PS2 - 178 Brevican-Specific Peptides for the Development of Next-Generation Targeted Theranostics for High Grade Gliomas
Bibliographic record
Abstract
High-grade gliomas are deadly cancers, and current standard-of-care has demonstrated limited success. The ability to specifically target glioma cells can allow for the development of improved theranostic agents leading to better detection methods, as well as safer anti-cancer therapies. Brevican (Bcan), a CNS-specific protein is upregulated in glioma cells and correlates with tumor progression. Particularly, a Bcan isoform lacking normal glycosylation, called B/bDg is a unique glioma marker and is not expressed in non-cancerous tissues. Therefore, B/bDg represents a valuable target for anti-cancer strategies. We describe here the discovery of novel high-affinity B/bDg-targeted peptides using rapid combinatorial library screening approaches and a microfluidic sorting device of our own design. Briefly, a one-bead-one-compound (OBOC) peptide library was screened against small magnetic particles decorated with B/bDg. Positive “hit” beads labeled with magnetic particles were isolated using an inexpensive but yet, accurate and high-throughput in-house microfluidic magnetic-activated sorter. These hits were exposed to cells expressing B/bDg, and beads with the highest cell association were isolated and sequenced. Seven novel peptides were identified. Cell uptake analyses and blocking studies revealed that 5 of these peptides displayed specific uptake in B/bDg-overexpressing cells. These candidates displayed nano-/micromolar binding affinity for recombinant B/bDg protein. Further analyses of these candidates using confocal microscopy revealed increased peptide binding/uptake in patient-derived glioma stem cells (GSCs) compared with primary human astrocytes. We plan to incorporate these onto multi-functional BBB-penetrating nanoparticles loaded with imaging agents or a drug payload to translate them into highly selective and efficacious brain cancer theranostic agents.
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) | 0.001 | 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 | 0.001 |
| 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".