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Record W4413932045 · doi:10.1038/s44182-025-00044-1

Sperm cell empowerment: X-ray-guided magnetic fields for enhanced actuation and localization of cytocompatible biohybrid microrobots

2025· article· en· W4413932045 on OpenAlexafffund
Veronika Magdanz, Leendert-Jan W. Ligtenberg, Yusra Pervez, Mathilda LaBrash-White, Motahareh Shabani Dargah, Iris Mulder, Sadaf Mohsenkani, Maud Gorbet, Negin Bouzari, Hamed Shahsavan, Remco H. Liefers, Michiel C. Warlé, Islam S. M. Khalil

Bibliographic record

Venuenpj Robotics · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMicro and Nano Robotics
Canadian institutionsUniversity of Waterloo
FundersUniversity of TwenteNatural Sciences and Engineering Research Council of CanadaNederlandse Organisatie voor Wetenschappelijk OnderzoekHealth~Holland
KeywordsEmpowermentNanotechnologyMaterials sciencePolitical science

Abstract

fetched live from OpenAlex

Magnetic microrobots have the potential to revolutionize medicine by navigating pathways to deliver precision-targeted therapy. However, a significant challenge arises. There commonly is a trade-off between magnetic responsiveness, detectability using medical imaging systems and cytotoxicity from increased amounts of magnetic content. Addressing this, we study biohybrid microrobots comprising clusters of iron oxide nanoparticle-coated sperm cells. These sperm-templated microrobots offer benefits over microrobots driven by live sperm, such as longer shelf-life and operation time, full directional and speed control and easy fabrication. To demonstrate their potential for use in clinical settings, we developed an X-ray-guided robotic platform investigating the magnetic response and detectability of these biohybrid clusters across varying nanoparticle concentrations, notably demonstrating simultaneous actuation and localization of sperm for the first time. These improvements advance the research closer to unleashing the potential of biohybrid microrobots for medical applications within the reproductive tract.

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: none
Teacher disagreement score0.912
Threshold uncertainty score0.674

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.009
GPT teacher head0.256
Teacher spread0.247 · 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

Citations3
Published2025
Admission routes2
Has abstractyes

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