structural homology and the lack of noninvasive imaging tools to identify potential responsive patients. We developed 12 galectin-7-specific inhibitors using nanobodies (Nbs) and identified G7N8 as the lead Nb. G7N8 was conjugated with the NOTA chelator, labeled with copper-64 ([ 64 Cu]Cu), and used as a radiotracer for PET imaging in a triple-negative breast cancer (TNBC) mouse model. Nbs demonstrated high affinity for galectin-7, with no binding activity for other galectins tested. The lead Nbs inhibited galectin-7 binding to T-cell glycoreceptors and reduced subsequent apoptosis. PET imaging with [ 64 Cu]Cu-NOTA-G7N8 showed selective radiotracer accumulation at 20 h ( P = 0.001). We developed galectin-7-specific Nbs that inhibit T-cell apoptosis and enable PET imaging of TNBC, providing novel tools for investigating immune regulation and enhancing cancer immunotherapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".