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Record W4318309349 · doi:10.21203/rs.3.rs-2506896/v1

Site-Selective Superassembly of Biomimetic Nanorobots Enabling DeepPenetration into Tumor with Stiff Stroma

2023· preprint· en· W4318309349 on OpenAlexaff
Miao Yan, Qing Chen, Tianyi Liu, Xiaofeng Li, Peng Pei, Lei Zhou, Shan Zhou, Runhao Zhang, Kang Liang, Jian Dong, Xunbin Wei, Jinqiang Wang, Osamu Terasaki, Pu Chen, Zhen Gu, Libo Jiang, Biao Kong

Bibliographic record

VenueResearch Square · 2023
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicMicro and Nano Robotics
Canadian institutionsUniversity of Waterloo
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of ChinaShanghai Municipal Education CommissionShanghai University of Medicine and Health SciencesShanghai Key Laboratory of Molecular ImagingFudan University
KeywordsNanoroboticsStromaPenetration (warfare)NanotechnologyMaterials scienceEngineeringMedicinePathology

Abstract

fetched live from OpenAlex

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 mice model of TNBC and also demonstrate potent antitumor efficacy in three different subcutaneous tumor models.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.344
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.049
GPT teacher head0.359
Teacher spread0.311 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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