In vivo spectroscopic photoacoustic imaging and laser‐induced nanoparticle vaporization for <scp>anti‐HER2</scp> breast cancer
Bibliographic record
Abstract
This study reports on the development and application of theragnostic agents targeting the HER2 receptors in breast tumors. The agent was constructed by loading silica-coated gold nanorods (GNRs) and a perfluorohexane liquid into PLGA-PEG nanoparticles, followed by surface conjugation with antibody Herceptin. The particle uptake in human breast cancer MDA-MB-231 (HER2-negative) and BT474 (HER2-positive) cell lines was tested. A proof of principle in vivo study was also performed using a xenograft mouse bilateral tumor model (16 mice, 32 tumors). Photoacoustic imaging was performed using a VevoLAZR device at 720/750/850 nm illuminations and 21 MHz central frequency. The relative concentrations of GNRs in the tumor were quantified using a linear spectral unmixing technique. The therapeutic efficacy of these nanoparticles was evaluated through optical droplet vaporization, and cell damage was confirmed using tissue immunofluorescence and histology. Our results demonstrate the potential of PLGA-GNRs as theragnostic agents for anti-HER2 breast cancer therapy.
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) | 0.000 | 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".