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Record W4321600996 · doi:10.1002/anie.202301202

Molecularly Imprinted Nanobeacons Redirect Innate Immune Killing towards Triple Negative Breast Cancer

2023· article· en· W4321600996 on OpenAlexaff
Peixin Guan, Zhanchen Guo, Shuxin Xu, LU Hai-feng, Lisheng Wang, Zikuan Gu, Zhen Liu

Bibliographic record

VenueAngewandte Chemie International Edition · 2023
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmunotherapy and Immune Responses
Canadian institutionsUniversity of Ottawa
FundersNanjing UniversityNational Natural Science Foundation of China
KeywordsTriple-negative breast cancerInnate immune systemHaptenAntibodyImmunologyCancer researchCancerImmunityImmune systemBiologyBreast cancer

Abstract

fetched live from OpenAlex

Harnessing innate immunity is an appealing strategy for cancer treatment. Herein, we report a new strategy called molecularly imprinted nanobeacons (MINBs) for redirecting innate immune killing towards triple-negative breast cancer (TNBC). The MINBs were molecularly imprinted nanoparticles with the N-epitope of glycoprotein nonmetastatic B (GPNMB) as the template and grafted with plentiful fluorescein moieties as the hapten. The MINBs could tag the TNBC cells via binding with GPNMB and thereby provide navigation for recruiting hapten-specific antibodies. The gathered antibodies could further trigger effective Fc-domain-mediated immune killing towards the tagged cancer cells. In vivo experiments showed that the TNBC growth was significantly inhibited after MINBs treatment by intravenous injection as compared with control groups. This study not only opens a new access for redirecting innate immunity towards TNBC but also paves the way for innate immunity-based therapy of other diseases.

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 categoriesInsufficient payload (model declined to judge)
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 score0.068
Threshold uncertainty score0.999

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)0.0020.000

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 head0.014
GPT teacher head0.271
Teacher spread0.257 · 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

Citations38
Published2023
Admission routes1
Has abstractyes

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