A Laser-Activated Biocompatible Theranostic Nanoagent for Targeted Multimodal Imaging and Photothermal Therapy
Bibliographic record
Abstract
Multifunctional nanoparticles have been reported for cancer detection and treatment currently. However, the accurate diagnosis and efficient treatment for tumors are still not satisfied. Here we report on the development of targeted phase change multimodal polymeric nanoparticles for the imaging and treatment of HER2-positive breast cancer. Methods: We evaluated the multimodal imaging capabilities of the prepared nanoparticles in vitro using agar-based phantoms. The targeting performance and cytotoxicity of the nanoparticles were examined in cell culture using SKBR3 (over-expressing HER2) and MDA-MB-231 (HER2 negative) cells. We then tested the magnetic resonance (MR)/ photoacoustic (PA)/ ultrasound (US)/ near-infrared fluorescence (NIRF) multimodal imaging properties and photothermal effect of the nanoparticles in vivo using a SKBR3 breast xenograft model in nude mice. Tissue histopathology and immunofluorescence were also conducted. Results: Both in vitro and in vivo systematical studies validated that the hybrid nanoparticles can be used as a superb MR/US/PA/NIRF contrast agent to simultaneously diagnose and guide tumor photothermal therapy (PTT). When irradiated by a near infrared laser, the liquid PFP vaporizes to a gas, rapidly expelling the contents and damaging surrounding tissues. The resulting micro-sized bubbles provide treatment validation through ultrasound imaging. Localization of DIR and SPIO in the tumor region facilitate photothermal therapy for targeted tumor destruction. The mice treated with HER2 targeted nanoparticles had a nearly complete response to treatment, while the controls showed continued tumor growth. Conclusion: This novel theranostic agent may provide better diagnostic imaging and therapeutic potential than current methods for treating HER2-positive breast cancer.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".