Effective treatment of metastatic sentinel lymph nodes by dual-targeting melittin nanoparticles
Bibliographic record
Abstract
Sentinel lymph node (SLN) metastasis is an important promoter of distant metastasis in breast cancer. Therefore, the timely diagnosis and precise treatment are crucial for patient staging and prognosis. However, the simultaneous diagnosis of metastasis and the implementation of imaging-guided SLN therapy is challenging. Here, we report a melittin-loaded and hyaluronic acid (HA)-conjugated high-density lipoprotein (HDL) mimic phospholipid scaffold nanoparticle (MLT-HA-HPPS), which dually-target to both breast cancer and its SLN and efficiently inhibit SLN metastasis in the LN metastasis model. The melittin peptide was successfully loaded onto HA-HPPS via electrostatic interactions, and MLT-HA-HPPS possesses effective cytotoxicity for breast cancer 4T1 cells. Moreover, the effective delivery of MLT-HA-HPPS from the primary tumor into SLN is monitored by NIR fluorescence imaging, which greatly benefits the prognosis and treatment of metastatic SLNs. After paracancerous administration, MLT-HA-HPPS can efficiently inhibit primary tumor growth with an inhibition rate of 81.3% and 76.5% relative to the PBS-treated control group and HA-HPPS group, respectively. More importantly, MLT-HA-HPPS can effectively inhibit the growth of the metastatic SLNs with an approximately 78.0%, 79.1%, and 64.2% decrease in SLNs weight than those in PBS, HA-HPPS, and melittin-treated mice, respectively. Taken together, the MLT-HA-HPPS may provide an encouraging theranostic of SLN drug delivery strategy to inhibit primary tumor progression and prevent SLN metastasis of 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".