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Record W2604318568 · doi:10.1021/acsmacrolett.7b00042

CO-Signaling Molecule-Responsive Nanoparticles Formed from Palladium-Containing Block Copolymers

2017· article· en· W2604318568 on OpenAlexaff
Miaomiao Xu, Lianxiao Liu, Jun Hu, Yue Zhao, Qiang Yan

Bibliographic record

VenueACS Macro Letters · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHeme Oxygenase-1 and Carbon Monoxide
Canadian institutionsUniversité de Sherbrooke
FundersRecruitment Program of Global ExpertsNational Natural Science Foundation of China
KeywordsOrganopalladiumMicelleNanocarriersPalladiumMoleculeCopolymerMaterials sciencePolymerSmall moleculeCell signalingNanomedicineBiophysicsNanoparticleCombinatorial chemistryNanotechnologyChemistrySignal transductionCatalysisOrganic chemistryBiochemistry

Abstract

fetched live from OpenAlex

The overproduction of cell-signaling molecules causes various human diseases. This intrinsic feature offers a biochemical basis to design biosignal-responsive nanocarriers for cell-selective therapy. Here we develop a new palladium-containing block copolymer, which can chemoselectively respond to carbon monoxide (CO)-a crucial gaseous signaling molecule in cells-while inhibiting disturbances from other endogenous analogues. Palladium is introduced into polymer for the first time, and such an organopalladium-connected chain can be cleaved by a CO-induced cascade insertion-elimination reaction, triggering a desirable disassembly of their self-assembling micelles. The micellar dissociation rate depends on the dose of CO stimulus. We envisage that this polymer model would enrich the repertoire of metallopolymers and provide a new platform for designing signaling molecule-responsive macromolecular systems.

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 categoriesMeta-epidemiology (narrow)
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 score0.005
Threshold uncertainty score1.000

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 science0.0010.000
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 head0.012
GPT teacher head0.260
Teacher spread0.248 · 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

Citations19
Published2017
Admission routes1
Has abstractyes

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