Targeted Micellar Phthalocyanine for Lymph Node Metastasis Homing and Photothermal Therapy in an Orthotopic Colorectal Tumor Model
Bibliographic record
Abstract
Tumor lymph node (LN) metastasis seriously affects the treatment prognosis. Studies have shown that nanoparticles with size of sub-50 nm can directly penetrate into LN metastases after intravenous administration. Here, we speculate through introducing targeting capacity, the nanoparticle accumulation in LN metastases would be further enhanced for improved local treatment such as photothermal therapy. Trastuzumab-targeted micelles (< 50 nm) were formulated using a unique surfactant-stripping approach that yielded concentrated phthalocyanines with strong near-infrared absorption. Targeted micellar phthalocyanine (T-MP) was an effective photothermal transducer and ablated HT-29 cells in vitro. A HER2-expressing colorectal cancer cell line (HT-29) was used to establish an orthotopic mouse model that developed metastatic disease in mesenteric sentinel LN. T-MP accumulated more in the LN metastases compared to the micelles conjugated with control IgG. Following surgical resection of the primary tumor, minimally invasive photothermal treatment of the metastatic LN with T-MP, but not the control micelles, extended mouse survival. Our findings demonstrate for the first time that targeted small-sized nanoparticles have potential to enable superior paradigms for dealing with LN metastases.
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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 | 0.001 |
| 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".