Satellite-like Gold Nanocomposites for Targeted Mass Spectrometry Imaging of Tumor Tissues
Why this work is in the frame
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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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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 | 0.000 | 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 | 0.001 | 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 it