Satellite-like Gold Nanocomposites for Targeted Mass Spectrometry Imaging of Tumor Tissues
Bibliographic record
Abstract
We have developed a simple, rapid, high-throughput cancer diagnosis system using functional nanoparticles (NPs) consisting of poly(catechin) capped-gold NPs (Au@PC NPs) and smaller nucleolin-binding aptamer (AS1411) conjugated gold NPs (AS1411-Au NPs).The AS1411-Au NPs/Au@PC NP is used as a targeting agent in laser desorption/ionization mass spectrometry (LDI-MS)-based tumor tissue imaging.Self-assembled core-shell Au@PC NPs are synthesized by a simple reaction of tetrachloroaurate(III) with catechin.Au@PC NPs with a well-defined and dense poly(catechin) shell (~40-60 nm) on the surface of each Au core (~60-80 nm) are obtained through careful control of the ratio of catechin to gold ions, as well as the pH of the reaction solution.Furthermore, we have shown that AS1411-conjugated Au NPs (13-nm) self-assembled on Au@PC NP can from a satellite-like gold nanocomposite.The high density of AS1411-Au NPs on the surface of Au@PC NP enhances multivalent binding with nucleolin molecules on tumor cell membranes.We have employed LDI-MS to detect AS1411-Au NPs/Au@PC NPs labeled nucleolin-overexpressing MCF-7 breast cancer cells through the monitoring of Au cluster ions ([Aun] + ; 1 ≤ n ≤ 3).The ultrahigh signal amplification from Au NPs through the formation of a huge number of [Au n ] + ions results in a sensing platform with a limit of detection of 100 MCF-7 cells mL -1 .Further, we have applied the satellite-like AS1411-Au NPs/Au@PC NP nanocomposite as a labeling agent for tumor tissue imaging by LDI-MS.Our nanocomposite-assisted LDI-MS imaging platform can be extended for simultaneous analysis of different tumor markers on cell membranes when using different ligand-modified metal nanoparticles.
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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".