The influence of physiological environment on the targeting effect of aptamer-guided gold nanoparticles
Bibliographic record
Abstract
Aptamer guided nanomedicine shows great promise in targeted cancer therapies. However the loss of targeting capacity during in vivo or clinical trials has largely hindered its popularity and there are no systematic studies to elucidate the causes. Herein, we investigated such loss of targeting capacity by examining how the physiological milieu affected targeting effect. Aptamer functionalized gold nanoparticle (AuNP) was chosen as the model and exposed to human blood serum that is used to mimic physiological milieu. Dynamic light scattering (DLS), flow cytometry and label-free liquid chromatography tandem mass spectrometry (LC-MS/MS) were employed to determine variations of NPs’ surface chemistry and biological identities changes after serum exposure. Results showed that the targeting ability loss was caused by protein corona blocking, replacement and enzymatic cleavage of surface aptamer targeting ligands. Noteworthy, the aggregation issue is critical for the smaller NPs. Analysis of the protein corona profile indicated the accumulation of immune-related proteins on the surface of aptamer-conjugated NPs, which could induce immune response, resulting in rapid clearance of NPs.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".