Site-selective superassembly of biomimetic nanorobots enabling deep penetration into tumor with stiff stroma
Bibliographic record
Abstract
Chemotherapy remains as the first-choice treatment option for triple-negative breast cancer (TNBC). However, the limited tumor penetration and low cellular internalization efficiency of current nanocarrier-based systems impede the access of anticancer drugs to TNBC with dense stroma and thereby greatly restricts clinical therapeutic efficacy, especially for TNBC bone metastasis. In this work, biomimetic head/hollow tail nanorobots were designed through a site-selective superassembly strategy. We show that nanorobots enable efficient remodeling of the dense tumor stromal microenvironments (TSM) for deep tumor penetration. Furthermore, the self-movement ability and spiky head markedly promote interfacial cellular uptake efficacy, transvascular extravasation, and intratumoral penetration. These nanorobots, which integrate deep tumor penetration, active cellular internalization, near-infrared (NIR) light-responsive release, and photothermal therapy capacities into a single nanodevice efficiently suppress tumor growth in a bone metastasis female mouse model of TNBC and also demonstrate potent antitumor efficacy in three different subcutaneous tumor models.
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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".