MétaCan
Menu
Back to cohort
Record W4416965525 · doi:10.1126/sciadv.adw9133

Ultrasoft hydrogel immune millirobot with multimodal locomotion

2025· article· en· W4416965525 on OpenAlexaff
Zhiqiang Zheng, Sinan Özgün Demir, Anping Wu, Shihao Zhong, Zhengyuan Xin, Chunxu Yuan, Huaping Wang, Metin Sitti, Yu Sun, Lixin Dong

Bibliographic record

VenueScience Advances · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMicro and Nano Robotics
Canadian institutionsUniversity of Toronto
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of ChinaCity University of Hong Kong
KeywordsImmune systemBiocompatibilityCellImmunotherapyCancer immunotherapyTumor cellsCell type

Abstract

fetched live from OpenAlex

Advancements in cellular immunotherapy demanded efficient immune cell delivery. To meet this need, we introduced hydrogel-based immune millirobots designed for high immune cell loading and precise tumor targeting. These ultrasoft robots, embedded with magnetic nanoparticles, exhibited adaptable locomotion: walking, rolling, climbing, and undulating, enabling navigation through complex biological environments and alignment with varied tumor morphologies. They responded to magnetic fields and ionic or pH changes, facilitating propulsion, grasping, and localized delivery. In vitro, the millirobots eradicated three-dimensional tumor models in four days; in vivo, they notably reduced tumor growth in HepG2-luc tumor-bearing nude mice within 15 days. Bioluminescence imaging confirmed enhanced natural killer cell activity at tumor sites. The robots demonstrated excellent biocompatibility and biodegradability and caused no adverse effects postimplantation. This work showcased a responsive, soft robotic system with potential for advancing immune cell delivery and exploring tumor-immune dynamics in cancer therapy.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.320
Threshold uncertainty score0.312

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.004
GPT teacher head0.248
Teacher spread0.244 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations2
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueScience AdvancesSame topicMicro and Nano RoboticsFrench-language works237,207