Bombesin‐functionalized water‐soluble gold nanoparticles for targeting prostate cancer
Bibliographic record
Abstract
Abstract Cancer targeting can be used for both tumor diagnosis and therapy. Recently, gold nanoparticles (AuNPs) have found utility in this field as they are very small in size, and thus display an enhanced permeability and retention effect, allowing them to be taken up by tumor cells through “passive targeting.” However, this accumulation is non‐specific. Conversely, AuNPs functionalized with targeting entities such as peptides, antibodies, or small molecules can specifically target tumors through interaction with cancer‐specific protein receptors. In this study, targeted AuNPs were developed using an azide‐modified peptide that was able to react with alkyne‐functionalized AuNPs through an interfacial strain‐promoted azide‐alkyne cycloaddition. Small (3 nm) AuNPs were made water‐soluble through PEGylation and functionalized with dibenzocyclooctyne to add the alkyne functionality. For the targeting entity, a pan‐bombesin peptide ([D‐Phe6,β‐Ala11,Phe13,Nle14]bombesin(6–14)) was chosen as it binds to all four receptor subtypes of the gastrin releasing peptide receptor, which is highly expressed in prostate cancer. Prostate cancer (PC‐3) cells were incubated with the targeted AuNPs and studied via transmission electron microscopy. AuNPs conjugated with bombesin showed higher accumulation in PC‐3 cells than either the blocking or control studies. These results suggest that these small, water‐soluble, bombesin‐functionalized AuNPs have potential applications in targeting prostate cancer as diagnostic or therapeutic entities.
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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".